Author:
Yallup David,Handley Will
Abstract
Abstract
Data driven modelling is vital to many analyses at collider
experiments, however the derived inference of physical properties
becomes subject to details of the model fitting procedure. This work
brings a principled Bayesian picture — based on the marginal
likelihood — of both data modelling and signal extraction to a
common collider physics scenario. First the marginal likelihood
based method is used to propose a more principled construction of
the background process, systematically exploring a variety of
candidate shapes. Second the picture is extended to propose the
marginal likelihood as a useful tool for anomaly detection
challenges in particle physics. This proposal offers insight into
both precise background model determination and demonstrates a
flexible method to extend signal determination beyond a simple bump
hunt.
Subject
Mathematical Physics,Instrumentation
Cited by
1 articles.
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