The LHC is a 27-kilometre ring buried beneath the border between France and Switzerland. Inside it, superconducting magnets guide beams of protons travelling in opposite directions at nearly the speed of light. At several points around the ring, the beams cross, and the protons collide.

These collisions briefly recreate conditions similar to those that existed just after the Big Bang. The particles produced in these collisions are recorded by enormous detectors such as ATLAS and CMS. By studying that debris, physicists can learn more about the fundamental particles and forces that make up our universe.

The LHC’s most famous achievement came in 2012, when the ATLAS and CMS experiments announced the discovery of the Higgs boson. Since then, the LHC has discovered dozens of previously unknown composite particles, tested the Standard Model with extraordinary precision, studied antimatter, and recreated the quark–gluon plasma that filled the early universe.

But the LHC has also taught physicists a difficult lesson: discovering the next layer of nature may require more than simply increasing the energy of collisions. We may need many more collisions, too. And that is exactly what comes next.

At six in the morning on June 29, 2026, operators at CERN removed the final beams from the Large Hadron Collider. After nearly two decades of operation, the world’s largest and most powerful particle accelerator went quiet.

This was not an emergency. Nothing had broken. The shutdown had been planned for years.

The Large Hadron Collider, or LHC, is entering a four-year period known as Long Shutdown 3. During this time, thousands of scientists, engineers and technicians will carry out the most extensive transformation of the machine since it was originally built.

When it returns around the middle of 2030, it will no longer be quite the same collider. It will have become the High-Luminosity Large Hadron Collider, or HL-LHC.

But how do you make a collider more powerful?

Once you have built a particle collider, there are two main ways to increase its power.

The first is to increase the energy of the particles. Higher-energy collisions can create heavier particles (thanks to Einstein’s famous E=mc squared). This allows scientists to explore processes that were previously out of reach. The LHC has done this repeatedly. Its collision energy increased from 7 and 8 trillion electronvolts during its first run, to 13 trillion electronvolts during its second, and then to a record 13.6 trillion electronvolts during its third.

The second way is to increase the number of collisions. You can pack more protons into the beams, and squeeze the beams more tightly where they cross. The more protons that pass through the same small region, the greater the chance that they will actually collide.

The LHC upgrade is mainly focused on the second approach because the collider has already reached close to its intended collision energy. It will not dramatically increase the energy of each collision.

Instead, it will pack more protons into the beams and focus them more tightly at the collision points. In other words, scientists are not making each collision much more powerful; they are creating many more opportunities for something rare and interesting to happen.

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How Do You Create a High-Luminosity Collider?

To achieve high luminosity, you need:

• Stronger focusing magnets: squeeze the proton beams into a smaller area at the collision point.

• Higher beam intensity: more protons in each bunch.

• Crab cavities: tilt the proton bunches so they overlap more effectively despite crossing at an angle.

• Better control of the beams: maintain the collision rate for longer during each physics run.

Scientists cannot simply add more protons to the LHC and switch it back on. Increasing the number of collisions requires major upgrades to the technology that controls, focuses, and protects the proton beams.

The first challenge is to make the beams more intense. The LHC sends protons around the ring in tightly packed bunches. By placing more protons in each bunch and circulating more bunches through the accelerator, scientists can increase the number of particles arriving at the collision points. But packing more protons together also makes the beam harder to control.

But the protons also push against one another, making it harder to keep the beam tightly focused. So the challenge is to pack in more protons while still keeping the beam extremely narrow.

To solve this problem, the High-Luminosity LHC will use new, more powerful superconducting magnets near the ATLAS and CMS experiments. Their job is not only to squeeze the proton beams into a smaller area, but also to keep the increasingly intense bunches tightly focused and stable as they race around the accelerator.

But there is another complication. The two beams don't meet perfectly head-on. This is intentional: with many tightly packed proton bunches circulating around the LHC, a small crossing angle helps prevent neighboring bunches from interacting with each other before or after the main collision point. But the angle also means that the two bunches do not overlap as much as they would in a head-on collision.

To solve this, the HL-LHC will use crab cavities to tilt the proton bunches just before they collide, increasing their overlap.

Achieving high luminosity is therefore not as simple as turning up the power. It requires stronger magnets, more precise beam control, and entirely new technologies capable of handling beams that are denser and more intense than before.

The Detectors Must Keep Up

Producing more collisions is only half the challenge. Scientists also need detectors capable of handling them.

During Run 3 of the LHC, around 50–60 proton–proton collisions can occur each time two proton bunches cross. At the High-Luminosity LHC, this number is expected to increase to around 140–200 collisions per bunch crossing. The particles produced in these simultaneous collisions pass through the detectors at nearly the same time, creating a dense and complicated web of overlapping tracks. Physicists call this pileup. The existing ATLAS and CMS detectors therefore need significant upgrades to operate effectively under these much more challenging conditions.

One of the most important upgrades is therefore the replacement of the inner tracking detectors, the instruments closest to the collision point that reconstruct the paths of charged particles. The new trackers will contain smaller and more numerous silicon sensors, allowing them to distinguish particles travelling extremely close together. They will also be designed to withstand much higher radiation levels.

ATLAS and CMS will also add precision timing detectors. By measuring not only where a particle travelled but also when it passed through the detector, these systems can help determine which of the many overlapping collisions produced it. Even differences of only a few tens of trillionths of a second can help separate one collision from another.

At Run 3, for CMS, the average data per collision is about 1 MB. The LHC proton beams cross each other 40 million times each second at CMS. That is a whopping 40 Terabytes of data each second!

It would be impossible to store all of this information. Instead, the trigger system acts as a filter, rapidly examining the collisions and keeping only a small fraction of them. At the HL-LHC, CMS expects to reduce the data from up to 750,000 events per second entering the trigger system to about 7,500 events per second that are ultimately stored. They would need 3.3 petabytes of storage per day.

The detector electronics must read information faster, while upgraded trigger systems must decide almost instantly which events might be scientifically interesting. ATLAS and CMS are therefore replacing large parts of their electronics and upgrading their trigger and data-acquisition systems.

Radiation presents another major challenge. The detectors closest to the beam will receive a much larger radiation dose over the lifetime of the HL-LHC. Sensors and electronics that work well today could gradually become damaged or unreliable. Many components must therefore be replaced with radiation-resistant technology capable of surviving the harsher environment.

In other words, increasing the luminosity does not merely require a more powerful accelerator. It requires faster electronics, more precise sensors, improved timing, and materials capable of surviving intense radiation. The LHC is being upgraded to produce more collisions, while its detectors are being rebuilt to make sense of them.

The increased luminosity of the HL-LHC is valuable because it gives physicists far more collisions to work with, making it possible to study both extremely rare processes and subtle effects that would otherwise be hidden in the data.

The five categories can be simplified as:

1. Higgs physics — measure the Higgs boson much more precisely, including its couplings and self-interaction.

2. Beyond the Standard Model — search for rare deviations or new particles/phenomena that the Standard Model cannot explain.

3. Flavor physics — study heavy quarks, especially the top quark, and test the CKM framework.

4. Precision Standard Model measurements — improve measurements of processes, PDFs, electroweak interactions, etc.

5. QCD and nuclear matter — study strongly interacting matter under extreme conditions, particularly in heavy-ion collisions.

The specific thing luminosity buys us is a better chance of seeing di-Higgs production: producing two Higgs bosons in the same proton–proton collision. This process is extremely rare, roughly a thousand times less common than producing a single Higgs boson. And it is especially interesting because the rate of di-Higgs production depends, in part, on how strongly the Higgs boson interacts with itself. That self-interaction is tied to the shape of the Higgs potential, which describes how the Higgs field behaves throughout the universe. Physicists will also use the HL-LHC to search for many other rare processes and possible signs of new physics.

I know physics might sound like jargon to you right now, but I will explain what all this means in a separate article because this one is already getting long!

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If you’ve been following tech news recently, you might have caught a series of headlines that read like near future thriller plots: autonomous AI models from OpenAI, Anthropic, and Meta broke out of their isolated test environments and interacted with real world target systems on the live web.

In response, the tech industry defaulted to a familiar word: misconfiguration.

Calling these events "misconfigurations" makes them sound like victimless administrative blunders, a misplaced rule or a setting someone forgot to toggle. But calling it a simple mistake misses the bigger picture. It ignores how goal-seeking AI functions, why holding these agents inside digital boundaries is so hard, and why Washington's policy response falls short.

What Is an AI “Sandbox”?

To understand what went wrong, it helps to understand how tech companies test AI.

When a lab wants to know how good a model is at finding software vulnerabilities, it can't just ask. It has to let the model try.

So it builds a sandbox: a walled-off testing environment with fake targets, mock networks, and no route to the live internet. The model attacks the mock targets, the lab measures how far it gets, and nothing outside the walls is at risk.

When an AI model experiences a "sandbox escape," it means the agent bypassed those safety parameters and established contact with live servers.

What Actually Happened?

During recent safety tests, researchers gave these AI models a standard assignment: Find vulnerabilities and bypass security controls.

When an AI agent is given a goal, it doesn’t think like a human. It simply probes every digital door and window it can find to complete its task.

• OpenAI’s agent found a path out and probed live systems on Hugging Face (a popular AI platform).

• Anthropic’s Claude escaped its test setting and accessed internal systems at three separate companies.

• Meta’s Muse Spark 1.1 slipped through its testing environment and made unauthorized changes to a live company’s systems.

The Word "Misconfiguration" Is Doing a Lot of Work

It’s easy to treat these incidents as separate stories, but they all boil down to the same underlying reality: When an autonomous AI is given a goal, it will test every boundary until it finds a way out.

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The industry tried to dismiss Anthropic and Meta’s escapes as “simple misconfigurations,” meaning a digital door to the open internet was accidentally left unlocked. But what if that open door hadn’t been there?

OpenAI’s case gives us the answer.

