Kavli Affiliate: Claudio Ricci
| First 5 Authors: Fucheng Zhong, Nicola R. Napolitano, Caroline Heneka, Rui Li, Franz Erik Bauer
| Summary:
Large sky spectroscopic surveys have reached the scale of photometric surveys
in terms of sample sizes and data complexity. These huge datasets require
efficient, accurate, and flexible automated tools for data analysis and science
exploitation. We present the Galaxy Spectra Network/GaSNet-II, a supervised
multi-network deep learning tool for spectra classification and redshift
prediction. GaSNet-II can be trained to identify a customized number of classes
and optimize the redshift predictions for classified objects in each of them.
It also provides redshift errors, using a network-of-networks that reproduces a
Monte Carlo test on each spectrum, by randomizing their weight initialization.
As a demonstration of the capability of the deep learning pipeline, we use 260k
Sloan Digital Sky Survey spectra from Data Release 16, separated into 13
classes including 140k galactic, and 120k extragalactic objects. GaSNet-II
achieves 92.4% average classification accuracy over the 13 classes (larger than
90% for the majority of them), and an average redshift error of approximately
0.23% for galaxies and 2.1% for quasars. We further train/test the same
pipeline to classify spectra and predict redshifts for a sample of 200k 4MOST
mock spectra and 21k publicly released DESI spectra. On 4MOST mock data, we
reach 93.4% accuracy in 10-class classification and an average redshift error
of 0.55% for galaxies and 0.3% for active galactic nuclei. On DESI data, we
reach 96% accuracy in (star/galaxy/quasar only) classification and an average
redshift error of 2.8% for galaxies and 4.8% for quasars, despite the small
sample size available. GaSNet-II can process ~40k spectra in less than one
minute, on a normal Desktop GPU. This makes the pipeline particularly suitable
for real-time analyses of Stage-IV survey observations and an ideal tool for
feedback loops aimed at night-by-night survey strategy optimization.
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