Inferring redshift and galaxy properties via a multi-task neural net with probabilistic outputs: An application to simulated MOONS spectra

TL;DR

This paper presents M-TOPnet, a multi-task neural network that simultaneously infers galaxy redshift and physical properties from spectra, using probabilistic outputs for robust and efficient analysis of large astronomical datasets.

Contribution

Introduces M-TOPnet, a convolutional neural network with multi-task learning and probabilistic redshift outputs, tailored for analyzing simulated galaxy spectra from upcoming large surveys.

Findings

Achieves up to 99% accuracy in redshift determination within |Δz|<0.01

Effectively predicts stellar mass and star formation rate at high redshift

Enables robust quality screening through probability distribution analysis

Abstract

The era of large-scale astronomical surveys demands innovative approaches for rapid and accurate analysis of extensive spectral data, and a promising direction in which to address this challenge is offered by machine learning. Here, we introduce a new pipeline, M-TOPnet (Multi-Task network Outputting Probabilities), which employs a convolutional neural network with residual learning to simultaneously derive redshift and other key physical properties of galaxies from their spectra. Our tool efficiently encodes spectral information into a latent space, employing distinct downstream branches for each physical quantity, thereby benefiting from multi-task learning. Notably, our method handles the redshift output as a probability distribution, allowing for a more refined and robust estimation of this critical parameter. We demonstrate preliminary results using simulated data from the MOONS instrument, which will soon be operating at the ESO/VLT. We highlight the effectiveness of our tool in accurately predicting the redshift, stellar mass, and star formation rate of galaxies at z>~1-3, even for faint sources (m_H ~ 24) for which traditional methods often struggle. Through analysis of the output probability distributions, we demonstrate that our pipeline enables robust quality screening of the results, achieving accuracy rates of up to 99% in redshift determination (defined as predictions within |Delta_z| < 0.01 relative to the true redshift) with 8h exposure spectra, while automatically identifying potentially problematic cases. Our pipeline thus emerges as a powerful solution for the upcoming challenges in observational astronomy, combining precision, interpretability, and efficiency, all aspects that are crucial for analysing the massive datasets expected from next-generation instruments.