Speaker
Description
Signal estimation in the presence of background noise is a common problem in many scientific disciplines. An “On/Off” measurement is when the background itself is imprecisely measured, which is the case for instance of observations performed in astronomy. The ‘frequentist’ and Bayesian approaches for signal estimation in “On/Off” measurements are reviewed and compared, focusing on the weakness of the former and on the advantages of the latter in correctly addressing the Poissonian nature of the problem. We propose a new method for estimating the signal rate based on the Bayesian formalism. It uses information on single-event variables 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 data selection cuts. Simulating “On/Off” measurements from imaging atmospheric Cherenkov observations, we conclude that this new method is capable of increasing the resolution of the signal estimation, in particular for background dominated observations.
Keywords
Excess estimation; On/Off measurements; Bayesian; inference; statistic
| Subcategory | Experimental Methods & Instrumentation |
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