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Title: Detecting new signals under background mismodelling Authors:  Sara Algeri - University of Minnesota (United States) [presenting]
Abstract: When searching for new astrophysical phenomena, uncertainty arising from background mismodelling can dramatically compromise the sensitivity of the experiment under study. Specifically, overestimating the background distribution in the signal region increases the chance of missing new physics. Conversely, underestimating the background outside the signal region leads to an artificially enhanced sensitivity and a higher likelihood of claiming false discoveries. The aim is to provide a unified statistical algorithm to perform modelling, estimation, inference and signal characterization under background mismodelling. The proposed method allows us to incorporate the (partial) scientific knowledge available on the background distribution, and provides a data-updated version of it in a purely nonparametric fashion, without requiring the specification of prior distributions. If a calibration sample or control regions are available, the solution discussed does not require the specification of a model for the signal; however, if the signal distribution is known, it allows further improvement of the accuracy of the analysis and detection of additional signals of unexpected new sources.