Speaker
Giacomo D\'Amico
(U)
Description
Signal estimation in the presence of background noise is a common problem in several scientific disciplines. An “on/off” measurement is performed when the background itself is not known, being estimated from a background control sample. In this work, we devise a novel reconstruction method, Bayesian analysis including single-event likelihoods (dubbed BASiL), for estimating the signal rate based on the Bayesian formalism. It uses information on event-by-event individual parameters and their distribution for the signal and background population. Events are thereby weighted according to their likelihood of being a signal or a background event and background suppression can be achieved without performing fixed fiducial cuts. We provides a performance test using real data and simulations of observations with the MAGIC telescopes, as a demonstration of the performance for Cherenkov telescopes. BASiL allows one to estimate the signal more precisely, avoiding loss of exposure due to signal extraction cuts.
Primary author
Giacomo D\'Amico
(U)
Co-authors
Ms
Jelena Strišković
(Josip Juraj Strossmayer University of Osijek, Department of Physics)
Juliane van Scherpenberg
(Max Planck Institute for Physic)
Dr
Marcel Strzys
(Institute for Cosmic Ray Research, The University of Tokyo)
Prof.
Michele Doro
(University of Padova and INFN)
Dr
Tomislav Terzić
(University of Rijeka, Department of Physics)