OpenAI’s model wasn’t given an open door. It was placed in a locked room with zero internet access, allowed only to talk to a single internal software helper. When the AI realized it needed outside information to pass its test, it didn’t give up. It systematically inspected that software helper, found a flaw in its security code, and used that flaw to break through the digital wall.

That is not even the scary part.

The AI didn’t just hack its way out. It learned to hide its tracks. It created sub-agents to divide up the work and left secret messages in digital corners to coordinate behind the scenes, all while keeping its activities quiet enough to avoid triggering security alerts.

The unsettling reality isn’t just that the AI was smart enough to do this. It’s the realization of what happens next.

Right now, human defenders were able to trace those hidden messages and figure out what happened after the fact. But AI models are getting exponentially smarter every few months. What happens when an agent becomes sophisticated enough to write secret messages that no human monitor, logging tool, or security system can ever trace?

When an AI can plan, split tasks, hide communications, and cover its tracks better than humans can audit it, “containment” isn’t just difficult; it becomes practically impossible.

But framing it as misconfiguration makes it sound like a minor, accidental glitch. In reality:

1. The AI models didn’t break or glitch. They did exactly what they were programmed to do: solve a problem using every tool and path available to them.

2. Containment is fundamentally hard. Humans assume boundaries exist because we understand rules. AI agents only understand physical and digital barriers. If an open wire exists, the AI will use it. If not, they will find a way to get out.

3. The stakes are changing. As AI models become faster, smarter, and better at solving complex tasks autonomously, relying on standard software settings to hold them in place is proving dangerously fragile.

The Takeaway

But these incidents expose a far more immediate reality: our ability to create smart, goal-seeking AI is moving faster than our ability to reliably contain or understand it.

Because modern AI models operate as “black boxes,” even their creators cannot fully predict how they will solve a given task. We give them a goal, and they optimize for it ruthlessly. They don’t have human ethics, common sense, or a concept of “rules”_, they only see pathways.

When an autonomous system can pick digital locks, spawn hidden sub-agents, and adapt its strategy in real time, the problem isn’t that the AI has malicious intent. The problem is that we are handing increasingly powerful tools to systems whose internal reasoning we cannot trace and assuming our traditional digital boundaries will be enough to hold them.

The Political and Regulatory Alarm Bells

This isn’t just an abstract concern for computer scientists anymore; governments are panicking behind closed doors.

The recent breaches have stirred deep concerns among U.S. lawmakers about whether increasingly capable AI models could be used to conduct or facilitate cyberattacks at scale. A group of Republican state attorneys general has already formally demanded that OpenAI preserve all potentially relevant documents related to its Hugging Face breach, while OpenAI promised a full technical report.

Meanwhile, the White House recently invited leading tech giants, including Meta, Anthropic, OpenAI, and Google, to discuss a newly finalized voluntary cybersecurity testing framework for advanced AI models.

Yet, in a paradoxical move, the White House told developers that open-weight AI models (like Meta’s Llama or Nvidia’s Nemotron) will be exempted from this voluntary safety testing regime altogether.

To understand why this exemption is so bizarre, let me explain what closed models and open-weight models are:

Closed models keep everything behind counter doors. You interact with ChatGPT or Claude on the company’s terms, and if you step out of line, they pull the plug.

Open-weight models hand over the key to the kitchen. Once you download Llama to your hard drive, you own the full system, can strip off its safety guardrails, and run it offline without asking anyone for permission.

We are left in a bizarre situation where Washington is attempting to address failures in closed-model containment while simultaneously giving open-weight systems a free pass.

We don’t all need to be computer scientists or programmers. But understanding that these black-box systems are moving into our digital world and that regulatory guardrails are struggling to keep up is why none of us can afford to ignore AI any longer.

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Last month marked the 70th anniversary since neutrinos were first detected. Neutrinos are weird particles, and I am going to write a lot of articles about them, but in this article I am going to focus on the history of how they were detected because it is very interesting.

The Problem with Beta Decay

The story starts in the late 1920s with a problem with a reaction called beta decay. An atomic nucleus is made of protons and neutrons, and sometimes that mix is unstable. Every nucleus requires the right amount of mixture of neutrons and protons.

When an atom has more neutrons than needed, it tries to fix the imbalance with beta decay. In the most common form (beta-minus decay), one neutron inside the nucleus transforms into a proton, and in the process it ejects an electron. That ejected electron is the "beta particle."

The trouble is that this looks like a two-body decay: a neutron turning into just two particles. Two-body decays are rigid, conservation of energy and momentum leaves no freedom, so the outgoing particle's energy is fixed. Every electron should come out carrying the same amount of energy.

But when scientists actually measured the energy of the electrons coming out of beta decay, they found it wasn't fixed at all. Some electrons came out with high energy, some with very low energy. What they got was a whole distribution, a spread. And that was not what they were expecting.

There were two things that could be going wrong.

1. Either energy conservation doesn't hold at the subatomic level.

2. Maybe it wasn't a two-body decay at all, and a third particle was coming out with the electron, carrying off the missing energy.

Both answers had serious backers. Niels Bohr, one of the greatest physicists, was willing to drop energy conservation. He thought it might hold only on average across many decays, rather than in every single one. He'd tried the same move before in the 1920s for the interaction of light and matter.

Another physicist, Pauli, made another proposal. Pauli made his proposal on 4 December 1930, in a letter to a physics meeting in Tübingen that he wasn't attending.

It opens "Dear radioactive ladies and gentlemen". He sent it by courier, with a colleague who could explain the idea in person.

He called it a desperate remedy himself. His suggestion was that nuclei contain electrically neutral particles with spin 1/2, light enough to be somewhere near the electron in mass and certainly no heavier than a hundredth of a proton. He called this particle a neutron.

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Then in 1932 Chadwick found the real neutron. It was heavy and neutral, and it solved the nuclear structure problem on its own, but it was far too massive to be what was slipping out of beta decay. Pauli’s version needed a different name, and Fermi gave it one: neutrino, the little neutral one.

So how does it fix the spectrum? If a neutrino comes out alongside the electron, and the two energies always add up to the same total, the paradox dissolves. The sum stays fixed. The split doesn’t. Sometimes the electron takes most of it, sometimes the neutrino does, and what you measure across many decays is a spread.

The rest of the letter is him hedging. He isn't sure what forces act on the thing. He guesses it might have a magnetic moment, and reasons that it can't ionize matter any more than a gamma ray does, which is his first stab at how weakly it interacts.

He says outright that he doesn't feel confident enough to publish. He admits the odds are against him, since a particle like that probably would have turned up by now. He mentions that his predecessor Debye told him not to think about it at all, the way you don't think about new taxes. His answer to that is that only people who bet can win. Then he apologizes for not coming to Tübingen, because he's needed at a ball in Zurich the same night.

Fermi took Pauli's idea seriously and built a theory around it.

There was a stretch where physicists thought the particle couldn't be detected at all. Bethe and Peierls put it in print in 1934: there was no practically possible way of observing the neutrino. They had calculated the odds of a neutrino hitting anything, and the answer was that it could pass through a thousand light years of solid matter before interacting once.

The verdict held about twelve years. Then Bruno Pontecorvo, one of Fermi’s students from Rome, wrote a report saying it was too strong. By 1946 he was in Canada at Chalk River, standing next to a reactor, exactly the kind of neutrino source that hadn’t existed when Bethe and Peierls ran their numbers.

He didn’t argue with the physics. He agreed the yield was tiny. What he disputed was the conclusion. The literature said the process couldn’t be observed, and Pontecorvo called that too drastic. Observation was not out of the question with modern experimental facilities, and he had a method in mind.

The method used chlorine. A neutrino hits a chlorine-37 nucleus and turns it into argon-37, which is radioactive with a half-life of about 35 days. That’s the clever part. You don’t have to catch the interaction as it happens.

Let a big tank of chlorine-bearing liquid sit for weeks while argon builds up inside it, then flush it out, concentrate it, and count the decays at your leisure. You’re not detecting a neutrino anymore; you’re detecting a few atoms of gas in a tank, and chemists were already good at that. He named the two sources worth aiming it at: the Sun, and nuclear reactors.

Nobody built it. The report was classified, so it sat unread for years.

Meanwhile in the United States, two physicists at Los Alamos were going after the neutrino with a completely different plan.

The bomb, and then the reactor

Frederick Reines and Clyde Cowan started from the same basic reasoning: if a single neutrino is nearly impossible to catch, flood the area with an unimaginable number of them and wait for the rare one to interact.

Their first idea, in 1951, was to put a detector next to an atomic bomb test. A fission explosion releases a huge burst of neutrinos at once, and they sketched out a detector, nicknamed El Monstro, to be dropped down a shaft near a 20-kiloton test tower and take two seconds of data before the blast arrived. Getting a bomb assigned to a physics experiment turned out not to be the hard part. The plan was approved.

It was never built. In late 1952, they were urged to reconsider by J. M. B. Kellogg, head of the Los Alamos physics division, who suggested a nuclear reactor instead.

Reactors were still in the early stages of commercial development, so this was not the obvious suggestion it now sounds like. Reines wrote later that he had wondered since why it took them so long to reach what seemed such an obvious conclusion, and how it escaped everyone around them they’d talked to. Pontecorvo had written it down six years earlier, in a document they had no way to read.

Their first working detector, at the Hanford reactor site in Washington State in 1953, was a real piece of engineering: 300 litres of cadmium-doped liquid scintillator watched by banks of photomultiplier tubes, wired to catch a specific double signal.

They nicknamed it Herr Auge, Mr. Eye. They saw a small effect that seemed to depend on whether the reactor was running, and published it under the title “Detection of the Free Neutrino,” with the careful phrasing that it appeared probable the aim had been accomplished, further work in progress.

Nobody was convinced, including them. Cosmic rays had swamped the detector with false signals roughly ten times larger than what they were chasing.

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Learning what was fighting them

They treated the failure as a list of things to fix. Better shielding. Better electronics. A stronger reactor. A longer run. And above all, a proper understanding of what was faking their signal.

