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

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.