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. Firstly the marginal likelihood based method is used to propose a more principled construction of the background process, systematically exploring a variety of candidate shapes. Secondly the picture is extended to propose the marginal likelihood as an 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.
Lightning summary:
The marginal likelihood can be used to construct both precisely determined backgrounds and sensitive data driven signal searches. Model comparison in it’s Bayesian realisation is ideal for this context.
