# Star Formation Rates for photometric samples of galaxies using machine learning methods

## TL;DR

This paper demonstrates that machine learning models can accurately estimate galaxy star formation rates using photometric data, providing a large catalog of SFRs for over 27 million galaxies from SDSS DR7.

## Contribution

The study introduces a machine learning-based method for estimating galaxy SFRs from photometry, reducing reliance on spectroscopic data and providing a comprehensive SFR catalog.

## Key findings

- Photometric SFR estimates are reliable when using multi-band data.
- Photometric redshifts impact SFR accuracy but still produce useful estimates.
- A catalog of SFRs for over 27 million galaxies is publicly available.

## Abstract

Star Formation Rates or SFRs are crucial to constrain theories of galaxy formation and evolution. SFRs are usually estimated via spectroscopic observations requiring large amounts of telescope time. We explore an alternative approach based on the photometric estimation of global SFRs for large samples of galaxies, by using methods such as automatic parameter space optimisation, and supervised Machine Learning models. We demonstrate that, with such approach, accurate multi-band photometry allows to estimate reliable SFRs. We also investigate how the use of photometric rather than spectroscopic redshifts, affects the accuracy of derived global SFRs. Finally, we provide a publicly available catalogue of SFRs for more than 27 million galaxies extracted from the Sloan Digital Sky survey Data Release 7. The catalogue is available through the Vizier facility at the following link ftp://cdsarc.u-strasbg.fr/pub/cats/J/MNRAS/486/1377.