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Assessing the Quality of Massive Spectroscopic Surveys with Unsupervised Machine Learning

Published online by Cambridge University Press:  01 August 2025

John F. Suárez-Pérez Open the ORCID record for John F. Suárez-Pérez [Opens in a new window]  and
Jaime Forero-Romero
John F. Suárez-Pérez*
Affiliation:
Departamento de Física, Universidad de los Andes, Cra. 1 No. 18A-10, Bogotá, Colombia
Jaime Forero-Romero*
Affiliation:
Departamento de Física, Universidad de los Andes, Cra. 1 No. 18A-10, Bogotá, Colombia
*
emails: jf.suarez@uniandes.edu.co, je.forero@uniandes.edu.co
emails: jf.suarez@uniandes.edu.co, je.forero@uniandes.edu.co
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Abstract

Massive spectroscopic surveys targeting tens of millions of galaxies are starting to dominate the observational landscape in the 2020 decade. For instance, a night of observation with the Dark Energy Spectroscopic Instrument (DESI) can measure around of 100,000 spectra, with each spectrum sampled over 2,000 wavelength points. Assessing the quality of such a massive data flow requires new approaches to complement visual inspection by humans. In this work, we explore the Uniform Manifold Approximation and Projection (UMAP) as a technique to assess the data quality of DESI. We use UMAP to project DESI data into a 2-dimensional space. In this space, we are able to find outliers that correspond to instrument fluctuations that can be fully diagnosed by inspecting the raw data. These results pave the way for to use machine learning to monitor the health of massive spectroscopic surveys automatically.

Keywords

methods: data analysistechniques: miscellaneoussurveys

Information

Type
Contributed Paper
Information
Proceedings of the International Astronomical Union , Volume 19 , Symposium S368: Machine Learning in Astronomy: Possibilities and Pitfalls , August 2023 , pp. 91 - 94
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Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of International Astronomical Union

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References

Baron, D., Poznanski, D. 2017. The weirdest SDSS galaxies: results from an outlier detection algorithm. MNRAS 465, 4530–4555. doi: 10.1093/mnras/stw3021 Google Scholar
Collaboration, DESI, Aghamousa, A., Aguilar, J., et al. 2016, The DESI Experiment Part I: Science,Targeting, and Survey Design, arXiv:1611.00036Google Scholar
Huchra, J.P. & Geller, M. J. 1982, Groups of Galaxies. I. Nearby groups, ApJ, 257, 423. doi: 10.1086/160000 CrossRefGoogle Scholar
McInnes, L., Healy, J., & Melville, J. 2018, UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv:1802.03426Google Scholar
Way, M. J., Scargle, J. D., Ali, K. M., Srivastava, A. N. 2012. Advances in Machine Learning and Data Mining for Astronomy.Google Scholar