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

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.

References in corpus (11)

• Cosmic Star Formation History
• UV Star Formation Rates in the Local Universe
• Photometric redshift and classification for the XMM-COSMOS sources
• The Multiwavelength Survey by Yale-Chile (MUSYC): Deep Medium-Band optical imaging and high quality 32-band photometric redshifts in the ECDF-S
• Robust Machine Learning Applied to Astronomical Datasets III: Probabilistic Photometric Redshifts for Galaxies and Quasars in the SDSS and GALEX
• METAPHOR: A machine learning based method for the probability density estimation of photometric redshifts
• A catalogue of photometric redshifts for the SDSS-DR9 galaxies
• Star formation rates and stellar masses from machine learning
• Automated physical classification in the SDSS DR10. A catalogue of candidate Quasars
• C3, A Command-line Catalogue Cross-match tool for large astrophysical catalogues
• Quasi-stellar objects in the ALHAMBRA survey. I. Photometric redshift accuracy based on a 23 optical-NIR filter photometry

Cited by in corpus (8)

• Machine Learning in Astronomy: a practical overview
• A machine learning approach to galaxy properties: joint redshift-stellar mass probability distributions with Random Forest
• Predicting star formation properties of galaxies using deep learning
• Photometric redshifts with machine learning, lights and shadows on a complex data science use case
• The miniJPAS survey quasar selection III: Classification with artificial neural networks and hybridisation
• ULISSE: A Tool for One-shot Sky Exploration and its Application to Active Galactic Nuclei Detection
• Astroinformatics based search for globular clusters in the Fornax Deep Survey
• The metal-poor dwarf irregular galaxy candidate next to Mrk 1172