ChatGPT and other state-of-the-art large language models (LLMs) are rapidly transforming multiple fields, offering powerful tools for a wide range of applications. These models, commonly trained on vast datasets, exhibit human-like text generation capabilities, making them useful for research tasks such as ideation, literature review, coding, drafting, and outreach. We conducted a study involving 13 astronomers at different career stages and research fields to explore LLM applications across diverse tasks over several months and to evaluate their performance in research-related activities. This work was accompanied by an anonymous survey assessing participants' experiences and attitudes towards LLMs. We provide a detailed analysis of the tasks attempted and the survey answers, along with specific output examples. Our findings highlight both the potential and limitations of LLMs in supporting research while also addressing general and research-specific ethical considerations. We conclude with a series of recommendations, emphasizing the need for researchers to complement LLMs with critical thinking and domain expertise, ensuring these tools serve as aids rather than substitutes for rigorous scientific inquiry.

In the realm of precision medicine, effective patient stratification and disease subtyping demand innovative methodologies tailored for multi-omics data. Clustering techniques applied to multi-omics data have become instrumental in identifying distinct subgroups of patients, enabling a finer-grained understanding of disease variability. This work establishes a powerful framework for advancing precision medicine through unsupervised random-forest-based clustering and federated computing. We introduce a novel multi-omics clustering approach utilizing unsupervised random-forests. The unsupervised nature of the random forest enables the determination of cluster-specific feature importance, unraveling key molecular contributors to distinct patient groups. Moreover, our methodology is designed for federated execution, a crucial aspect in the medical domain where privacy concerns are paramount. We have validated our approach on machine learning benchmark data sets as well as on cancer data from The Cancer Genome Atlas (TCGA). Our method is competitive with the state-of-the-art in terms of disease subtyping, but at the same time substantially improves the cluster interpretability. Experiments indicate that local clustering performance can be improved through federated computing.

ABSTRACT We demonstrate the potential of the state-of-the-art OpenAI GPT-4 large language model to engage in meaningful interactions with Astronomy papers using in-context prompting. To optimize for efficiency, we employ a distillation technique that effectively reduces the size of the original input paper by 50%, while maintaining the paragraph structure and overall semantic integrity. We then explore the model's responses using a multi-document context (ten distilled documents). Our findings indicate that GPT-4 excels in the multi-document domain, providing detailed answers contextualized within the framework of related research findings. INTRODUCTION Large language models (LLMs) have significantly advanced natural language processing, allowing machines to process and generate intricate text with remarkable quality (e.g., Devlin et al. 2018; Brown et al. 2020; Chowdhery et al. 2022; Bubeck et al. 2023).

In images collected by astronomical surveys, stars and galaxies often overlap visually. Deblending is the task of distinguishing and characterizing individual light sources in survey images. We propose StarNet, a Bayesian method to deblend sources in astronomical images of crowded star fields. StarNet leverages recent advances in variational inference, including amortized variational distributions and an optimization objective targeting an expectation of the forward KL divergence. In our experiments with SDSS images of the M2 globular cluster, StarNet is substantially more accurate than two competing methods: Probabilistic Cataloging (PCAT), a method that uses MCMC for inference, and DAOPHOT, a software pipeline employed by SDSS for deblending. In addition, the amortized approach to inference gives StarNet the scaling characteristics necessary to perform Bayesian inference on modern astronomical surveys.

Phil Plait, creator of the popular astronomy blog Bad Astronomy, credits his interest in outer space partly to his childhood love of science fiction movies such as Angry Red Planet and Robinson Crusoe on Mars. "I'm a huge science fiction dork," Plait says in Episode 541 of the Geek's Guide to the Galaxy podcast. "I've watched every TV show, just about, and movies and everything, read tons of books. In his new book, Under Alien Skies, Plait explores what various cosmic vistas would look like for a person who was physically present, studying them with ordinary human eyesight. "I open each chapter with a short vignette, basically a fictional tale," he says. So I say'You are at this planet,' 'You are standing on the bridge of your starship,' 'You are standing there watching a dust storm approach you on Mars.' Plait hopes that the book will serve as a valuable resource for filmmakers and science fiction authors looking to inject an extra dose of reality into their speculative visions. "I've actually done some consulting for movies and TV shows, and even a couple of video games," he says. "So I kind of know that process of advising writers, or other folks who are involved in the entertainment business, of what the real science is." As much as Plait enjoys seeing science fiction that incorporates real science, he recognizes that the ultimate aim of any book or movie is to tell a good story. "Even if they don't get the science correct, it's OK, because you're still inspiring people," he says. "And if they get the science right?

