Galaxy Spectra neural Network (GaSNet). II. Using Deep Learning for Spectral Classification and Redshift Predictions Article Swipe
Fucheng Zhong
,
N. R. Napolitano
,
Caroline Heneka
,
Rui Li
,
F. E. Bauer
,
Johan Comparat
,
Young-Lo Kim
,
J.-K. Krogager
,
M. Longhetti
,
J. Loveday
,
Boudewijn F. Roukema
,
Benedict L. Rouse
,
M. Salvato
,
C. Tortora
,
Roberto J. Assef
,
L. P. Cassarà
,
Luca Costantin
,
S. M. Croom
,
L. J. M. Davies
,
Alexander Fritz
,
N. Bouché
,
G. Guiglion
,
E. Pompei
,
A. Humphrey
,
Cláudio Ricci
,
Cristobál Sifón
,
Elmo Tempel
,
T. Zafar
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2311.04146
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2311.04146
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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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2311.04146
- https://arxiv.org/pdf/2311.04146
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388514899
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4388514899Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2311.04146Digital Object Identifier
- Title
-
Galaxy Spectra neural Network (GaSNet). II. Using Deep Learning for Spectral Classification and Redshift PredictionsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-11-07Full publication date if available
- Authors
-
Fucheng Zhong, N. R. Napolitano, Caroline Heneka, Rui Li, F. E. Bauer, Johan Comparat, Young-Lo Kim, J.-K. Krogager, M. Longhetti, J. Loveday, Boudewijn F. Roukema, Benedict L. Rouse, M. Salvato, C. Tortora, Roberto J. Assef, L. P. Cassarà, Luca Costantin, S. M. Croom, L. J. M. Davies, Alexander Fritz, N. Bouché, G. Guiglion, E. Pompei, A. Humphrey, Cláudio Ricci, Cristobál Sifón, Elmo Tempel, T. ZafarList of authors in order
- Landing page
-
https://arxiv.org/abs/2311.04146Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2311.04146Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2311.04146Direct OA link when available
- Concepts
-
Redshift, Galaxy, Astrophysics, Physics, Sky, Active galactic nucleus, Quasar, Spectral line, Pipeline (software), Artificial neural network, Artificial intelligence, Computer science, Astronomy, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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