Fast Identification of Transients: Applying Expectation Maximization to Neutrino Data
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astro-ph.IM
astro-ph.HEphysics.comp-phphysics.data-an
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algorithmexpectationmaximizationneutrinotransientsachieveanalyzeapplication
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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 $10^4$ 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.
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