Can experiments with unknown backgrounds still produce robust constraints on new physics? The answer is yes -- with appropriate statistical techniques. I will review existing approaches, then introduce the 'deficit hawk' method, which allows analysts to leverage partial or speculative background knowledge. Deficit hawks simplify decisions on fiducial volumes and energy thresholds, are well-suited to analyses that use machine learning or multidimensional likelihoods, and permit discoveries in regions without unknown backgrounds.
Join from PC, Mac, Linux, iOS or Android: https://stanford.zoom.us/j/98973156241?pwd=cEU5RFdlVXoyc0JTeTlDMkozKzQ5UT09
Federico Bianchini, Sander Breur
(fbianc@slac, sanderb@slac)
