Galaxy Spectra neural Network (GaSNet). II. Using deep learning for spectral classification and redshift predictions

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dc.contributor.authorZhong, Fucheng
dc.contributor.authorNapolitano, Nicola R.
dc.contributor.authorHeneka, Caroline
dc.contributor.authorLi, Rui
dc.contributor.authorBauer, Franz Erik
dc.contributor.authorBouche, Nicolas
dc.contributor.authorComparat, Johan
dc.contributor.authorKim, Young-Lo
dc.contributor.authorKrogager, Jens-Kristian
dc.contributor.authorLonghetti, Marcella
dc.contributor.authorLoveday, Jonathan
dc.contributor.authorRoukema, Boudewijn F.
dc.contributor.authorRouse, Benedict L.
dc.contributor.authorSalvato, Mara
dc.contributor.authorTortora, Crescenzo
dc.contributor.authorAssef, Roberto J.
dc.contributor.authorCassara, Letizia P.
dc.contributor.authorCostantin, Luca
dc.contributor.authorCroom, Scott M.
dc.contributor.authorDavies, Luke J. M.
dc.contributor.authorFritz, Alexander
dc.contributor.authorGuiglion, Guillaume
dc.contributor.authorHumphrey, Andrew
dc.contributor.authorPompei, Emanuela
dc.contributor.authorRicci, Claudio
dc.contributor.authorSifon, Cristobal
dc.contributor.authorTempel, Elmo
dc.contributor.authorZafar, Tayyaba
dc.date.accessioned2025-01-20T16:14:07Z
dc.date.available2025-01-20T16:14:07Z
dc.date.issued2024
dc.description.abstractThe size and complexity reached by the large sky spectroscopic surveys require efficient, accurate, and flexible automated tools for data analysis and science exploitation. We present the Galaxy Spectra Network/GaSNet-II, a supervised multinetwork 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. Redshift errors are determined via an ensemble/pseudo-Monte Carlo test obtained by randomizing the weights of the network-of-networks structure. As a demonstration of the capability of GaSNet-II, 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 per cent average classification accuracy over the 13 classes and mean redshift errors of approximately 0.23 per cent for galaxies and 2.1 per cent for quasars. We further train/test the pipeline on a sample of 200k 4MOST (4-metre Multi-Object Spectroscopic Telescope) mock spectra and 21k publicly released DESI (Dark Energy Spectroscopic Instrument) spectra. On 4MOST mock data, we reach 93.4 per cent accuracy in 10-class classification and mean redshift error of 0.55 per cent for galaxies and 0.3 per cent for active galactic nuclei. On DESI data, we reach 96 per cent accuracy in (star/galaxy/quasar only) classification and mean redshift error of 2.8 per cent for galaxies and 4.8 per cent for quasars, despite the small sample size available. GaSNet-II can process similar to 40k spectra in less than one minute, on a normal Desktop GPU. This makes the pipeline particularly suitable for real-time analyses and feedback loops for optimization of Stage-IV survey observations.
dc.fuente.origenWOS
dc.identifier.doi10.1093/mnras/stae1461
dc.identifier.eissn1365-2966
dc.identifier.issn0035-8711
dc.identifier.urihttps://doi.org/10.1093/mnras/stae1461
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/90423
dc.identifier.wosidWOS:001258896400005
dc.issue.numero1
dc.language.isoen
dc.pagina.final665
dc.pagina.inicio643
dc.revistaMonthly notices of the royal astronomical society
dc.rightsacceso restringido
dc.subjectmethods: data analysis
dc.subjecttechniques: spectroscopic
dc.subjectsurveys
dc.subjectsoftware: development
dc.subjectgalaxies: distances and redshifts
dc.titleGalaxy Spectra neural Network (GaSNet). II. Using deep learning for spectral classification and redshift predictions
dc.typeartículo
dc.volumen532
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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