Detecting new signals under background mismodelling. (arXiv:1906.06615v1 [physics.data-an])

Detecting new signals under background mismodelling. (arXiv:1906.06615v1 [physics.data-an])
<a href="http://arxiv.org/find/physics/1/au:+Algeri_S/0/1/0/all/0/1">Sara Algeri</a>

Searches for new astrophysical phenomena often involve several sources of
non-random uncertainties which can lead to highly misleading results. Among
these, model-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
chances 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 of this work is to
provide a unified statistical strategy to perform modelling, estimation,
inference, and signal characterization under background mismodelling. The
method proposed allows 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. Applications in the context of dark matter searches and
radio surveys show how the tools presented in this article can be used to
incorporate non-stochastic uncertainty due to instrumental noise and to
overcome violations of classical distributional assumptions in stacking
experiments.

Searches for new astrophysical phenomena often involve several sources of
non-random uncertainties which can lead to highly misleading results. Among
these, model-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
chances 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 of this work is to
provide a unified statistical strategy to perform modelling, estimation,
inference, and signal characterization under background mismodelling. The
method proposed allows 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. Applications in the context of dark matter searches and
radio surveys show how the tools presented in this article can be used to
incorporate non-stochastic uncertainty due to instrumental noise and to
overcome violations of classical distributional assumptions in stacking
experiments.

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