Campuses:

Handling model uncertainties via informative Goodness-of-Fit

Tuesday, September 21, 2021 - 1:25pm - 2:25pm
Walter Library 402

Sara Algeri (University of Minnesota, Twin Cities)

You may attend the talk either in person in Walter 402 or registering via Zoom.

Registration is required to access the Zoom webinar.

When searching for signals of new astrophysical phenomena, astrophysicists have to account for several sources of non-random uncertainties which can dramatically compromise the sensitivity of the experiment under study. Among these, model uncertainty arising from background mismodeling is particularly dangerous and can easily lead to highly misleading results. Specifically, overestimating the background distribution in the signal region increases the chances of falsely rejecting the hypothesis that the new source is present. Conversely, underestimating the background outside the signal region leads to an artificially enhanced sensitivity and a higher likelihood of claiming a false discovery. The aim of this work is to provide a self-contained framework to perform modeling, estimation, and inference under background mismodeling. The method proposed allows incorporating the (partial) scientific knowledge available on the background distribution, and provides a data-updated version of it in a purely nonparametric fashion, and thus, without requiring the specification of prior distributions. If a calibration (or control regions) is available, the solution discussed does not require the specification of a model for the signal, however when available, it allows to further improve the accuracy of the analysis and to detect additional and unexpected signal sources.

I have been an Assistant Professor in the School of Statistics at the University of Minnesota since August 2018. My appointment at UMN started soon after completing my doctoral studies in statistics at Imperial College London (UK). My research interests mainly lie in astrostatistics, computational statistics, and statistical inference. The main purpose of my work is to provide generalizable statistical solutions which directly address fundamental scientific questions, and can at the same time be easily applied to any other scientific problem following a similar statistical paradigm. In line with this, motivated by the problem of the detection of particle dark matter, my current research focuses on statistical inference for signal detection under lack of regularity. I am also interested in uncertainty quantification in the context of astrophysical discoveries.