The transition from anaerobic to aerobic life was a pivotal adaptation in Earth’s history, yet the timing and genomic drivers remain poorly resolved.
![Probability distribution functions (PDFs) for the chemical abundances used to sample the stellar properties in our dataset,with lighter colors indicating higher probability densities. Left panel: PDF of [C/Fe] versus [Fe/H]. Right panel: PDF of [α/Fe] versus [Fe/H]. In both panels, the pink contours show the abundance distributions of halo stars from the APOGEE survey, as provided by the astroNN catalog. APOGEE does not reach the lowest metallicities present in our simulated dataset, which explains the differences at low [Fe/H]. -- astro-ph.SR](https://i0.wp.com/astrobiology.com/wp-content/uploads/2026/01/Towards-Model-free.png?resize=1200%2C723&ssl=1)
Chemical abundance determinations from stellar spectra are challenged by observational noise, limitations in stellar models, and departures from simplifying assumptions.
This study explores the application of autoencoder-based machine learning techniques for anomaly detection to identify exoplanet atmospheres with unconventional chemical signatures using a low-dimensional data representation.
The search for extraterrestrial intelligence (SETI) commensal surveys aim to scan the sky to detect technosignatures from extraterrestrial life.
The high-precision photometry from NASA’s Kepler and TESS missions has revolutionized exoplanet detection, enabling the discovery of over 5500 confirmed exoplanets via the transit method and around 10000 additional candidates […]
A machine learning framework can distinguish molecules made by biological processes from those formed through non-biological processes and could be used to analyze samples returned by current and future planetary […]
Since the first observations of interstellar molecules, astrochemical simulations have been employed to model and understand its formation and destruction pathways.
Radiative transfer calculations are essential for modeling planetary atmospheres. However, standard methods are computationally demanding and impose accuracy-speed trade-offs. High computational costs force numerical simplifications in large models (e.g., General […]
In the last few years, several approaches have used machine learning to reproduce the inventory and column densities of interstellar molecular sources.
Advances in machine learning over the past decade have resulted in a proliferation of algorithmic applications for encoding, characterizing, and acting on complex data that may contain many high dimensional […]
