Abstract
Abstract
We present a novel method for identifying transients suitable for both strong signal-dominated and background-dominated objects. By employing the unsupervised machine learning algorithm known as expectation maximization, we achieve computing time reductions of over 104 on a single CPU compared to conventional brute-force methods. Furthermore, this approach can be readily extended to analyze multiple flares. We illustrate the algorithm's application by fitting the IceCube neutrino flare of TXS 0506+056.
Reference20 articles.
1. Active galactic nuclei: what’s in a name?;Padovani;Astron. Astrophys. Rev.,2017
2. Gamma-ray bursts;Higdon;Ann. Rev. Astron. Astrophys.,1990
3. Studies in Astronomical Time Series Analysis. VI. Bayesian Block Representations;Scargle;Astrophys. J.,2013
4. The spectra of IceCube Neutrino (SIN) candidate sources – IV. Spectral energy distributions and multiwavelength variability;Karl;Mon. Not. Roy. Astron. Soc.,2023
5. Multimessenger observations of a flaring blazar coincident with high-energy neutrino IceCube-170922A;IceCube, Fermi-LAT, MAGIC, AGILE, ASAS-SN, HAWC, H.E.S.S., INTEGRAL, Kanata, Kiso, Kapteyn, Liverpool Telescope, Subaru, Swift NuSTAR, VERITAS, VLA/17B-403 Collaboration;Science,2018