# Detecting new signals under background mismodelling

## TL;DR

This paper introduces a unified statistical approach to detect new signals in astrophysics experiments, effectively handling background mismodelling uncertainties to improve sensitivity and reduce false discoveries.

## Contribution

It presents a nonparametric method that incorporates partial scientific knowledge and updates background models without relying on prior distributions.

## Key findings

- Method improves detection sensitivity under background uncertainties
- Application to dark matter searches demonstrates robustness
- Handles violations of classical distributional assumptions

## Abstract

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 on the parameters. 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.