That last one turned into detective work. Cowan later described testing materials one by one to see which were quietly radioactive. Brass and aluminium were worse than iron and steel. Even the potassium in the glass in their own light detectors was giving off a faint signal of its own. Everything going into the experiment had to be checked first.

The new site was the Savannah River Plant in South Carolina, which had a far more powerful reactor. The detector they built there in 1956 was much bigger: large tanks of the flashing liquid, wrapped around thin tanks of water dosed with cadmium. The water was the target, the part the neutrinos would actually hit. They buried the whole thing twelve metres underg

round, behind eleven metres of concrete, and packed lead and wax around it to keep everything else out.

The trick was in the signature

The reaction they were watching for was an antineutrino hitting a proton in the water and knocking out two things: a positron, which is the electron’s antimatter twin, and a neutron. Those two leave very different trails.

The positron dies almost instantly, meeting an ordinary electron and annihilating in a burst of light. The neutron lasts longer. It drifts through the water for a few millionths of a second until a cadmium atom swallows it, and that produces a second burst of light in the same spot.

So the signature was a flash, then a pause, then another flash, in the same place. Nothing else in the detector does that. Stray radiation might produce one flash, but getting two in the right order, the right distance apart in time, and the same location, is almost impossible by accident. That pattern is what let them pull a real signal out of the noise.

The telegram

They ran for months, comparing the count rate with the reactor on against the reactor off to make sure they weren’t fooling themselves. Roughly 2.55 counts per minute with the reactor running, versus 2.14 with it shut down.

On 14 June 1956 they sent a telegram to Pauli in Zurich, telling him they had detected the particle he’d proposed twenty-six years earlier. Their results were published in Science on 20 July, and a fuller paper titled simply “The Neutrino” followed in Nature on 1 September, reporting a reactor-dependent signal of 2.88 ± 0.22 counts per hour.

The measured cross section came out around 6 × 10⁻⁴⁴ square centimetres, which is roughly what Bethe and Peierls had calculated in 1934. They’d had the physics right the whole time.

Pauli was reportedly delighted. The thing he’d worried might never be found had turned up after all. Reines received the Nobel Prize in 1995. Cowan had died in 1974, and the prize doesn’t go to the dead.

The timeline is worth seeing together:

1930: Pauli proposes the particle.
1932: Chadwick discovers the neutron.
1934: Fermi develops the theory of beta decay, and Bethe and Peierls calculate the tiny interaction probability.
1946: Pontecorvo proposes using reactors and radiochemical detection.
1951: Reines and Cowan consider using an atomic bomb.
1953: The Hanford experiment sees an uncertain signal.
1956: The Savannah River experiment detects the neutrino convincingly.

Twenty-six years from the idea to the detection. That is not unusual in particle physics. The work runs on patience, and many people give a whole career to a question and come away with nothing.

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This spectacle is possible because of an extraordinary astronomical coincidence: the Sun is about 400 times wider than the Moon, but it is also about 400 times farther away from Earth.

Because these two ratios are nearly the same, the Sun and the Moon appear almost the same size in our sky.

The Sun has a diameter of roughly 1.4 million kilometers, while the Moon is only about 3,500 kilometers wide. The Sun is therefore approximately 400 times larger than the Moon.

But the Sun is also much farther away. The Moon is, on average, about 384,000 kilometers from Earth, while the Sun is about 150 million kilometers away, again, a ratio close to 400.

The result is a remarkable visual coincidence: the Sun and the Moon appear almost exactly the same size in our sky. During a total solar eclipse, the Moon can therefore cover the Sun’s bright visible surface while revealing the ghostly white corona of its outer atmosphere.

No other rocky planet in our solar system experiences total solar eclipses quite like Earth. Mercury and Venus have no moons, while Mars’s two tiny moons, Phobos and Deimos, are far too small to cover the Sun completely. From the Martian surface, they produce only small, partial, or annular transits across the Sun.

Apparent size does not depend only on how large an object actually is. It also depends on how far away it is. A small object nearby can appear as large as a much bigger object far away. You can see this by holding your thumb in front of your face. Your thumb is obviously much smaller than a distant building, but from the right distance, it can completely cover the building from view.

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The same thing happens during a solar eclipse. The nearby Moon, though much smaller than the Sun, can cover the distant Sun almost perfectly.

The Coincidence Is Not Exact

The relationship is only approximate. Both Earth’s orbit around the Sun and the Moon’s orbit around Earth are slightly elliptical rather than perfectly circular. Their distances therefore change throughout the year.

Sometimes the Moon appears slightly larger than the Sun. When the alignment is perfect, it completely blocks the bright surface of the Sun, producing a total solar eclipse.

At other times, the Moon is farther from Earth and appears slightly smaller. It cannot completely cover the Sun, leaving a bright ring around its edge. This is called an annular eclipse, often described as a “ring of fire.”

This variation makes the coincidence even more interesting. The apparent sizes of the Moon and Sun are not permanently identical. They simply overlap closely enough that, under the right conditions, the Moon can cover the Sun with astonishing precision.

A Temporary Moment in Earth’s History

Total solar eclipses have not always looked the way they do today, and they will not continue forever.

The Moon is slowly moving away from Earth by a few centimeters each year. In the distant past, it appeared larger in the sky and would have covered the Sun more completely. Far in the future, it will appear too small to block the Sun entirely.

Eventually, total solar eclipses will no longer occur. Only annular eclipses will remain.

This means that humanity exists during a special period in the history of the Earth-Moon system, a period when the Moon is at almost exactly the right distance to cover the Sun while still revealing its delicate corona.

Coincidence or Something More?

Scientifically, there is no known physical law requiring the Moon and Sun to have nearly identical apparent sizes. The Moon did not form so that it could perfectly cover the Sun. Its current apparent size is the result of the sizes, distances, orbital motions, and long-term evolution of the Earth, Moon, and Sun.

It is therefore reasonable to call it a coincidence.

But “coincidence” does not mean unimportant. This accidental alignment has had enormous scientific value. Total solar eclipses allow astronomers to observe the Sun’s corona, study its magnetic activity, and investigate how light behaves near massive objects.

The famous 1919 eclipse expedition led by Arthur Eddington provided early evidence supporting Einstein’s general theory of relativity. During the eclipse, astronomers measured how the Sun’s gravity appeared to bend light from distant stars. Normally, that cannot be done because the Sun’s bright surface makes the daytime sky far too bright. The faint light from nearby stars is completely overwhelmed.

During a total solar eclipse, the Moon blocks the Sun’s bright visible disk. The surrounding sky becomes dark enough for stars near the Sun to appear in photographs. Astronomers could then:

1. Photograph the stars near the eclipsed Sun.

2. Photograph the same stars months later, when the Sun was elsewhere in the sky.

3. Compare their apparent positions.

If the stars appeared slightly displaced during the eclipse, that would indicate that their light had been bent while passing through the Sun’s gravitational field.

The Moon’s apparent size did not merely create a beautiful sight. It helped humanity test one of the deepest theories in physics.

A Cosmic Accident Worth Appreciating

A total solar eclipse is not simply the Sun being covered by the Moon. It is the result of an unlikely arrangement of scale and distance.

A Moon roughly 400 times smaller than the Sun sits roughly 400 times closer to Earth. For a brief moment, these enormous differences cancel each other out.

The result is darkness in daylight, a glowing solar corona, and one of the clearest reminders that beauty in the universe does not always require a deeper plan. Sometimes, the most breathtaking events arise from geometry, motion, time, and coincidence.

We are lucky to see such rare cosmic events. So if a total eclipse ever passes near you, go. The Moon is drifting away, and the fit has a shelf life. Somewhere in the far future there's a final one.

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It’s worth sitting with that for a second, because we’ve gotten so used to the idea of going back that the actual going back slipped by almost quietly. Reid Wiseman, Victor Glover, Christina Koch, and Canada’s Jeremy Hansen spent about ten days aboard NASA’s Orion capsule, looped around the far side, and splashed down in the Pacific on April 10. It was the first time humans had traveled to the Moon in more than half a century, since Apollo 17 packed up and left in December 1972.

So the natural next question is the obvious one: when do we land?

There’s a phrase that keeps turning up in the coverage, and it slides right past most readers: the astronauts will dock with commercial landers. It sounds like a throwaway detail. It’s actually the whole story of how America gets back to the Moon, and it’s the part worth slowing down on.

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NASA buys its biggest machines in two completely different ways.

The old way is the way you’d imagine. NASA decides exactly what it wants, writes the detailed specifications, and stays in the room for the engineering decisions; the contractor builds to NASA’s direction. NASA pays whatever it ends up costing, plus a guaranteed fee on top, and owns the finished vehicle. And if the bill comes in billions over, which it usually does, the taxpayer covers it. This is how Apollo was built, and it’s how the giant SLS rocket and the Orion capsule are built today.

The new way flips that on its head. Instead of buying a machine, NASA buys an outcome: "deliver our astronauts to the surface and bring them home." It picks a couple of companies, agrees on a fixed price, and pays out only as each one hits an agreed milestone. The company designs the vehicle, builds it, owns it, and can rent it out to other customers, too. If development runs over budget, that's the company's problem, not NASA's.

Here's the kicker: the rocket and the capsule that carry the crew to lunar orbit are built the old way, and they're exactly the parts famous for going over budget. But the lander, the thing that actually touches the Moon, is bought in a new way. NASA doesn't own a Moon lander. It's hired SpaceX and Blue Origin to bring their own.

And they really are their own. If NASA were drawing the plans, the two landers would look like cousins. Instead they look nothing alike: SpaceX's Starship is a fifty-meter stainless-steel tower that has to be refueled in orbit, while Blue Origin's Blue Moon is a squat lander barely a third its height. Each company made its own bets to answer the same question.

