Detecting Stellar Flares in Photometric Data Using Hidden Markov Models
We present a hidden Markov model (HMM) for discovering stellar flares in light-curve data of stars. HMMs provide a framework to model time series data that are nonstationary; they allow for systems to be in different states at different times and consider the probabilities that describe the switchin...
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2025-01-01
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| Online Access: | https://doi.org/10.3847/1538-4357/ad95f6 |
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| author | J. Arturo Esquivel Yunyi Shen Vianey Leos-Barajas Gwendolyn Eadie Joshua S. Speagle Radu V Craiu Amber Medina James R. A. Davenport |
| author_facet | J. Arturo Esquivel Yunyi Shen Vianey Leos-Barajas Gwendolyn Eadie Joshua S. Speagle Radu V Craiu Amber Medina James R. A. Davenport |
| author_sort | J. Arturo Esquivel |
| collection | DOAJ |
| description | We present a hidden Markov model (HMM) for discovering stellar flares in light-curve data of stars. HMMs provide a framework to model time series data that are nonstationary; they allow for systems to be in different states at different times and consider the probabilities that describe the switching dynamics between states. In the context of the discovery of stellar flares, we exploit the HMM framework by allowing the light curve of a star to be in one of three states at any given time step: quiet , firing , or decaying . This three-state HMM formulation is designed to enable straightforward identification of stellar flares, their duration, and associated uncertainty. This is crucial for estimating the flare's energy, and is useful for studies of stellar flare energy distributions. We combine our HMM with a celerite model that accounts for quasiperiodic stellar oscillations. Through an injection recovery experiment, we demonstrate and evaluate the ability of our method to detect and characterize flares in stellar time series. We also show that the proposed HMM flags fainter and lower energy flares more easily than traditional sigma-clipping methods. Lastly, we visually demonstrate that simultaneously conducting detrending and flare detection can mitigate biased estimations arising in multistage modeling approaches. Thus, this method paves a new way to calculate stellar flare energy. We conclude with an example application to one star observed by TESS, showing how the HMM compares with sigma clipping when using real data. |
| format | Article |
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| issn | 1538-4357 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astrophysical Journal |
| spelling | doaj-art-d1cbba436062459b992febc03fa460b02025-08-20T02:09:05ZengIOP PublishingThe Astrophysical Journal1538-43572025-01-01979214110.3847/1538-4357/ad95f6Detecting Stellar Flares in Photometric Data Using Hidden Markov ModelsJ. Arturo Esquivel0https://orcid.org/0009-0006-0387-6544Yunyi Shen1https://orcid.org/0000-0003-2779-6507Vianey Leos-Barajas2https://orcid.org/0000-0001-8016-773XGwendolyn Eadie3https://orcid.org/0000-0003-3734-8177Joshua S. Speagle4https://orcid.org/0000-0003-2573-9832Radu V Craiu5https://orcid.org/0000-0002-1348-8063Amber Medina6https://orcid.org/0000-0001-8726-3134James R. A. Davenport7https://orcid.org/0000-0002-0637-835XDepartment of Statistical Sciences, University of Toronto , Toronto, ON, Canada ; a.esquivel@mail.utoronto.ca; Data Sciences Institute, University of Toronto , Toronto, ON, CanadaLaboratory for Information and Decision Systems, Massachusetts Institute of Technology , Cambridge, MA, USADepartment of Statistical Sciences, University of Toronto , Toronto, ON, Canada ; a.esquivel@mail.utoronto.ca; Data Sciences Institute, University of Toronto , Toronto, ON, Canada; School of the Environment, University of Toronto , Toronto, ON, CanadaDepartment of Statistical Sciences, University of Toronto , Toronto, ON, Canada ; a.esquivel@mail.utoronto.ca; Data Sciences Institute, University of Toronto , Toronto, ON, Canada; David A. Dunlap Department of Astronomy & Astrophysics, University of Toronto , Toronto, ON, CanadaDepartment of Statistical Sciences, University of Toronto , Toronto, ON, Canada ; a.esquivel@mail.utoronto.ca; Data Sciences Institute, University of Toronto , Toronto, ON, Canada; David A. Dunlap Department of Astronomy & Astrophysics, University of Toronto , Toronto, ON, CanadaDepartment of Statistical Sciences, University of Toronto , Toronto, ON, Canada ; a.esquivel@mail.utoronto.ca; Data Sciences Institute, University of Toronto , Toronto, ON, CanadaLas Cruces , NM, USADepartment of Astronomy, University of Washington , Box 351580, Seattle, WA 98195, USAWe present a hidden Markov model (HMM) for discovering stellar flares in light-curve data of stars. HMMs provide a framework to model time series data that are nonstationary; they allow for systems to be in different states at different times and consider the probabilities that describe the switching dynamics between states. In the context of the discovery of stellar flares, we exploit the HMM framework by allowing the light curve of a star to be in one of three states at any given time step: quiet , firing , or decaying . This three-state HMM formulation is designed to enable straightforward identification of stellar flares, their duration, and associated uncertainty. This is crucial for estimating the flare's energy, and is useful for studies of stellar flare energy distributions. We combine our HMM with a celerite model that accounts for quasiperiodic stellar oscillations. Through an injection recovery experiment, we demonstrate and evaluate the ability of our method to detect and characterize flares in stellar time series. We also show that the proposed HMM flags fainter and lower energy flares more easily than traditional sigma-clipping methods. Lastly, we visually demonstrate that simultaneously conducting detrending and flare detection can mitigate biased estimations arising in multistage modeling approaches. Thus, this method paves a new way to calculate stellar flare energy. We conclude with an example application to one star observed by TESS, showing how the HMM compares with sigma clipping when using real data.https://doi.org/10.3847/1538-4357/ad95f6Stellar flaresBayesian statisticsAstrostatistics toolsTime series analysisM dwarf starsStellar activity |
| spellingShingle | J. Arturo Esquivel Yunyi Shen Vianey Leos-Barajas Gwendolyn Eadie Joshua S. Speagle Radu V Craiu Amber Medina James R. A. Davenport Detecting Stellar Flares in Photometric Data Using Hidden Markov Models The Astrophysical Journal Stellar flares Bayesian statistics Astrostatistics tools Time series analysis M dwarf stars Stellar activity |
| title | Detecting Stellar Flares in Photometric Data Using Hidden Markov Models |
| title_full | Detecting Stellar Flares in Photometric Data Using Hidden Markov Models |
| title_fullStr | Detecting Stellar Flares in Photometric Data Using Hidden Markov Models |
| title_full_unstemmed | Detecting Stellar Flares in Photometric Data Using Hidden Markov Models |
| title_short | Detecting Stellar Flares in Photometric Data Using Hidden Markov Models |
| title_sort | detecting stellar flares in photometric data using hidden markov models |
| topic | Stellar flares Bayesian statistics Astrostatistics tools Time series analysis M dwarf stars Stellar activity |
| url | https://doi.org/10.3847/1538-4357/ad95f6 |
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