ULISSE: Determination of star-formation rate and stellar mass based on the one-shot galaxy imaging technique

Abstract

Modern sky surveys produce vast amounts of observational data, making the application of classical methods for estimating galaxy properties challenging and time-consuming. This challenge can be significantly alleviated by employing automatic machine and deep learning techniques. We propose an implementation of the ULISSE algorithm aimed at determining physical parameters of galaxies, in particular star-formation rates (SFR) and stellar masses ($M_{\ast }$), using only composite-color images. ULISSE is able to rapidly and efficiently identify candidates from a single image based on photometric and morphological similarities to a given reference object with known properties. This approach leverages features extracted from the ImageNet dataset to perform similarity searches among all objects in the sample, eliminating the need for extensive neural network training. Our experiments, performed on the Sloan Digital Sky Survey, demonstrate that we are able to predict the joint star formation rate and stellar mass of the target galaxies within 1 dex in 60% to 80% of cases, depending on the investigated subsample (quiescent/star-forming galaxies, early-/late-type, etc.), and within 0.5 dex if we consider these parameters separately. This is approximately twice the fraction obtained from a random guess extracted from the parent population. Additionally, we find ULISSE is more effective for galaxies with active star formation compared to elliptical galaxies with quenched star formation. Additionally, ULISSE performs more efficiently for galaxies with bright nuclei such as AGN. Our results suggest that ULISSE is a promising tool for a preliminary estimation of star-formation rates and stellar masses for galaxies based only on single images in current and future wide-field surveys (e.g., Euclid, LSST), which target millions of sources nightly.

22 pages, 18 figures, accepted to A&A

References in corpus (19)

• Deep Learning in Neural Networks: An Overview
• Cosmic Star Formation History
• UV Star Formation Rates in the Local Universe
• The Co-Evolution of Galaxies and Supermassive Black Holes: Insights from Surveys of the Contemporary Universe
• The Evolution of Galaxy Structure over Cosmic Time
• Photometric redshift estimation via deep learning
• Surveying the reach and maturity of machine learning and artificial intelligence in astronomy
• The weirdest SDSS galaxies: results from an outlier detection algorithm
• X-rays across the galaxy population I: tracing the main sequence of star formation
• METAPHOR: A machine learning based method for the probability density estimation of photometric redshifts
• Star formation rates and stellar masses from machine learning
• New Approaches To Photometric Redshift Prediction Via Gaussian Process Regression In The Sloan Digital Sky Survey
• Star Formation Rates for photometric samples of galaxies using machine learning methods
• Predicting star formation properties of galaxies using deep learning
• Improving the reliability of photometric redshift with machine learning
• Random Forests as a viable method to select and discover high redshift quasars
• Classification of Planetary Nebulae through Deep Transfer Learning
• Massive young stellar objects in the Local Group irregular galaxy NGC6822 identified using machine learning
• ULISSE: A Tool for One-shot Sky Exploration and its Application to Active Galactic Nuclei Detection

Cited by in corpus (1)

• A Value-added Physical Properties Catalog for Low-redshift Galaxies from DESI Legacy Imaging Surveys DR10