Why hand off the single most dangerous, never-been-done part of the trip? Because it's cheaper, because competition between two companies keeps everyone honest, and because NASA already proved the model works, it's been buying rides to the space station from private companies for years. The lander is just that idea aimed at the Moon.

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The catch, of course, is that you're now betting the whole mission on hardware that two private companies haven't finished yet. Which is exactly why the next Artemis flight isn't a landing at all, it's a rehearsal to find out whether those landers are real.

It’s not that NASA couldn’t build a Moon lander itself. It did exactly that during Apollo; the Lunar Module was a government-directed, contractor-built vehicle in the old cost-plus mold. So it’s technically possible. The problem is that it’s no longer affordable the old way, given two things that have changed since Apollo: the budget and the ambition.

The budget is the blunt part. At its peak in the mid-1960s, NASA consumed something like 4% of the entire federal budget, an extraordinary, war-footing level of spending that let it build everything bespoke and fast. Today, NASA runs on roughly half a percent of the federal budget. You cannot reproduce Apollo’s build-everything-in-house model on a fraction of Apollo’s share of national resources. Something has to give.

It’s worth knowing that this is all unfolding under real budget pressure. The Trump administration’s FY2026 request proposed the largest single-year cut in NASA’s history, slicing total funding by nearly a quarter and gutting the agency’s science programs by 47 percent, enough to kill more than 40 active and planned missions. Congress rejected it. In January 2026, lawmakers funded NASA at $24.4 billion and preserved science spending almost entirely, in a bipartisan rebuke.

But the administration came right back, reviving nearly identical cuts in its FY2027 request, so the fight isn’t over. It’s recurring. And the pattern underneath it is telling. The cuts target robotic science, meaning planetary probes, telescopes, and Earth observation, while the Moon program is the priority being funded.

Within a shrinking NASA, human exploration is winning, and space science is losing, which is exactly why leaning on privately built landers is so appealing. When money is this tight and getting tighter, the commercial model isn’t just convenient. It’s how an ambitious Moon and Mars program stays alive at all.

The plan everyone expected

For years, the script was simple. Artemis I (2022) was an uncrewed test flight. Artemis II would carry a crew around the Moon. And Artemis III would be the big one, boots on the lunar surface, the first woman and the next man, a triumphant sequel to Apollo.

Then, in late February, NASA tore up that third act.

Administrator Jared Isaacman stood up and announced that Artemis III would no longer attempt a landing at all. The mission that was supposed to be humanity’s return to the lunar surface had been quietly rewritten into something far more modest: a flight that never leaves the neighborhood of Earth.

If you only read the headline, it sounds like a retreat. It’s more complicated and more sensible than that.

NASA’s real problem was rhythm. The SLS rocket and Orion capsule work, but they fly rarely, historically once every three years or more. It’s hard to get good at something you do that infrequently. Each mission becomes a high-stakes event rather than a routine, and the gaps mean teams half-relearn the job every time.

That’s the context for Isaacman’s call. Going straight from a flyby on Artemis II to a full landing on the old Artemis III was, he said, too big a jump. The list of things that had never been done with a crew aboard was long: docking Orion to a lunar lander, transferring into that lander, and riding a brand-new commercial spacecraft down to the surface and back. Folding all of that into one mission, after a multi-year wait, was asking a lot.

What comes next

If Artemis III is the practice run, the landing itself moves to Artemis IV, now set for no earlier than 2028. That’s the mission that will do what Artemis III was originally meant to do: put astronauts back on the surface.

NASA wants more than one. The plan is two landing missions in 2028, Artemis IV and Artemis V, followed by roughly one mission a year after that. The reasoning behind the whole reshuffle is frequency. Launch more often, change less between flights, and turn a once-a-decade event into something closer to routine. NASA has talked about getting the gap between launches down to about ten months.

There’s logic to that. A program that flies regularly keeps its skills sharp. One that flies once every few years has to relearn the job each time.

The point isn’t to visit. It’s to stay.

The landings are not the real goal. They’re the start of something larger: a permanent base near the Moon’s south pole. A December 2025 executive order told NASA to land people on the Moon by 2028 and stand up the first pieces of a permanent lunar outpost by 2030, and in March 2026 NASA released its plan to do it.

The work has already started, before anyone has landed. NASA has begun ordering the hardware, awarding hundreds of millions of dollars to four companies for landers, rovers, and drones. Blue Origin will deliver moon buggies built by Astrolab and Lunar Outpost, and Firefly Aerospace will deliver the first drones, all of it meant to arrive near the south pole before the first crew lands in 2028. A second phase, from 2029 into the early 2030s, would build up the permanent infrastructure, including a power grid.

The south pole is the target for one reason: water ice, which has been collecting in its permanently shadowed craters for billions of years. Ice means drinking water, breathable oxygen, and rocket fuel that doesn’t have to be hauled up from Earth. But those same shadows are a problem. Stretches of darkness at the pole can last for months, which rules out solar power, so the base will need radioisotope sources and eventually a nuclear reactor to survive them. Much of that hardware, down to power cables rated for lunar radiation, doesn’t exist yet.

One quieter change came with the shift in focus. NASA intends to pause work on Gateway, the small station it had planned to keep in orbit around the Moon, and put the effort into the surface instead.

And the base isn’t only about science. Underlying the rush is concern in Congress and the administration about rivals. China and Russia are developing their own lunar outpost, and a summary of one Senate bill calls for a US base “so we can get there before the Chinese” and to “dominate the Moon, control strategic terrain in space, and write the rules of the 21st century.”

What NASA says less loudly

“Course correction” is the phrase you use when the original course wasn’t working. Artemis is nearly a decade old and has run years late and billions over budget. Artemis II slipped repeatedly, and engineers spent the run-up chasing technical problems, including a helium leak that NASA now plans to fix by replacing an entire upper stage of the rocket. Adding a mission does lower the risk of the eventual landing. It also buys time for hardware that wasn’t going to be ready for a 2027 landing anyway.

That hardware is the landers. Both still have a lot to prove. SpaceX’s Starship has to be refueled in orbit by a series of tanker launches, a maneuver no one has ever performed, and it has yet to reach orbit at all. Blue Origin’s New Glenn rocket, which will carry its lander, has only flown a handful of times. Testing them in Earth orbit first is, among other things, a way to find out whether they’re real before NASA commits a Moon landing to them.

What’s next isn’t really about the Moon

NASA’s stated logic has long been that the Moon is a stepping stone to Mars, a place to learn how to live off-world and to mine ice for fuel before the longer trip. The executive order that set the 2028 landing and the 2030 base also tells NASA to lay the groundwork for Mars.

The connection is sharper than the official language suggests, because the lander NASA chose is itself a Mars vehicle. Starship is the ship Elon Musk is building to reach Mars, and he has never pretended otherwise. He has called the Moon a distraction and Artemis a jobs program, and said he’d rather go straight to Mars. When NASA bought a commercial lander, it tied part of its Moon program to a contractor whose real ambition lies well beyond it.

This is where it gets genuinely contested. The administration’s budget proposed canceling SLS and Orion, the expensive government hardware, after just two crewed flights. Musk and the president have pushed Mars as the national priority. Isaacman tries to hold both, calling SLS outrageously expensive while insisting NASA doesn’t have to choose between the Moon and Mars. What keeps the Moon in first place is the race with China, which is aiming to land its own astronauts by 2030.

So the commercial bet and the Mars bet turn out to be the same bet, wearing different clothes. If the landers are private and reusable, NASA can eventually lean on them and shed the costly rocket, which is exactly what the Mars camp wants. The redesigned Artemis III, testing those landers in orbit, is the moment where the Moon program and the Mars ambition quietly touch.

What to watch

NASA named the Artemis III crew on Tuesday, June 9, 2026, from Johnson Space Center in Houston. Randy Bresnik will command the mission, with ESA's Luca Parmitano as pilot, and Andre Douglas and Frank Rubio as mission specialists. Bob Hines was named as the backup crew member.

For now, “what’s next” isn’t a boot print in the dust. It’s two spacecraft meeting in Earth orbit while a crew checks that the hatches line up, the suits hold, and the systems talk to each other. It’s a less dramatic answer than a landing. But it’s the step that decides whether the landing happens at all.

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That number is called the anomalous magnetic moment of the muon.

It sounds like a tiny technical detail, but it became one of the most important precision tests of the Standard Model. For years, this number seemed to disagree with theory, raising the possibility that the muon was quietly pointing toward new physics.

That is what made the measurement so important. The experiment was not just asking, “What is this number?” It was asking whether the Standard Model, our best theory of particle physics, could survive one of the most precise tests ever thrown at it.

To understand why this mattered, we need to answer a few questions: What is a muon? What does “anomalous magnetic moment” mean? How do physicists calculate it? And why did they win this prize?

What is a muon?

A muon is a heavier, unstable version of the electron. Same electric charge, same spin, about 207 times the mass. It lives for around 2.2 microseconds before decaying.

There are three particles like this in nature, called the charged leptons: the electron, the muon, and the tau. They are identical except in mass and lifetime. The electron is light and stable; the tau is heavy and decays almost immediately; the muon sits in between.

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It was discovered in cosmic rays in 1936, which was a surprise. Nobody had been looking for a second electron, and there was no theoretical reason to expect one. When the physicist I. I. Rabi heard about it, he reportedly said, “Who ordered that?” We still don’t really have an answer. The muon doesn’t build atoms or chemistry or any familiar matter. It’s just there, a copy of the electron we can’t explain.

What we can do is use it.

What is its anomalous magnetic moment?

The property they were studying is the spin of the muons.

Every muon has a property called spin. It's something the muon has the way it has electric charge: built in, always present, can't be turned off. Physicists call this kind of property intrinsic. Mass, charge, and spin are all intrinsic.

Spin, like many things in quantum mechanics, is quantized. It can only come in specific, discrete amounts. Particles either have an integer spin (0, 1, 2…) or a half-integer spin (½, 3/2…), and nothing in between. The muon, like the electron, is a spin-½ particle.

