Automated quasar continuum estimation using neural networks

Context . Ongoing and upcoming large spectroscopic surveys are drastically increasing the number of observed quasar spectra, making the development of fast and accurate automated methods to estimate spectral continua necessary. Aims . This study evaluates the performance of three neural networks (NNs) – an autoencoder, a convolutional NN (CNN), and a U-Net – in predicting quasar continua within the rest frame wavelength range of 1020 Å to 2000 Å. The ability to generalize and predict galaxy continua within the range of 3500 Å to 5500 Å is also tested. Methods . We evaluated the performance of these architectures using the absolute fractional flux error (AFFE) on a library of mock quasar spectra for the WEAVE survey and on real data from the early data release observations of the Dark Energy Spectroscopic Instrument (DESI) and the VIMOS Public Extragalactic Redshift Survey (VIPERS). Results . The autoencoder outperforms U-Net, achieving a median AFFE of 0.009 for quasars. The best model also effectively recovers the Lyα optical depth evolution in the DESI quasar spectra. With minimal optimization, the same architectures can be generalized to the galaxy case, with the autoencoder reaching a median AFFE of 0.014 and reproducing the D4000n break in DESI and VIPERS galaxies.

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Publication Details

Journal
Astronomy and Astrophysics
Published
2025-05-27
DOI
https://doi.org/10.1051/0004-6361/202453377
Primary Topic
Seismic Imaging and Inversion Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

Automated quasar continuum estimation using neural networks

Rajeshwari Dutta, Sean Morrison, F. Pistis, Michele Fumagalli et al.
Astronomy and Astrophysics
Seismic Imaging and Inversion Techniques
article

Automated quasar continuum estimation using neural networks

Rajeshwari Dutta, Sean Morrison, F. Pistis, Michele Fumagalli, Margherita Grespan, Matthew M. Pieri, A. Pollo, K. Małek, D. Vergani, A. Iovino, Matteo Fossati, Ignasi Pérez-Ràfols, Trystyn A. M. Berg, William Pearson, D. N. A. Murphy, Elena S. Mangola
article en

Abstract

Context . Ongoing and upcoming large spectroscopic surveys are drastically increasing the number of observed quasar spectra, making the development of fast and accurate automated methods to estimate spectral continua necessary. Aims . This study evaluates the performance of three neural networks (NNs) – an autoencoder, a convolutional NN (CNN), and a U-Net – in predicting quasar continua within the rest frame wavelength range of 1020 Å to 2000 Å. The ability to generalize and predict galaxy continua within the range of 3500 Å to 5500 Å is also tested. Methods . We evaluated the performance of these architectures using the absolute fractional flux error (AFFE) on a library of mock quasar spectra for the WEAVE survey and on real data from the early data release observations of the Dark Energy Spectroscopic Instrument (DESI) and the VIMOS Public Extragalactic Redshift Survey (VIPERS). Results . The autoencoder outperforms U-Net, achieving a median AFFE of 0.009 for quasars. The best model also effectively recovers the Lyα optical depth evolution in the DESI quasar spectra. With minimal optimization, the same architectures can be generalized to the galaxy case, with the autoencoder reaching a median AFFE of 0.014 and reproducing the D4000n break in DESI and VIPERS galaxies.

Astronomy and AstrophysicsVol. 698
Herzberg Institute of Astrophysics (CA), Jagiellonian University (PL), Camosun College (CA), Centre National de la Recherche Scientifique (FR), University of Illinois Urbana-Champaign (US), Pontificia Universidad Católica de Chile (CL), Aix-Marseille Université (FR), University of Cambridge (GB), Trieste Astronomical Observatory (IT), Brera Astronomical Observatory (IT), National Centre for Nuclear Research (PL), Osservatorio di Astrofisica e Scienza dello Spazio, Inter-University Centre for Astronomy and Astrophysics (IN), University of Milano-Bicocca (IT), Universitat Politècnica de Catalunya (ES)
Narodowe Centrum Nauki
Openalex Percentile: Top 86%
Seismic Imaging and Inversion Techniques
DOIOpen Access PDFOpenAlex
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