Fast Identification of Transients: Applying Expectation Maximization to Neutrino Data
arxiv(2023)
摘要
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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