Professor Daniela Bortoletto

Abstract:

In recent years, neural network-based classifica- tion has been used to improve data analysis at collider exper- iments. While this strategy proves to be hugely successful, the underlying models are not commonly shared with the public and rely on experiment-internal data as well as full detector simulations. We show a concrete implementation of a newly proposed strategy, so-called Classifier Surrogates, to be trained inside the experiments, that only utilise publicly accessible features and truth information. These surrogates approximate the original classifier distribution, and can be shared with the public. Subsequently, such a model can be evaluated by sampling the classification output from high- level information without requiring a sophisticated detector simulation. Technically, we show that continuous normaliz- ing flows are a suitable generative architecture that can be efficiently trained to sample classification results using con- ditional flow matching. We further demonstrate that these models can be easily extended by Bayesian uncertainties to indicate their degree of validity when confronted with unknown inputs by the user. For a concrete example of tag- ging jets from hadronically decaying top quarks, we demon- strate the application of flows in combination with uncer- tainty estimation through either inference of a mean-field Gaussian weight posterior, or Monte Carlo sampling network weights

Abstract:

Rare $B$, $D$, and $K$ decays offer unique opportunities to probe for evidence of new particles from physics beyond the Standard Model at mass scales extending from the electroweak scale to well above those directly accessible at the LHC. We review a selection of theoretical and experimental results on rare $B$, $D$, and $K$ decays illustrating the progress made during the past two years.Comment: 15 pages, 3 figures. To appear in the proceedings of the 9th International Workshop on the CKM Unitarity Triangle, Nov 28- Dec 2 2016, Mumba