Fast Identification of Transients: Applying Expectation Maximization to Neutrino Data
Instrumentation and Methods for Astrophysics2024-07-25v3High Energy Astrophysical PhenomenaComputational PhysicsData Analysis, Statistics and Probability
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.
@article{arxiv.2312.15196,
title = {Fast Identification of Transients: Applying Expectation Maximization to Neutrino Data},
author = {Martina Karl and Philipp Eller},
journal= {arXiv preprint arXiv:2312.15196},
year = {2024}
}