A search for dark matter among Fermi-LAT unidentified sources with systematic features in machine learning
Entity
UAM. Departamento de Física TeóricaPublisher
Oxford University PressDate
2023-03-01Citation
10.1093/mnras/stad066
Monthly Notices of the Royal Astronomical Society 520.1 (2023): 1348-1361
ISSN
0035-8711DOI
10.1093/mnras/stad066Funded by
The work of VG and MASC was supported by the grants PROYECTOS DE I+D DE «GENERACIÓN DE CONOCIMIENTO PGC2018-095161-B-I00, CENTRO DE EXCELENCIA “SEVERO OCHOA” CEX2020-001007-S, Proyectos I+D+i PID2021-125331NB-I00 all funded by Ministerio de Ciencia e Innovación MCIN/AEI/10.13039/501100011033 and by ‘European Regional Development Fund (ERDF) A way of making Europe’. VG’s contribution to this work has been supported by Juan de la Cierva-Formación FJCI-2016-29213 and Juan de la Cierva-Incorporación IJC2019-040315-I grants. BZ has been further supported by the Programa Atracción de Talento de la Comunidad de Madrid under grant no. 2017-T2/TIC-5455, from the Comunidad de Madrid/Universidad Autonoma Madrid ‘Proyecto de Jóvenes Investigadores’ grant no. SI1/PJI/2019-00294, from Spanish ‘Proyectos de I + D de Generacion de Conocimiento’ via grant PGC2018-096646-A-I00. BZ also acknowledges the support from Generalitat Valenciana through the plan GenT program (CIDEGENT/2020/055). The work of MASC and JCB was also supported by the Atracción de Talento contracts no. 2016-T1/TIC-1542 and 2020-5A/TIC-19725 granted by the Comunidad de Madrid in SpainProject
Gobierno de España. PGC2018-095161-B-I00; Gobierno de España. CEX2020-001007-S; Gobierno de España. PID2021-125331NB-I00Editor's Version
https://doi.org/10.1093/mnras/stad066Subjects
Astroparticle physics; methods: data analysis; methods: observational; methods: statistical; dark matter; gamma-rays: general; Astronomía; FísicaRights
© 2023 The Author(s)Abstract
Around one-third of the point-like sources in the Fermi-LAT catalogues remain as unidentified sources (unIDs) today. Indeed, these unIDs lack a clear, univocal association with a known astrophysical source. If dark matter (DM) is composed of weakly interacting massive particles (WIMPs), there is the exciting possibility that some of these unIDs may actually be DM sources, emitting gamma-rays from WIMPs annihilation. We propose a new approach to solve the standard, machine learning (ML) binary classification problem of disentangling prospective DM sources (simulated data) from astrophysical sources (observed data) among the unIDs of the 4FGL Fermi-LAT catalogue. We artificially build two systematic features for the DM data which are originally inherent to observed data: the detection significance and the uncertainty on the spectral curvature. We do it by
sampling from the observed population of unIDs, assuming that the DM distributions would, if any, follow the latter. We consider different ML models: Logistic Regression, Neural Network (NN), Naive Bayes, and Gaussian Process, out of which the best,
in terms of classification accuracy, is the NN, achieving around 93.3 per cent ± 0.7 per cent performance. Other ML evaluation parameters, such as the True Negative and True Positive rates, are discussed in our work. Applying the NN to the unIDs sample, we find that the degeneracy between some astrophysical and DM sources can be partially solved within this methodology. None the less, we conclude that there are no DM source candidates among the pool of 4FGL Fermi-LAT unIDs
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Google Scholar:Gammaldi, Viviana
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Zaldívar, B.
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Sánchez Conde, Miguel Ángel
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Coronado-Blázquez, Javier
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Coronado, B.
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