The StarXiv ✨ podcast

@starxiv.bsky.social

A podcast where Payel Das (@payeldas.bsky.social) and Michelle Collins (@runningastronomer.bsky.social) discuss papers from astroph Starxiv.com Spotify - https://shorturl.at/xdTFw Apple - https://shorturl.at/nKFNj RSS - https://rss.com/podcasts/starxiv

Michelle's first paper is hunting for Dyson spheres! The authors used JWST to follow up two M dwarf stars with an infrared signature at their position. The results? These two objects are not Dyson spheres. The hunt for alien life continues! 👽🔭☄️🧪https://arxiv.org/abs/2607.09460

Images of the Dyson sphere candidates in several JWST bands. We see that there is an offset between the star and the infrared source, making them not Dyson spheres

Episode 40 – globular clusters, extended disks, high redshift galaxies and aliens! In this episode, Michelle and Payel explore the search for extra-terrestrial life, mechanisms for light element variations in globular clusters, characteristics of a typical galaxy at z>10, and Andromeda's extended…

Episode 40 – globular clusters, extended disks, high redshift galaxies and aliens!

In this episode, Michelle and Payel explore the search for extra-terrestrial life, mechanisms for light element variations in globular clusters, characteristics of a typical galaxy at z>10, and Andromeda's extended stellar disc origin.

starxiv.com

Michelle's second paper asked if we can classify supernova from photometry alone. This is not without problems, given imbalance of types of supernova in training sets, and shifting between different datasets. But, this teams mixing approach works really well! arxiv.org/abs/2605.28922 ☄️ 🔭

Photometry is all you need: supernova classification as a mixing problem

In the era of large-scale photometric surveys, scalable and robust methods for classifying supernova (SN) populations are increasingly necessary. Often, spectroscopy is essential in addition to…

arxiv.org

Michelle's first paper looked at using vision-language models and a Bayesian uncertainty framework for classifying galaxies undergoing mergers, Turns out, the machines can do as well as human experts! 🔭 ☄️https://arxiv.org/abs/2606.00415

A histogram showing the merger fraction estimated by the VLMs looking at simulated images. It matches the true merger fraction really well (0.52 vs 0.59)! The machines are coming for our jobs...

Episode 38 – merging galaxies, exploding stars and the beauty of individual galaxies In this episode, Michelle and Nicole explore machine learning techniques for classifying merging galaxies and supernovae. They discuss planetary engulfment's role in unusual chemical signatures in binary systems…

Episode 38 – merging galaxies, exploding stars and the beauty of individual galaxies

In this episode, Michelle and Nicole explore machine learning techniques for classifying merging galaxies and supernovae. They discuss planetary engulfment's role in unusual chemical signatures in binary systems and analyse MUSE data of a spiral galaxy.

starxiv.com