engleski

Signal estimation in on/off measurements including event-by-event variables

Signal estimation in the presence of background noise is a common problem in several scientificdisciplines. An “On/Off” measurement is performed when the background itself is not known, beingestimated from a background control sample. The “frequentist” and Bayesian approaches for signalestimation in On/Off measurements are reviewed and compared, focusing on the weakness of theformer and on the advantages of the latter in correctly addressing the Poissonian nature of theproblem. In this work, we devise a novel reconstruction method, dubbed BASiL (Bayesian Analysisincluding Single-event Likelihoods), for estimating the signal rate based on the Bayesian formalism.It uses information on event-by-event individual parameters and their distribution for the signaland background population. Events are thereby weighted according to their likelihood of beinga signal or a background event and background suppression can be achieved without performingfixed fiducial cuts. Throughout the work, we maintain a general notation, that allows to apply themethod generically, and provide a performance test using real data and simulations of observationswith the MAGIC telescopes, as demonstration of the performance for Cherenkov telescopes. BASiLallows to estimate the signal more precisely, avoiding loss of exposure due to signal extraction cuts.We expect its applicability to be straightforward in similar cases.

Gamma-ray astronomy, Bayesian methods, Cherenkov detectors, Combinatorics, Data analysis, Statistical methods, Telescopes

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