Fast Identification of Transients: Applying Expectation Maximization to Neutrino Data
arXiv:2312.15196 · doi:10.1088/1475-7516/2024/07/057
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 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.
arXiv commentAccepted by JCAP
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