Early Detection of Multiwavelength Blazar Variability
Blazars are a subclass of active galactic nuclei with relativistic jets pointing toward the observer. They are notable for their flux variability at all observed wavelengths and timescales. Together with simultaneous measurements at lower energies, the very-high-energy (VHE) emission observed during...
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IOP Publishing
2025-01-01
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| Series: | The Astrophysical Journal |
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| Online Access: | https://doi.org/10.3847/1538-4357/ad960c |
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| author | Hermann Stolte Jonas Sinapius Iftach Sadeh Elisa Pueschel Matthias Weidlich David Berge |
| author_facet | Hermann Stolte Jonas Sinapius Iftach Sadeh Elisa Pueschel Matthias Weidlich David Berge |
| author_sort | Hermann Stolte |
| collection | DOAJ |
| description | Blazars are a subclass of active galactic nuclei with relativistic jets pointing toward the observer. They are notable for their flux variability at all observed wavelengths and timescales. Together with simultaneous measurements at lower energies, the very-high-energy (VHE) emission observed during blazar flares may be used to probe the population of accelerated particles. However, optimally triggering observations of blazar high states can be challenging. Notable examples include identifying a flaring episode in real time and predicting VHE flaring activity based on lower-energy observables. For this purpose, we have developed a novel deep learning analysis framework, based on data-driven anomaly detection techniques. It is capable of detecting various types of anomalies in real-world, multiwavelength light curves, ranging from clear high states to subtle correlations across bands. Based on unsupervised anomaly detection and clustering methods, we differentiate source variability from noisy background activity, without the need for a labeled training data set of flaring states. The framework incorporates measurement uncertainties and is robust given data quality challenges, such as varying cadences and observational gaps. We evaluate our approach using both historical data and simulations of blazar light curves in two energy bands, corresponding to sources observable with the Fermi Large Area Telescope and the upcoming Cherenkov Telescope Array Observatory. In a statistical analysis, we show that our framework can reliably detect known historical flares. |
| format | Article |
| id | doaj-art-ec9eef427d344a6390051dcafe9e7a64 |
| institution | Kabale University |
| issn | 1538-4357 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astrophysical Journal |
| spelling | doaj-art-ec9eef427d344a6390051dcafe9e7a642025-02-07T10:13:03ZengIOP PublishingThe Astrophysical Journal1538-43572025-01-01980114110.3847/1538-4357/ad960cEarly Detection of Multiwavelength Blazar VariabilityHermann Stolte0https://orcid.org/0000-0003-0047-5842Jonas Sinapius1https://orcid.org/0009-0004-8608-0853Iftach Sadeh2https://orcid.org/0000-0003-1387-8915Elisa Pueschel3https://orcid.org/0000-0002-0529-1973Matthias Weidlich4https://orcid.org/0000-0003-3325-7227David Berge5https://orcid.org/0000-0002-2918-1824Humboldt Universität zu Berlin , Unter den Linden 6, 10117 Berlin, Germany ; [email protected] Elektronen-Synchrotron DESY , Platanenallee 6, 15738 Zeuthen, Germany ; [email protected] Elektronen-Synchrotron DESY , Platanenallee 6, 15738 Zeuthen, Germany ; [email protected] Elektronen-Synchrotron DESY , Platanenallee 6, 15738 Zeuthen, Germany ; [email protected]; Ruhr-Universität Bochum , D-44780 Bochum, GermanyHumboldt Universität zu Berlin , Unter den Linden 6, 10117 Berlin, Germany ; [email protected] Universität zu Berlin , Unter den Linden 6, 10117 Berlin, Germany ; [email protected]; Deutsches Elektronen-Synchrotron DESY , Platanenallee 6, 15738 Zeuthen, Germany ; [email protected] are a subclass of active galactic nuclei with relativistic jets pointing toward the observer. They are notable for their flux variability at all observed wavelengths and timescales. Together with simultaneous measurements at lower energies, the very-high-energy (VHE) emission observed during blazar flares may be used to probe the population of accelerated particles. However, optimally triggering observations of blazar high states can be challenging. Notable examples include identifying a flaring episode in real time and predicting VHE flaring activity based on lower-energy observables. For this purpose, we have developed a novel deep learning analysis framework, based on data-driven anomaly detection techniques. It is capable of detecting various types of anomalies in real-world, multiwavelength light curves, ranging from clear high states to subtle correlations across bands. Based on unsupervised anomaly detection and clustering methods, we differentiate source variability from noisy background activity, without the need for a labeled training data set of flaring states. The framework incorporates measurement uncertainties and is robust given data quality challenges, such as varying cadences and observational gaps. We evaluate our approach using both historical data and simulations of blazar light curves in two energy bands, corresponding to sources observable with the Fermi Large Area Telescope and the upcoming Cherenkov Telescope Array Observatory. In a statistical analysis, we show that our framework can reliably detect known historical flares.https://doi.org/10.3847/1538-4357/ad960cBlazarsTransient detectionOutlier detectionNeural networksGamma-ray astronomyAstronomical methods |
| spellingShingle | Hermann Stolte Jonas Sinapius Iftach Sadeh Elisa Pueschel Matthias Weidlich David Berge Early Detection of Multiwavelength Blazar Variability The Astrophysical Journal Blazars Transient detection Outlier detection Neural networks Gamma-ray astronomy Astronomical methods |
| title | Early Detection of Multiwavelength Blazar Variability |
| title_full | Early Detection of Multiwavelength Blazar Variability |
| title_fullStr | Early Detection of Multiwavelength Blazar Variability |
| title_full_unstemmed | Early Detection of Multiwavelength Blazar Variability |
| title_short | Early Detection of Multiwavelength Blazar Variability |
| title_sort | early detection of multiwavelength blazar variability |
| topic | Blazars Transient detection Outlier detection Neural networks Gamma-ray astronomy Astronomical methods |
| url | https://doi.org/10.3847/1538-4357/ad960c |
| work_keys_str_mv | AT hermannstolte earlydetectionofmultiwavelengthblazarvariability AT jonassinapius earlydetectionofmultiwavelengthblazarvariability AT iftachsadeh earlydetectionofmultiwavelengthblazarvariability AT elisapueschel earlydetectionofmultiwavelengthblazarvariability AT matthiasweidlich earlydetectionofmultiwavelengthblazarvariability AT davidberge earlydetectionofmultiwavelengthblazarvariability |