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@ARTICLE{Stolte:612335,
      author       = {Stolte, Hermann and Sadeh, Iftach and Pueschel, Elisa and
                      Weidlich, Matthias and Sinapius, Jonas and Berge, David},
      title        = {{E}arly {D}etection of {M}ultiwavelength {B}lazar
                      {V}ariability},
      journal      = {The astrophysical journal / Part 1},
      volume       = {980},
      number       = {1},
      issn         = {0004-637X},
      address      = {London},
      publisher    = {Institute of Physics Publ.},
      reportid     = {PUBDB-2024-05295},
      pages        = {141},
      year         = {2025},
      abstract     = {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.},
      cin          = {$Z_GA$},
      ddc          = {520},
      cid          = {$I:(DE-H253)Z_GA-20210408$},
      pnm          = {613 - Matter and Radiation from the Universe (POF4-613)},
      pid          = {G:(DE-HGF)POF4-613},
      experiment   = {EXP:(DE-H253)VERITAS-20170101 /
                      EXP:(DE-H253)Fermi-20170101},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001415702300001},
      doi          = {10.3847/1538-4357/ad960c},
      url          = {https://bib-pubdb1.desy.de/record/612335},
}