It is a thing of mesmerising beauty: humanity's first glimpse at the only full resolution photo of a supermassive black hole ever produced. This'orange donut', as it has been dubbed, sits at the heart of the Messier 87 galaxy 55 million light-years from Earth and in 2019 became the first black hole to be directly imaged by astronomers. Now, with the help of artificial intelligence (AI) machine learning, it has received its first official makeover -- and the results reveal that rather than being a'fuzzy donut', it is actually more of a'skinny donut'. Scientists say this new perspective of the supermassive black hole will'play a critical role in our ability to understand its behaviour' and could help explain how the stellar phenomenon'eats' matter. They called it a'golden opportunity' to learn more about black hole physics.

Kindly give this article a like or a comment, so I know that you are still reading. I'm a big believer in A.I.'s ability to enable human civilization to become a multi-planetary species. I think artificial intelligence will be critical in enabling us to make this jump in the brief window afforded to us by time and history since the risks of human extinction will become greater in the decades and centuries ahead. I'm always searching and on the hunt for big stories in how A.I. is shaping our understanding of the world and in terms of business innovation. Sometimes however you have to look up.

Compact stellar systems such as Ultra-compact dwarfs (UCDs) and Globular Clusters (GCs) around galaxies are known to be the tracers of the merger events that have been forming these galaxies. Therefore, identifying such systems allows to study galaxies mass assembly, formation and evolution. However, in the lack of spectroscopic information detecting UCDs/GCs using imaging data is very uncertain. Here, we aim to train a machine learning model to separate these objects from the foreground stars and background galaxies using the multi-wavelength imaging data of the Fornax galaxy cluster in 6 filters, namely u, g, r, i, J and Ks. The classes of objects are highly imbalanced which is problematic for many automatic classification techniques. Hence, we employ Synthetic Minority Over-sampling to handle the imbalance of the training data. Then, we compare two classifiers, namely Localized Generalized Matrix Learning Vector Quantization (LGMLVQ) and Random Forest (RF). Both methods are able to identify UCDs/GCs with a precision and a recall of >93 percent and provide relevances that reflect the importance of each feature dimension %(colors and angular sizes) for the classification. Both methods detect angular sizes as important markers for this classification problem. While it is astronomical expectation that color indices of u-i and i-Ks are the most important colors, our analysis shows that colors such as g-r are more informative, potentially because of higher signal-to-noise ratio. Besides the excellent performance the LGMLVQ method allows further interpretability by providing the feature importance for each individual class, class-wise representative samples and the possibility for non-linear visualization of the data as demonstrated in this contribution. We conclude that employing machine learning techniques to identify UCDs/GCs can lead to promising results.

Artificial intelligence (AI) has helped in creating the first complete family tree of Earth's home galaxy – the Milky Way. An international team of researchers, led by astrophysicists Diederik Kruijssen of the University of Heidelberg and Joel Pfeffer of Liverpool John Moores University, published their work in Monthly Notices of the Royal Astronomical Society. The researchers used AI to analyse large groups of stars with as many as million stars, orbiting the Milky Way. "The Milky Way hosts over 150 such clusters, many of which formed in the smaller galaxies that merged to form the galaxy that we live in today," a Royal Astronomical Society (RAS) release noted. With the help of the latest models and observations, the researchers managed to use the clusters as "fossils" to generate the history of galaxies, it added.

The Milky Way has had a long and eventful life. Throughout its history, our galaxy has collided and merged with multiple other galaxies, events that are hard to disentangle and make sense of. With the aid of Artificial Intelligence, a team of astronomers took on this painstaking task, piecing together the most complex history of our galaxy -- and the main attraction is something called The Kraken. Just like geologists look for fossils to see how ancient life might have looked like, astronomers also look for fossils of their own -- but instead of trilobites or dinosaurs, astronomers are preoccupied with very old cosmic structures called globular clusters. Globular clusters are spherical-shaped, densely-packed collections of ancient stars.