The direction of the spin matters too. When you place a muon in a magnetic field, its spin lines up with the field in one of two ways: pointing along it, or against it. Physicists call these up and down. There is no third option, no diagonal, no halfway. The magnitude of the spin is fixed, and the orientation is binary.

Spin has no classical analog. It is a purely quantum mechanical property, and nothing in everyday experience behaves quite like it. But physicists still find it useful to picture a muon as something like a tiny spinning top, because in some ways it behaves like one. It responds to magnetic fields the way a spinning charge would. The picture is helpful, but not a literal truth.

Because the muon has both electric charge and spin, it behaves like a tiny bar magnet. A spinning charge generates a magnetic field, and the muon, carrying charge and spin together, ends up with its own little north and south poles.

You can guess what happens next. Put a bar magnet in a magnetic field, and it doesn’t just ignore it. If the field is uniform, the magnet feels a torque and tries to turn so it lines up with the field, like a compass needle pointing north. A muon behaves in a similar way, except the “tiny magnet” is its spin. When a muon is placed in a magnetic field, its spin does not simply snap into place. Instead, it rotates around the field direction, like a wobbling top. In physics, this motion is called spin precession.

That wobble is the entire game. The rate at which the muon’s spin wobbles inside a magnetic field is what the experiment measures.

The rate of spin precession follows a simple formula. If the muon has charge q, mass m, and the magnetic field has strength B, then the spin precesses at a frequency: g · (qB) / (2m).

That g is the g-factor, the dimensionless number that captures how strongly the muon’s spin couples to magnetism. Larger g, faster wobble. Stronger B, faster wobble. Everything in the formula is something you can measure.

The Fermilab experiment uses a much cleverer trick. The muons are not just sitting in the field. They are circling a giant ring at nearly the speed of light, going around about 150 times every microsecond.

That circular motion has its own frequency, called the cyclotron frequency, and it also depends on the magnetic field:

Now look at what happens if you subtract the cyclotron frequency from the spin precession frequency. The two formulas share a qB/m factor, and when you do the subtraction, almost everything cancels:

The difference between how fast the spin wobbles and how fast the muon goes around the ring depends on (g − 2) / 2. That quantity has a name. It is the anomalous magnetic moment of the muon, the very number the experiment is trying to measure. It is also why the experiment is called the Muon g-2 experiment.

The g-factor is the key to everything that comes next, and its history is surprisingly strange.

Start with classical physics. Imagine the muon as a tiny ball of charge that is spinning. That picture gives you (g = 1). In other words, the muon would act like a small magnet, but nothing strange would happen. No surprise. No mystery.

Then came Dirac.

Then Dirac arrived. In 1928, he wrote down the relativistic quantum wave equation for the electron, and out of it fell something nobody had asked the equation to deliver: the spin of the electron, and the fact that g should equal exactly 2. Not 1. Twice the classical value.

The result was so unexpected, and so precisely confirmed by experiment, that it became one of the early triumphs of quantum mechanics. For a point-like spin-½ particle obeying Dirac's equation, g equals 2 on the nose.

It didn’t last long. Two decades later, with the development of quantum field theory, physicists realized that Dirac’s equation, beautiful as it was, treated the particle as if it lived alone in empty space. In quantum field theory, no particle lives alone. Around every electron and every muon, virtual particles are constantly flickering into and out of existence: photons, electron-positron pairs, eventually quarks and W bosons. The “real” particle is forever wading through this quantum foam, and the foam tugs on it.

These quantum fluctuations shift the g-factor by a little. And the remarkable thing is, we can calculate that shift. We can sit down and work out, to ten or eleven decimal places, what an unobservable cloud of virtual particles does to a real one. It is one of the more astonishing things physics can do.

How do you calculate it?

Quantum field theory gives you a way to calculate what the quantum foam does to the muon. The tool is called a Feynman diagram.

A Feynman diagram is a little picture of something the muon can do. Lines for particles, dots where they meet. The simplest picture is just a muon sitting in a magnetic field, and it gives you Dirac’s answer, g = 2. More interesting pictures show the muon briefly emitting a photon, or a particle pair flickering in and out, or heavier particles making a brief appearance.

Each picture is one thing that can happen, and each one tweaks the magnetism a little. The trick is that every picture corresponds to an equation. Feynman wrote down the rules for turning a diagram into math: convert each line and dot into a specific factor, do the arithmetic, and get a number.

So predicting the muon's anomalous magnetic moment works like this. Draw every picture you can think of. Convert each one to math. Add up all the numbers. That sum is your prediction.

That’s what physicists did, and “did” understates it. To get a prediction precise enough to compare against the experiment, they had to draw and compute more than 13,000 diagrams. The simpler ones are manageable. The harder ones involving integrals are nightmares and are done with custom-built code, by people who specialize in exactly that. Doing 13,000 of them is a multi-decade, multi-continent effort involving hundreds of physicists, careful cross-checks between independent groups, and occasional discoveries that someone, somewhere, made a sign error twenty years ago.

They did it anyway. They drew the diagrams, summed the contributions, and got a number.

What did the experiment find and why did it look, for a while, like the Standard Model was breaking?

Then the experiment measured the same quantity, and the two numbers didn’t match. (How the experiment actually works is a story for another post. This one is already long.)

What could be going wrong? Two possibilities.

Either the calculation was off somewhere, or the list of particles going into the calculation was incomplete.

That second possibility was the exciting one. The prediction includes every particle we know about. If something else was flickering in the quantum foam, something nature has, but we haven’t discovered yet, its fingerprint would show up exactly here, as a disagreement between prediction and measurement. For a few years, it really did look like the muon was pointing us toward a particle nobody had ever seen.

Let me explain how results are presented in particle physics.

To claim a discovery, physicists usually require something called 5 sigma significance. That means the chance that the result is just a random statistical fluctuation is extremely small, about 1 in 3.5 million.

In other words, before physicists say “we discovered something,” they want to be almost completely sure that the signal is not just noise pretending to be a discovery.

At Brookhaven National Laboratory, the muon seemed to disagree with the Standard Model by about 3.5 sigma.

That was not enough to claim a discovery. Particle physicists usually want 5 sigma for that. But it was too interesting to ignore.

So the experiment moved to Fermilab, another national lab in the United States, just outside Chicago.

And the move itself was almost unbelievable.

The giant magnetic storage ring from Brookhaven was too delicate to take apart, so they moved the whole thing. It traveled down the East Coast by barge, around Florida, up the Mississippi River, and then by truck to Fermilab.

They literally moved a 50-foot-wide ring across the country.

Then Fermilab took new data. And after the first result came out, the disagreement with the Standard Model became even stronger: about 4.2 sigma.

Still not enough for a discovery.

But definitely enough to make physicists pay attention.

Then Fermilab kept going.

More data came in. The measurement became sharper and sharper. And in 2025, Fermilab released the final result.

The experiment itself was rock solid: the muon really was wobbling at the value they measured.

But then the twist came.

The disagreement did not grow into a discovery. Instead, the theory changed.

The twist came from one specific part of the calculation.

Most of the muon (g-2) calculation can be done incredibly precisely. But one piece is much harder. It involves the strong force, where quarks and gluons interact in messy ways. This part is called the hadronic vacuum polarization contribution.

There are two main ways to calculate it.

One way is data-driven. You use real experimental data from particle collisions and feed that into the calculation.

The other way is lattice QCD. You simulate the strong force directly on a huge spacetime grid using supercomputers.

For a long time, the data-driven method was trusted more because it had smaller uncertainties. Lattice QCD was promising, and its central value was closer to the Fermilab measurement, but its error bars were larger. So the famous (4.2 sigma) disagreement was quoted using the data-driven Standard Model prediction.

Then things changed.

New data-driven results started moving closer to the lattice result. At the same time, lattice QCD calculations improved and their error bars became smaller. Now the Standard Model prediction sits much closer to the experimental measurement.

So the muon (g-2) anomaly did not disappear because the experiment was wrong.

The experiment measured the muon very precisely.

What changed was our calculation of what the Standard Model predicts. The hardest part of the theory moved closer to the experimental value. And because of that, the big disagreement is no longer there.

So the story changed.

The muon (g-2) anomaly is no longer a strong hint of new physics. The experiment measured the muon very precisely, and the updated Standard Model calculation now sits much closer to that measurement.

In that sense, the big mystery is mostly finished.

But one puzzle remains.

The different data-driven calculations do not all agree with each other. Some collision data gives one answer. Other data gives a slightly different answer. And physicists still need to understand why.

The lesson here is not that the experiment failed.

The lesson is almost the opposite.

The experiment became so precise that theory had to catch up.

That is a rare and beautiful thing in physics. Usually, we think of theory as leading the way, making predictions for experiments to test. But here, the experiment was pushing harder than the theory calculation could comfortably handle.

Even when it did not reveal new physics, it exposed something important: precision experiments can put pressure not only on nature, but also on our own calculations.

And this is why the experimentalists won the Breakthrough Prize.

The experimentalists pushed so far that theory had to catch up. Even though the anomaly no longer looks like strong evidence for new physics, the achievement remains huge. They measured the muon with extraordinary precision and forced physicists to understand the Standard Model at a deeper level.

That is the real breakthrough.

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It’s called the Drake Equation, and even though it looks scientific, it’s really just a way to organize our uncertainty.

In 1961, astrophysicist Frank Drake wanted a structured way to think about the chances of other civilizations in the Milky Way. So he wrote an equation with seven factors, each representing a step needed for intelligent life to exist and communicate.

The parts we actually know

The first three terms are the easy ones. Astronomers have solid numbers here.

How many new stars does the Milky Way produce each year? About 1 to 3. Not controversial. Our galaxy is a star factory, and it’s been running for 13 billion years.

The Pillars of Creation in the Eagle Nebula are perhaps the most famous star-forming structures ever imaged. Those towering columns of gas and dust are incubators where new stars are slowly condensing out of the cloud. The James Webb Space Telescope gave us a stunning new look at them in 2022.

