Conveners
Overcoming challenges I: Publishing and using statistical models
- Sabine Kraml (LPSC Grenoble)
Analysing statistical models is at the heart of any empirical study for hypothesis testing. We present a new cross-platform Python-based package which employs different likelihood prescriptions through a plug-in system, enabling the statistical inference of hypotheses. This framework empowers users to propose, examine, and publish new likelihood prescriptions without the need for developing a...
An increasingly frequent challenge faced in HEP data analysis is to characterize the agreement between a prediction that depends on a dozen or more model parameters-such as predictions coming from an effective field theory (EFT) framework-and the observed data. Traditionally, such characterizations take the form of a negative log likelihood (NLL) distribution, which can only be evaluated...
Rare decays like $B^+ \to K^+ \nu \bar{\nu}$, searched for by the Belle II collaboration, are important in particle physics research as they offer a window into physics beyond the Standard Model. However, the experimental challenges induced by the two final state neutrinos require assumptions on the kinematic distribution of this decay....
Most Beyond Standard Model (BSM) physics theories are characterized by multiple BSM parameters. These encompass properties like new particle masses, coupling constants, decay widths, and effective field theory parameters.
When testing such theories against data, analysts might choose to consider only a subset of relevant BSM physics parameter in order to work within limits of computational...
Machine learning tools have enabled a new type of differential cross section measurements that are unbinned and high-dimensional (see e.g. 2109.13243). This talk will discuss the challenges and prospects of (re)using such measurements with respect to new physics.
