Kavli Affiliate: Roberto Maiolino
| First 5 Authors: Michele Ginolfi, Filippo Mannucci, Francesco Belfiore, Alessandro Marconi, Nicholas Boardman
| Summary:
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.
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