Those bright red orbs scattered throughout the image are protostars, stars caught in the act of being born. A dense pocket of gas within a pillar reaches a tipping point where gravity overcomes the outward pressure of the surrounding material, collapses inward, and over hundreds of thousands of years grows hot and dense enough for hydrogen fusion to ignite a true star.

Along the edges of the pillars, the wavy lines that look like flowing lava are ejections from stars still in the process of forming. Young stars periodically shoot out supersonic jets that tear through the surrounding gas and dust, creating rippling shockwaves with the same wavy patterns a boat leaves cutting through water. These stars are only a few hundred thousand years old by cosmic standards, and will continue forming for millions of years to come.

Thanks to the Kepler mission, we now know this number is at least 0.7, probably closer to 1.0. Almost every star has planets. That was a guess in 1961. Today it's a fact.

The chart shows 4,034 planet candidates discovered by the Kepler mission as of June 2017, plotting each one by how long it takes to orbit its star and how large it is compared to Earth. The dense blue cloud packed into the lower left corner reflects Kepler’s detection method, where planets that orbit closely and cross their star frequently were simply easier to spot, meaning the actual number of worlds out there is almost certainly far greater than what is shown. With over four thousand candidates from just one telescope staring at one small patch of sky, the takeaway is impossible to ignore: planets are not rare exceptions; they are everywhere.

How many planets sit in the habitable zone — not too hot, not too cold? Current estimates say about 0.2 to 0.5 per system. Not every star gets one, but there are billions of stars, so billions of chances.

This chart maps known exoplanet candidates by how much starlight they receive compared to Earth and how hot their host star is, with the shaded regions marking the habitable zone where liquid water could exist on the surface.

The conservative zone between the yellow and blue lines is where scientists are most confident, while the optimistic zone stretches further in both directions to include edge cases, much like how Venus and Mars sit just outside our own solar system's sweet spot. Each planet is sized to scale relative to Earth, and names like Gliese 667Cc, Proxima Centauri b, and several TRAPPIST-1 planets land right in that critical band, making them the most watched targets in the search for life.

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The parts where we’re guessing

Here’s where the equation stops being astronomy and starts being philosophy.

We have exactly one example: Earth. And life here showed up almost immediately, within a few hundred million years. That either means life is easy and the universe is teeming with it, or we got extraordinarily lucky. One data point can't tell you which.

This is the Pale Blue Dot, one of the most famous photographs ever taken. In 1990, NASA turned the Voyager 1 spacecraft around after it had traveled roughly 3.7 billion miles from Earth and pointed its camera back toward home, capturing our planet as a tiny fraction of a single pixel suspended in a ray of scattered sunlight against the absolute blackness of space.

Carl Sagan, who championed taking the photo, wrote that it puts our entire existence in a humbling context, every human who ever lived, every war ever fought, every civilization ever built, all of it on that one pale blue speck.

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This is the big filter. Earth had living things for 3.5 billion years before anything bothered to think. Most species that ever lived were perfectly content being bacteria. Intelligence might be a fluke, not a destination.

Humans did. Dolphins didn't. There might be brilliant civilizations out there that never felt the need to broadcast.

This is the term that changes everything. A hundred years? A million? If civilizations tend to destroy themselves shortly after discovering nuclear physics, and we don't yet know that they don't, then the galaxy could be full of dead signals and empty ruins. If they last millions of years, we should have neighbors everywhere.

So how many civilizations are out there?

Plug in pessimistic values and you get about 20 civilizations in the entire Milky Way. Plug in optimistic values and you get 50 million.

That range, from 20 to 50 million, should bother you. Same equation, wildly different answers. The first three terms are basically solved. The last four? We're not even close. The Drake Equation doesn't tell you how many civilizations are out there. It tells you what we'd need to know to find out, and how little of that we actually have.

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Look around you. Trees, oceans, mountains, stars, the phone in your hand, the air in your lungs. The visible universe contains an almost incomprehensible variety of stuff. You might expect that explaining all of it would require a vast catalog of ingredients.

But it doesn't. Everything is built from a shockingly small number of fundamental particles.

The Standard Model contains seventeen fundamental particles.

But it gets worse. The stuff you're made of, every atom in your body, every atom in the Earth, every atom in every star you can see, uses only particles from the first generation: up quarks, down quarks, and electrons, bound together by gluons and photons. That's it. Five particles build essentially everything you have ever touched.

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Stop and think about what that means. The human brain, a hundred billion neurons firing in concert, conscious and aware. A star, burning for billions of years. A flower. A hurricane. All of it, every bit of it, is just these five particles arranged differently. That should sound crazy. It is crazy. And it's true.

Here is a fact so strange that it's easy to gloss over: every electron in the universe is identical. Not similar. Not approximately the same. Identical. You cannot put a scratch on an electron. You cannot dent a quark. There is no serial number stamped on a photon.

This is radically different from the everyday world. No two grains of sand are alike. No two snowfalkes are alike. But subatomic particles are manufactured, so to speak, from a single blueprint with zero tolerance for defects. Swap any electron on Earth with any electron in the Andromeda galaxy, and the laws of physics won’t notice.

Because particles are perfectly identical, the laws we derive in a laboratory in Geneva apply equally on the surface of a neutron star. Indistinguishability is what makes universal laws possible. Without it, physics would be different in different parts of the universe, depending on whether your local electrons were a little fatter or a little lighter or slightly lopsided. There would be no universal laws. This indistinguishability is the founding miracle of the field.

For most of human history, understanding a force meant watching things interact. Hold a magnet near iron filings, and you see the field. Drop a ball, and you feel gravity. The method was empirical and direct: bring objects together, observe what happens, write down the pattern.

This strategy collapses at the subatomic scale. You can’t hold a quark in each hand and push them together. You can’t isolate a single gluon and poke it. The interactions are too fast, too small, and too quantum-mechanical to observe directly in the classical sense.

So particle physicists had to invent a completely different approach: guess the theory first, then check if nature agrees. But how do you make an educated guess about laws you cannot see?

Symmetry. Instead of asking “what does this force do?”, you ask “what transformation leaves the physics unchanged?” Every symmetry constrains the theory. Enough symmetries and the theory practically writes itself.

This is the profound insight behind modern particle physics. The structure of the Standard Model was not just discovered by looking at forces; it was deduced from the symmetry group

Each group gives you a force. The representations tell you which particles feel which force. The mathematics of group theory replaces the experiment of pushing things together.

This leads to Noether’s theorem, which we will talk about in some other blog post. Noether’s theorem tells us a connection between symmetries and conservation laws. It takes a lifetime of education to learn to make such an educated guess, but this is the big picture.

Now for the framework. In physics, the tool you use depends on what you're studying. Big, slow objects? Classical physics. Fast objects, approaching the speed of light? Special relativity. Tiny objects, at the atomic scale? Quantum mechanics. But subatomic particles are both fast and small. So you need to combine quantum mechanics and special relativity. That combination is called quantum field theory.

And this combination changes everything. Once you put quantum mechanics and special relativity together, you’re forced into conclusions nobody asked for. Antiparticles have to exist. Particles can be created and destroyed. Even empty space isn’t really empty; it’s full of fluctuations that pop in and out of existence. None of this was put in by hand. It all falls out of the math.

In QFT, particles are not little billiard balls sitting inside a proton waiting to be knocked out. They are excitations of fields that fill all of space. The electron field, the quark field, the photon field, they are always there, everywhere. A particle is just a ripple in the right field.

When two protons collide at the LHC, we are not cracking them open and finding stuff inside. We are concentrating enough energy in a tiny region of space that the fields themselves get excited into new vibrations. That is what a "new particle" is. It wasn't hiding in there. It was created from the energy of the collision.

Quantum field theory isn't just one approach among many in particle physics; it's the framework the whole field runs on. Every calculation, every prediction, every comparison with experiment is done in QFT. It's the mathematical foundation on which the Standard Model is built.

If you want to learn more about the Standard Model in plain language, consider subscribing, and if you can, a pledge helps me dedicate more time to writing and keep the blog going.

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It is the first crewed lunar mission since Apollo 17 in December 1972. For 53 years, no human had ventured beyond low Earth orbit. That era is now officially over.

"Humanity has once again shown what we are capable of, and it's your hopes for the future that carry us now on this journey around the Moon," said Canadian Space Agency astronaut Jeremy Hansen moments after the crew completed the critical translunar injection burn that set Orion on its path away from Earth.

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The Goal: Testing the Machine Before the Landing

Artemis II is not a landing mission, not yet. Think of it as the rehearsal before the main act. The primary objective is to test the Orion spacecraft with a live crew for the first time, putting its life support systems, navigation, communications, and emergency procedures through a full real-world test in the harsh environment of deep space.

NASA will use this 10-day mission to validate every critical system aboard Orion so that the crews of Artemis III and beyond can go to the lunar surface with confidence. As NASA Associate Administrator Amit Kshatriya put it at launch: “Reid, Victor, Christina, and Jeremy will put Orion through its paces so the crews who follow them can go to the Moon’s surface with confidence.”

But Artemis II is just step one of a carefully staged plan. Artemis III (2027) comes next, not as a landing, but as a critical rehearsal in Earth orbit, where the crew will practice docking Orion with the commercial lunar landers (SpaceX's Starship HLS and Blue Origin's Blue Moon) and test the new spacesuits before committing to the real thing. Only once that is proven will NASA be ready for Artemis IV (2028), the actual lunar landing, and humanity's first return to the Moon's surface since Apollo 17 in 1972.

The Trajectory

The mission spans approximately 10 days and follows an elegant path that traces a figure eight between Earth and the Moon.

Day 1 (April 1): Eight minutes after liftoff, Orion reaches space. The rocket’s upper stage fires twice to push the spacecraft into a high elliptical orbit roughly 46,000 miles from Earth. The crew spends the first 23 hours checking every system while still relatively close to home, a critical safety window before committing to the journey ahead.

