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arXiv · 2312.15196

Fast Identification of Transients: Applying Expectation Maximization to Neutrino Data

Martina Karl·Philipp Eller
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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 $10^4$ 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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Martina Karl, Philipp Eller. 2023-12-23. Fast Identification of Transients: Applying Expectation Maximization to Neutrino Data. https://doi.org/10.1088/1475-7516%2F2024%2F07%2F057

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