Model independent search for transient multimessenger events with AMON using outlier detection methods

Abstract

The Astrophysical Multimessenger Observatory Network (AMON) receives subthreshold data from multiple observatories in order to look for coincidences. Combining more than two datasets at the same time is challenging because of the range of possible signals (time windows, energies, number of events...). However, outlier detection methods can circumvent this issue by identifying any signal divergent from the background (e.g. scrambled data). We propose to use these methods to make a model independent combination of the subthreshold data of neutrino and gamma ray experiments. Using the python outlier detection (PyOD) package, it allows us to test several methods from a simple "k-nearest neighbours" algorithm to a more sophisticated Generative Adversarial Active Learning neural networks which generates data points to better discriminate inliers from outliers.

References in corpus (4)

• Gravitational Waves and Gamma-rays from a Binary Neutron Star Merger: GW170817 and GRB 170817A
• Multi-messenger observations of a flaring blazar coincident with high-energy neutrino IceCube-170922A
• Neutrino emission from the direction of the blazar TXS 0506+056 prior to the IceCube-170922A alert
• The Astrophysical Multimessenger Observatory Network (AMON): Performance and Science Program

Cited by in corpus (1)

• 'I think I discovered a military base in the middle of the ocean' -- Null Island, the most real of fictional places