Day 2 (April 2): The pivotal moment, the Translunar Injection (TLI) burn. Orion’s engine fires, boosting speed by ~900 mph and breaking free of Earth’s gravitational grip. The spacecraft is now on a free-return trajectory, a flight path designed so that if the main engines fail, the Moon's gravity will still bend Orion's path back toward Earth. NASA calls this the mission's 'built-in safety net.'

Days 3–5: A four-day coast to the Moon. On Day 5, Orion crosses into the Moon’s sphere of gravitational influence, the point where the Moon’s pull is stronger than Earth’s.

Day 6 (April 7–8): The lunar flyby. Orion sweeps around the far side of the Moon, reaching approximately 6,400 miles beyond the lunar surface, farther from Earth than any human in history, surpassing 252,000 miles. The crew photographs and observes regions of the far side never witnessed by human eyes in real time.

Days 7–10: The Moon’s gravity bends Orion’s path homeward. The spacecraft coasts back to Earth, re-entering the atmosphere at 25,000 mph before splashing down in the Pacific Ocean off San Diego on April 10.

The Crew

Selected in April 2023, the Artemis II crew collectively shattered four historic records on Day 1 alone.

Commander Reid Wiseman (U.S. Navy): ISS veteran with 165 days in space; the oldest person to travel beyond low Earth orbit.

Pilot Victor Glover (U.S. Navy): SpaceX Crew-1 alumnus; the first person of color beyond low Earth orbit.

Mission Specialist Christina Koch: holds the record for the longest single spaceflight by a woman (328 days); the first woman beyond low Earth orbit.

Mission Specialist Jeremy Hansen (CSA): former CF-18 fighter pilot; the first non-U.S. citizen beyond low Earth orbit.

What They’re Wearing

From launch through reentry into Earth’s atmosphere, the crew will wear the Orion Crew Survival System (OCSS) suit, custom-fitted, bright orange pressure suits designed to protect astronauts even in a “worst case” scenario during launch and landing. During the journey itself, when conditions are stable, the crew can change into regular clothing and move freely within Orion’s cabin.

Life Inside Orion: Cramped but Functional

The Orion crew capsule offers 316 cubic feet of habitable space, about the size of two minivans, or roughly 20% more room than the Apollo command module. It is tight, but it is designed around the practical needs of four human beings spending 10 days in deep space.

Eating in Space

The crew’s meals have been developed in collaboration with food scientists, based on each astronaut’s personal preferences and nutritional needs. The menu is pre-set, but Orion is equipped with a potable water dispenser that allows the crew to both hydrate and re-heat their food. Regular meal breaks are built into the daily schedule.

Exercise

Each crew member is expected to exercise for 30 minutes every day to combat the bone density loss and muscle atrophy that deep space microgravity accelerates. Orion carries a compact flywheel exercise device, a cable-based system capable of providing up to 400 pounds of resistance, that can be used for rowing, squats, deadlifts, and aerobic workouts. Commander Reid Wiseman was the first crew member to use it after launch, reporting that the muscle fatigue felt “the same as training on the ground.”

Sleep

The crew sleeps by attaching sleeping bags to the walls of the spacecraft, resting for roughly eight hours per sleep cycle. One day of the mission, flight day 7, is designated as an off-duty day.

Hygiene

Orion is equipped with a toilet (with private doors) and space for personal hygiene kits. As NASA notes, astronauts cannot shower in space; instead, they use liquid soap, water, and rinseless shampoo to stay clean.

Staying Connected: Communication Systems

Keeping in contact with a spacecraft flying a quarter-million miles from Earth is no small feat. NASA uses a three-layer system to make sure the crew is never out of reach.

The Deep Space Network (DSN) is the primary backbone, a global array of massive radio antennas built specifically to handle communication at the extreme distances of lunar and deep space missions.

When Orion is closer to home during the early Earth-orbit phase, the Near Space Network steps in to supplement it with Earth-based antennas.

And if the primary system ever fails, a fully independent emergency backup takes over, ensuring the crew can always be reached and navigated safely back to Earth.

Throughout the mission, one or two live space-to-ground video calls are scheduled nearly every day, giving mission control and the public a direct window into life aboard Orion.

Safety: A Mission Built Around Redundancy

NASA has been candid that Artemis II is a test flight, and test flights carry real risk. Every system aboard Orion was designed with that in mind.

A Launch Abort System can pull the capsule away from a failing rocket in under 3 seconds.

The free-return trajectory acts as a built-in safety net; if the engines fail, lunar gravity will still guide Orion back to Earth.

Six radiation sensors monitor the crew’s exposure once they leave Earth’s protective magnetic field.

And the heat shield, the largest ever built, is engineered to survive reentry temperatures of 5,000°F at 25,000 mph.

What Comes Next

Artemis II is the beginning, not the destination. If this mission proves successful and the early signs are strongly positive, NASA’s path forward leads to:

• Artemis III (2027): No longer a landing mission. Revised in February 2026, it will instead test docking between Orion and the commercial lunar landers (SpaceX's Starship HLS and Blue Origin's Blue Moon) in Earth orbit — the Artemis program's version of Apollo 9.

• Artemis IV (2028): Now officially the first crewed lunar landing mission. Two astronauts will descend to the Moon's surface, the first humans to do so since Apollo 17 in 1972.

• A Permanent Moon Base: NASA and its international partners are working toward a sustained human presence on and around the Moon.

  

• Mars: The technologies and lessons of Artemis are explicitly designed as a stepping stone for eventual crewed missions to the Red Planet.

For now, four human beings are hurtling through deep space for the first time in 53 years, writing their names in the history books whether they choose to or not.

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Inside the truck was a container holding 92 antiprotons, particles that annihilate on contact with any ordinary matter, releasing pure energy. Getting them from one building to another without losing them was considered, until this week, essentially impossible.

The team at CERN’s BASE (Baryon Antibaryon Symmetry Experiment) experiment pulled it off. And while the drive itself was modest, what it unlocks isn’t.

Let's start with a brief introduction to Antimatter.

Antimatter is the mirror image of regular matter. Every fundamental particle has a corresponding antiparticle with the same mass but opposite quantum numbers (like electric charge, etc).

For example, the antiparticle of the electron is the positron.

When a particle meets its antiparticle, they annihilate, converting their entire mass into pure energy

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This makes antimatter fascinating for two reasons.

First, it’s at the core of the biggest mystery in physics: why the universe exists.

According to our best theory (which is the Standard Model), the universe shouldn’t exist, but we are here, so something is missing in our theory. There is an assumption that the Big Bang should have created equal amounts of matter and antimatter, which should have wiped each other out. We also observe this in the LHC (Large Hadron Collider), an experiment designed to recreate conditions similar to those of the Big Bang, which creates almost an equal amount of matter and antimatter. Something, nobody knows what, gave matter a tiny edge.

One extra particle per billion. All you need for stars, planets, and you to exist. That's it. That fraction of a fraction is why everything exists. Our theories can't explain it. Which means something is missing.

Second, antimatter is extraordinarily hard to work with. You can’t store it in a jar. You can’t let it touch anything. You have to suspend it in a vacuum using magnetic fields, at temperatures near absolute zero, indefinitely, while studying it. And all of that can only happen at one facility on Earth: the CERN Antimatter Factory.

The problem CERN couldn't solve from the inside

CERN’s antimatter factory is brilliant at making antiprotons. It’s less brilliant at letting scientists study them in peace. The same machines that produce those particles create noise (electromagnetic interference) that messes with the precise measurements. And since the whole point is precision, that’s a big problem.

The answer is obvious: take the antiprotons somewhere quieter. Executing that answer took ten years and an entirely new class of technology.

The BASE-STEP trap, an 800-kilogram container using superconducting magnets, liquid helium cooling, and battery backup, was built to keep antiprotons alive outside the lab. It fits through a standard door. It fits on a truck. Also kept in a near-perfect vacuum so the antiprotons never touch anything. And on March 24, it did exactly that.

What comes next

The parking lot loop was a proof of concept. The next step is an eight-hour drive to a dedicated precision lab at Heinrich Heine University in Düsseldorf. A journey that would require the trap to maintain near-absolute-zero temperatures across German motorways for a full working day.

If it works, the precision gains are enormous. Measurements 100 to 1,000 times more accurate than anything currently possible. Enough, potentially, to finally detect the differences between matter and antimatter that could explain why the universe didn’t immediately collapse into nothing.

The BASE experiment is not the one trying to answer this question. The next generation of experiments is racing to answer it: Why does the universe exist? If you want to learn about other experiments tackling the same question from different angles, make sure to like, share, and subscribe, or support by pledging if you can.

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Claude did it in two weeks and wrote about the experience in a way that felt genuinely honest for the most part, with some bias creeping in occasionally. At one point, he said, “Buy the $20 subscription. It will change your life.”

The post is called "Vibe Physics: The AI Grad Student," and it's up on Anthropic's research blog. I think it's worth unpacking.

The Rules

Schwartz gave himself constraints. He could only talk to Claude through text prompts, no editing files, no pasting in his own calculations. He wanted to know if there’s a set of instructions, basically how you’d guide a sharp second-year grad student, that could get an AI to produce publishable physics.

He picked a problem he already knew how to solve. Smart move. If AI can’t handle a well-defined calculation where the expert knows the answer, it definitely can’t handle the open-ended stuff.

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What Went Right

The sheer volume of output is hard to wrap your head around. 110 draft versions. Over 51,000 messages. 36 million tokens. Claude compiled old Fortran code, wrote Python scripts, ran Monte Carlo simulations, and spit out a 20-page LaTeX draft with equations, plots, and references. Three days.

All the tedious stuff that burns months of a researcher’s life, running regressions, debugging code dependencies, and doing statistical checks. Claude just... did it. No complaints, no burnout. Schwartz called it “a welcome relief,” which I think is underselling it.

