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.
Accepted by JCAP
References in corpus (7)
- Active Galactic Nuclei: what's in a name?
- Methods for point source analysis in high energy neutrino telescopes
- IceCube Data for Neutrino Point-Source Searches Years 2008-2018
- Search for multi-flare neutrino emissions in 10 years of IceCube data from a catalog of sources
- A search for time-dependent astrophysical neutrino emission with IceCube data from 2012 to 2017
- Search for Continuous and Transient Neutrino Emission Associated with IceCube's Highest-Energy Tracks: An 11-Year Analysis
- The Spectra of IceCube Neutrino (SIN) candidate sources -- IV. Spectral energy distributions and multi-wavelength variability