标签：#Astronomy

Astronomers will test equipment that, if it’s successful, will one day be crucial for discovering Earthlike planets.

How AI Helps Us Explore the Universe Modern telescopes and space missions generate more data in a single night than a team of human astronomers could review in a lifetime. The Vera C. Rubin Observatory in Chile, for instance, is expected to produce up to seven million alerts every night once it reaches full operational cadence, each one flagging something in the sky that changed since the last image. No group of humans can look at that stream and make sense of it in real time. Machine learning can, and increasingly does. This is the quiet story behind most recent breakthroughs in astronomy: it is not just bigger telescopes, but bigger telescopes paired with models that can filter, classify, reconstruct, and predict faster than any manual pipeline. Here is a tour of where AI is actually doing that work, and why it matters to anyone who writes code. The Data Problem Comes First Space science has quietly become a big data problem. The Rubin Observatory's ten-year Legacy Survey of Space and Time will produce roughly 60 petabytes of raw imagery and catalog around 20 billion galaxies and a similar number of stars. Every image the telescope takes is compared, pixel by pixel, against previous images of the same patch of sky, and any meaningful difference (a moving asteroid, a brightening supernova, a flaring galactic nucleus) triggers an alert within about two minutes of the exposure being taken. That alert stream is too large and too fast for manual triage. So astronomers built software "brokers": machine learning classifiers that sit between the telescope's raw output and the scientists, deciding in near real time which alerts are worth a second look. This is a pattern you will see across almost every domain of modern astronomy: instruments generate more signal than humans can parse, and a model is inserted into the pipeline to do the first pass of filtering. Finding Planets in a Sea of Noise Exoplanets are found mostly through the transit method: a planet passes in front

I built a calculator for BaZi — Chinese "Four Pillars" birth charts. Whatever you think of the interpretive tradition (and I'll get to that), the input math turned out to be a genuinely deep time-zone problem, and that's what this post is about. If you've ever thought "time zones, how hard can it be" — this is a tour of exactly how hard, with working TypeScript. The problem BaZi divides the day into twelve two-hour "branches", so your birth hour is one of the chart's four pillars. Get the hour wrong and you get a different chart — not slightly different, categorically different. Every calculator I could find feeds the system the wall-clock time from your birth certificate. But the tradition predates time zones by about two thousand years; it obviously means solar time — where the sun actually was over your birthplace. Clock time and solar time differ by more than most people think, and the difference decomposes into exactly three parts: 1. Daylight saving time — and it's historical. You need the DST rules in force on the birth date , not today's. China ran a now-forgotten DST experiment from 1986–91; Harbin kept its own zone before 1949. If you were born in Beijing in July 1988, your certificate is an hour ahead of standard time and no modern-day lookup will tell you that. 2. Longitude. Solar time shifts 4 minutes per degree from your zone's standard meridian. China spans five geographic zones but uses one clock — born in Ürümqi, your clock runs about two hours ahead of the sun. It's not just a China quirk: Vancouver sits at 123°W in a zone whose meridian is 120°W, so that's another 12 minutes, everywhere, always. 3. The equation of time. The sun itself runs up to ±16 minutes fast or slow over the year, thanks to orbital eccentricity and axial tilt. NOAA publishes an approximation that's accurate to under a minute: /** Equation of time (minutes), NOAA approximation */ export function equationOfTimeMinutes ( dayOfYear : number ): number { const b = ( 2 * Math . PI *

Star, moving at 8% the speed of light, may let us measure the black hole's rotation.

The summer is officially over. I am staring at a remarkably clean Git branch, my laptop didn't literally take off into orbit (though the CPU fans certainly tried a few times during local CI builds), and I somehow know what git rebase -i does without having to Google it in a cold sweat. If you'd asked me back in May what I was going to be doing, I would have confidently told you I was going to "write tests for Astropy's C extensions." It sounded so neat. So contained. But open source doesn't really work like that. I came in thinking I was just going to write tests, and somewhere along the way, I ended up learning how the actual machinery underneath the Python abstraction works, how maintainers think about architecture, and how to safely catch C-level memory panics without taking down the entire interpreter. So, here is the real story of what I did for the last few months, what broke, how we fixed it, and where the project stands now. So, what was I actually supposed to do? Astropy is a beast of a library. The Python-facing API is incredibly robust and beautifully documented. But underneath all those pretty Python classes is a complex, mixed-language architecture. The library relies heavily on compiled C, and Cython extensions to handle the performance-critical hot-paths. The problem? That compiled layer was a massive testing blind spot. Before this summer, these performance-critical extensions were almost entirely tested indirectly, meaning they were only validated by calling the high-level Python wrappers. That is a risky abstraction. If a regression happens deep inside the C code, the Python layer sitting above it can accidentally mask it. You wouldn't know something was fundamentally broken until a downstream package started acting weird. My project goal was to build a dedicated, de novo test suite that bypassed the public API completely and exercised each compiled extension module directly. This wasn't just for code coverage. It was an absolute prerequisite for t

Fitted with telescopes, the plane did our longest imaging of the Sun's corona.

We expected them to be there, but they had previously been too small to see.

Here’s when some of this year’s most anticipated astronomical events will happen and the best way to observe them.

Also: Cyborg diving suits for cockroaches, why sleepy sperm whales blow bubbles, Betelgeuse's companion star.

From how galaxies form to the beginnings of the universe, the images from the space telescope are giving cosmologists new data to explore—and theories to build out.

A discovery in a solar system 73 light-years from Earth is challenging definitions and “blurring the lines between stars, planets, and moons.”

Scientists have long assumed that there’s an upper limit to the intensity of the solar winds that reach Earth. New research suggests there’s not—and the implications are alarming.

Black holes eject powerful energy jets that blow away the surrounding gas to great distances. So how can they continue to grow?

Astronomers have discovered a rocky exoplanet that has an atmosphere and sits within the habitable zone of a solar system.

If the large, rocky planet is losing helium, then we can infer what is left behind.

Researchers identified traces of erythrulose, a monosaccharide, thousands of light-years away.

The FCC authorized Reflect Orbital to launch the mirror satellite Eärendil-1. “For optical astronomy, this poses an existential threat,” the European Southern Observatory said.

It's unclear how the planet avoided its star's bloated red giant stage.

Something on Pluto and one of Saturn’s moons, Titan, absorbs light in a way unexplained by anything in spectroscopic databases.

A new study suggests that when our star becomes unstable in 5 billion years or so, Earth may avoid being engulfed by its fiery demise.