Classifying white dwarfs from multi-object spectroscopy surveys with machine learning
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866915775170740224 |
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| author | Munday, James Tremblay, Pier-Emmanuel Pelisoli, Ingrid Killestein, Thomas Martikainen, Julia Jones, David Bédard, Antoine Sowicka, Paulina |
| author_facet | Munday, James Tremblay, Pier-Emmanuel Pelisoli, Ingrid Killestein, Thomas Martikainen, Julia Jones, David Bédard, Antoine Sowicka, Paulina |
| contents | With tens to hundreds of spectra of white dwarfs being taken each night from multi-object spectroscopic surveys, automated spectral classification is essential as part of efficient data processing. In this study, we design a neural network to classify the spectral type of white dwarfs using a combination of spectra from the Dark Energy Spectroscopic Instrument (DESI) data release~1 and imaging from Pan-STARRS photometry. The trained network has a near 100% accuracy at identifying DA and DB white dwarf spectral types, while having an 85-95% accuracy for identifying all other primary types, including metal pollution. Distinct spectral or photometric features map into separate structures when performing a Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction. Investigating further and looking at multiple epoch spectra, we performed a separate search for objects that have strongly changing spectral signatures using UMAP, discovering 3 new inhomogeneous surface composition ('double-faced') white dwarfs in the process. We lastly show how machine learning has the potential to separate single white dwarfs from double white dwarf binary star systems in a large dataset, ideal for isolating a single star population. The results from all of these techniques show a compelling use of machine learning to boost efficiency in analysing white dwarfs observed in multi-object spectroscopy surveys, at times replacing the need for human-driven spectral classifications. This demonstrates our techniques as powerful tools for batch population analyses, finding outliers as a form of rare subclass detection, and in conducting multi-epoch spectral analyses. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2602_04964 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Classifying white dwarfs from multi-object spectroscopy surveys with machine learning Munday, James Tremblay, Pier-Emmanuel Pelisoli, Ingrid Killestein, Thomas Martikainen, Julia Jones, David Bédard, Antoine Sowicka, Paulina Solar and Stellar Astrophysics Astrophysics of Galaxies Instrumentation and Methods for Astrophysics With tens to hundreds of spectra of white dwarfs being taken each night from multi-object spectroscopic surveys, automated spectral classification is essential as part of efficient data processing. In this study, we design a neural network to classify the spectral type of white dwarfs using a combination of spectra from the Dark Energy Spectroscopic Instrument (DESI) data release~1 and imaging from Pan-STARRS photometry. The trained network has a near 100% accuracy at identifying DA and DB white dwarf spectral types, while having an 85-95% accuracy for identifying all other primary types, including metal pollution. Distinct spectral or photometric features map into separate structures when performing a Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction. Investigating further and looking at multiple epoch spectra, we performed a separate search for objects that have strongly changing spectral signatures using UMAP, discovering 3 new inhomogeneous surface composition ('double-faced') white dwarfs in the process. We lastly show how machine learning has the potential to separate single white dwarfs from double white dwarf binary star systems in a large dataset, ideal for isolating a single star population. The results from all of these techniques show a compelling use of machine learning to boost efficiency in analysing white dwarfs observed in multi-object spectroscopy surveys, at times replacing the need for human-driven spectral classifications. This demonstrates our techniques as powerful tools for batch population analyses, finding outliers as a form of rare subclass detection, and in conducting multi-epoch spectral analyses. |
| title | Classifying white dwarfs from multi-object spectroscopy surveys with machine learning |
| topic | Solar and Stellar Astrophysics Astrophysics of Galaxies Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2602.04964 |