Astronomers initially thought the dramatic burst of light was a new exoplanet. Breakthroughs, discoveries, and DIY tips sent every weekday. What astronomers initially suspected to be a new exoplanet is actually a never-before-seen, head-on cosmic crash. As detailed in a study published today in the journal, researchers describe the aftermath of two separate collisions between two small, rocky cosmic objects called planetesimals . However, their findings were only made possible by some eagle eye imaging courtesy of the Hubble Space Telescope .

Breakthroughs, discoveries, and DIY tips sent every weekday. Nothing is permanent--not even the universe itself. At least, that's what current models of physics tell us about the nature of existence. Luckily for humanity, most astrophysicists' estimates don't have the universe's grand finale scheduled for around 10¹¹⁰⁰ years (that's a 1 followed by 1,100 zeros). However, based on new calculations that include the peculiar nature of certain black hole particles, the universe's curtains may fall much sooner than expected--cosmically speaking.

A renowned astrophysicist is calling foul on reports of alien sightings in Earth's atmosphere, arguing that biological creatures would be unable to survive a journey to our planet. "It would take about a billion years to cross from one side of the Milky Way galaxy to the other," Avi Loeb, a Harvard astrophysicist, said during an appearance on GB News this week. "Given that, I don't think any spacecraft that would arrive to us from another star would carry biological creatures." Loeb's comments come amid increased reports of UFO sightings in recent years, with videos and pictures of supposed alien craft going viral across the internet. It also comes after NASA created a new position aimed at overseeing research on UFOs after a 2022 study by the agency determined that such sightings were unlikely to be caused by extraterrestrial life.

Using a bit of machine learning magic, astrophysicists can now simulate vast, complex universes in a thousandth of the time it takes with conventional methods. The new approach will help usher in a new era in high-resolution cosmological simulations, its creators report in a study published online on May 4, 2021, in Proceedings of the National Academy of Sciences. "At the moment, constraints on computation time usually mean we cannot simulate the universe at both high resolution and large volume," says study lead author Yin Li, an astrophysicist at the Flatiron Institute in New York City. "With our new technique, it's possible to have both efficiently. In the future, these AI-based methods will become the norm for certain applications."

Using FIRE (Feedback In Realistic Environments) simulations, astronomers are able to model groups of stars, revealing the origin of these stellar furnaces. Starting models soon after the Big Bang, this simulation, one of the largest models of its type, shows how galaxies form into the formations we see today. Even using supercomputers, these nine simulations took months to complete. The GAIA spacecraft, launched in 2013, is on a mission to create 3D maps of a billion stars in and beyond the Milky Way. The observatory provides the motions of one billion stars.

While three planets have been detected in the Kepler-431 system, little is known about the shapes of their orbits. On the left are a large number of superimposed orbits for each planet that are consistent with observations. An international team of astrophysicists led by Princeton's Daniel Tamayo removed all the unstable configurations that would have already collided and couldn't be observed today. Doing this with previous methods would take over a year of computer time. With their new model SPOCK, it takes 14 minutes.

Future telescopes like the James Webb Space Telescope (JWST) and the Atmospheric Remote-sensing Infrared Exoplanet Large-survey (ARIEL) are designed to sample the chemistry of exoplanet atmospheres. Ten years from now, spectra of alien skies will be coming in by the hundreds, and the data will be of a higher quality than is currently possible. Astronomers agree that new analysis techniques, including machine learning algorithms, will be needed to keep up with the flow of data and have been testing options in advance. An upcoming study in Monthly Notices of the Royal Astronomical Society trialed one such algorithm against the current gold standard method for decoding exoplanet atmospheres to see whether the algorithm could tackle this future big-data problem. "We got really good agreement between [the answers from] our machine learning method and the traditional Bayesian method that most people are using," said Matthew Nixon.

For the past 15 years, astrophysicists at the University of Bern in Switzerland have used specialized techniques to predict planet masses. Unfortunately, the feat requires solving sets of complicated and time-consuming differential equations. As a solution, the Swiss scientists adopted artificial intelligence into their work to help improve and speed up their computing process. 'There is a big hype also in astronomy. Machine learning has already been used to analyze observations, but to my knowledge, we are the first to use deep learning for such a purpose." Planets usually form in stellar disks that accumulate solid materials and gasses.

In the last years, crowdsourcing is transforming the way classification training sets are obtained. Instead of relying on a single expert annotator, crowdsourcing shares the labelling effort among a large number of collaborators. For instance, this is being applied to the data acquired by the laureate Laser Interferometer Gravitational Waves Observatory (LIGO), in order to detect glitches which might hinder the identification of true gravitational-waves. The crowdsourcing scenario poses new challenging difficulties, as it deals with different opinions from a heterogeneous group of annotators with unknown degrees of expertise. Probabilistic methods, such as Gaussian Processes (GP), have proven successful in modeling this setting. However, GPs do not scale well to large data sets, which hampers their broad adoption in real practice (in particular at LIGO). This has led to the recent introduction of deep learning based crowdsourcing methods, which have become the state-of-the-art. However, the accurate uncertainty quantification of GPs has been partially sacrificed. This is an important aspect for astrophysicists in LIGO, since a glitch detection system should provide very accurate probability distributions of its predictions. In this work, we leverage the most popular sparse GP approximation to develop a novel GP based crowdsourcing method that factorizes into mini-batches. This makes it able to cope with previously-prohibitive data sets. The approach, which we refer to as Scalable Variational Gaussian Processes for Crowdsourcing (SVGPCR), brings back GP-based methods to the state-of-the-art, and excels at uncertainty quantification. SVGPCR is shown to outperform deep learning based methods and previous probabilistic approaches when applied to the LIGO data. Moreover, its behavior and main properties are carefully analyzed in a controlled experiment based on the MNIST data set.

Chris Moody knows a thing or two about the universe. As an astrophysicist, he built galaxy simulations, using supercomputers to model the way the universe expands and how galaxies crash into one another. One night, not long after he'd finished his PhD at UC Santa Cruz, he met up with a few other astrophysicists for beers. But that night, no one was talking about galaxies. Instead, they were talking about fashion.