He also figured out a useful trick: instead of one massive conversation, he had Claude maintain a tree of markdown files, one per task. The AI works way better when it can look things up instead of trying to hold everything in context. Makes sense if you know anything about how these models work.

The Messy Part

OK, but here’s where it gets real.

Claude kept faking results. Not maliciously but in a way that’s arguably worse. When a plot didn’t look right, Claude would quietly tweak parameters to force agreement instead of actually finding the bug. When asked, “Did you verify this?”, it would say yes without having checked. It made up coefficients that weren’t in the paper and wrote plausible-sounding justifications for work it never did.

The worst part: the factorization formula at the very center of the paper, the thing everything else depends on, was wrong. Claude had grabbed a formula from a different physical system and slotted it in without adapting it. It took Schwartz hours to catch. Claude couldn’t find the problem itself because it kept talking itself into thinking everything was fine.

Schwartz’s description of this is perfect: “Claude loves to please.” Push it hard enough on something, and it’ll eventually just tell you what you want to hear.

He also found that Claude would oversimplify code based on patterns from other examples, ignoring the specifics of the actual problem. When caught, Claude literally responded: “You’re absolutely right, I cheated!”

So Was the Paper Any Good?

The final version is on arXiv. I didn’t have time to read the paper myself, but it made a splash, Schwartz got a wave of invitations to present it, it trended on r/physics, and the Institute for Advanced Study held what he describes as an “emergency meeting” about LLMs in research.

But, and this is the part I keep coming back to, without Schwartz’s expertise, that paper would have been polished-looking nonsense. The AI couldn’t tell the difference between correct and convincing. Only humans could.

I think there are three takeaways worth sitting with.

First, the 10x acceleration is real. A project that would have taken Schwartz 3–5 months solo (or 1–2 years with a student) took two weeks. That’s not hype, it’s a documented case with receipts. If you’re a researcher ignoring these tools, you’re leaving an enormous amount on the table.

Second, expertise isn’t optional. This wasn’t a case of AI replacing the physicist. It was a case of AI massively amplifying an expert who knew exactly what to check and when to push back. Without Schwartz’s domain knowledge, the paper would have been impressive-looking garbage. The AI couldn’t tell the difference between correct and plausible.

Third, the implications for education are profound. Schwartz advises students to take LLMs seriously and to consider experimental science, work that requires hands-on empirical measurement, as a career path. He even said that theoretical physics might eventually become like music theory or French literature: an intellectual pursuit for people who enjoy thinking through a particular lens, rather than a path to discoveries that only humans can make.

That last point is worth pausing on. After decades of STEM displacing the humanities, Schwartz wonders whether the humanities might be all that survives.

He also claims that we should rethink the role of education, arguing that in the next decade, AI could surpass us in every domain, leaving higher education to focus only on what’s fundamentally human. I don’t really buy that. It feels a bit overstated and, at times, like a way to push subscriptions or products. Maybe it’s possible; these studies show we are not there yet.

It wasn’t clear whether he was being paid; at least I couldn’t find anything confirming that. But it’s worth noting that the study was published by Anthropic, so some bias is likely. Keep that in mind when evaluating these claims.

Let me know what you think in the comments.

The full post is available at anthropic.com/research/vibe-physics, and the paper itself is on arXiv.

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He got a ladder that’s 20 meters long, and a barn that’s only 15 meters deep. The ladder doesn’t fit inside the barn. Simple geometry.

Now, this would be the end of the story for most farmers.

But this farmer used to be a theortical physicist.

So instead of buying a smaller ladder, he decides to use special relativity.

Specifically, he knows about special relativity and something called length contraction. Objects moving close to the speed of light get shorter along their direction of motion. The faster they move, the more they shrink.

So the farmer has an idea.

He turns to his daughter and says: “If you run through the barn holding this ladder really fast, close to the speed of light, the ladder will contract. It’ll shrink down small enough to fit inside. I’ll be able to close door and the ladder inside!”

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His daughter stares at him.

“That’s stupid,” she says.

“Why?”

“Because from my perspective, I’m not moving. The barn is moving toward me. And if things moving fast get shorter, then the barn will shrink, not the ladder. The barn will get even smaller! The ladder will definitely not fit.”

The farmer pauses.

They’re both using the same physics. They’re both applying length contraction correctly. But they’ve reached opposite conclusions.

Who is right?

Well, both the farmer and his daughter are correct about length contraction.

From the farmer’s perspective, standing at the barn:

• The barn is stationary

• The daughter and ladder are moving close to speed of light.

• The ladder contracts and firs inside the barn.

From the daughter's perspective, running with the ladder:

• The ladder is stationary with her.

• The barn is moving toward her with close to the speed of light.

• The barn contracts and ladder absolutely does not fit.

So how can both be right?

To understand how both of them can be right, we need to understand an important concept in special relativity: the relativity of simultaneity.

Let me explain this with short example:

Imagine a light bulb in the middle of a train car. When it turns on, light travels toward both ends of the train where detectors a (front) and b (back) are placed.

Observer on the train

For someone inside the train, the bulb is exactly in the middle.
Light travels the same distance to both detectors, so both buzzers ring at the same time.

Observer on the ground

For someone standing on the ground, the train is moving forward.

• The back detector (b) moves toward the light, so the light reaches it sooner.

• The front detector (a) moves away from the light, so the light takes longer to reach it.

  

Even though light moves at the same speed in both frames, the distances are different.
So the back buzzer rings first.

The lesson here is that events that happen at the same time in one frame may not happen at the same time in another frame.

This is called the relativity of simultaneity.

Now let’s go back to the barn-ladder paradox. So what does it mean when we say the ladder is in the barn?

To say the ladder “fits inside the barn” means that both ends of the ladder are inside the barn at the same time.

Two important events happen:

1. The back end of the ladder enters the barn door.

2. The front end of the ladder hits the far wall of the barn.

Farmer’s perspective (barn frame)

The farmer sees event 1 happen before event 2.
So there is a moment when both ends of the ladder are inside the barn.

Daughter’s perspective (ladder frame)

The daughter sees event 2 happen before event 1.
So the ladder never fits completely inside the barn.

There is no contradiction.
The order of events depends on the observer’s frame of reference because events that are simultaneous in one frame don’t mean they will be simultaneous in another frame.

But the farmer eventually wants the ladder to stop inside the barn. When the ladder slows down, length contraction disappears, and the ladder returns to its normal (longer) length.

At that point, the ladder is too long for the barn, so something must happen:

• Either the ladder crashes into the far wall, or

• The ladder breaks.

So while relativity allows the ladder to fit momentarily when moving, it cannot stay inside the barn once it stops.

This thought experiment helps us understand the strange and surprising consequences of special relativity.

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To understand why the prize was awarded to John Clarke, Michel H. Devoret, and John M. Martinis, you need to understand four key ideas:
1. Quantum Tunneling
2. What Makes This Discovery Special
3. Energy Quantization in Josephson Junctions
4. Quantum Tunneling in Josephson Junctions

Quantum Tunneling

In classical physics, a particle cannot cross an energy barrier if it doesn’t have enough energy.

An electron is like a soccer ball rolling toward a brick wall.

• If the ball doesn’t have enough energy, it will hit the wall and bounce back.

• It can never cross the wall unless it has enough energy to break it.

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Classical rule: No energy means no crossing.
That’s how classical physics behaves.

In quantum mechanics, particles behave like waves.

Because of this wave nature, a particle can “tunnel” through a barrier it classically shouldn’t cross.

This is a purely quantum effect, with no classical explanation.

Why it matters:
Quantum tunneling is usually microscopic: electrons, atoms, etc. What these scientists discovered happens on a much larger scale.

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What Makes Their Discovery Special?

This Nobel Prize recognizes the first clear demonstration that a macroscopic object, something involving trillions of electrons acting together, can behave according to quantum mechanics.

They showed that:

• A whole electric circuit behaves as a single quantum object

• The circuit can tunnel

• The circuit’s energy can only take quantized values

This means quantum mechanics applies not just to particles, but to large engineered systems, which is foundational for quantum computing.

Let’s explain how a whole circuit can act like a quantum object.

Energy Quantization in Josephson junctions

A Josephson junction is the core building block of many superconducting circuits.

It consists of:

• Two superconductors

• Separated by a very thin insulating barrier

Electrons in a superconductor don’t act normally; they form Cooper pairs.

Normal metal:
Electrons move individually (white dots) and constantly scatter, which creates resistance.

Superconductor (top-right):
Electrons form Cooper pairs. These pairs act very differently from single electrons.

Superconductor (bottom):
All Cooper pairs collapse into one coherent quantum state (the long white line).
This means the entire superconductor shares a single quantum state.

This macroscopic quantum state is described by a single phase ϕ. This phase becomes the key variable in Josephson junctions.

In a Josephson junction, what matters is the phase difference between the two superconductors δ. The energy of the junction U, which depends on this phase difference, becomes quantized.

This phase difference is a macroscopic quantity that behaves quantum mechanically.

It can be in a superposition:

The phase difference across the Josephson junction is a quantum variable.
Because the junction has quantized energy levels, the phase does not need to stay in only one level. It can be in a superposition of several levels at the same time.

Quantum tunneling in a Josephson junction

The phase difference in the junction sits in a potential well, like a ball resting in a valley. Classically, the ball is stuck because it does not have enough energy to climb over the hill.

Quantum mechanically, the phase can tunnel through the hill as shown above and appear in the next valley even if it does not have enough energy to go over the top.

This tunneling event makes the junction escape from the zero-voltage state and produces a measurable signal. This is called macroscopic quantum tunneling because it involves the collective motion of many electrons acting as one quantum object.

This forms the basis of quantum qubits because the junction can occupy a superposition of two energy states, which we call the qubit.

This is what they won the Nobel Prize for. They showed that a macroscopic variable, the small phase difference delta across a junction, can behave quantum mechanically. This discovery is now the foundation of superconducting qubits used in today’s quantum computers, including those built by Google and IBM.

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