Ezequiel Alvarez - Maximizing the Potential of LHC Data: A New Frontier in Particle Physics

Aula B (Via della Vasca Navale 84)

Aula B

Via della Vasca Navale 84


The LHC is a fantastic Machine whose design and construction was incredibly carefully coordinated to give the best possible performance in all of its aspects. We are committed to harness every bit of its data with unparalleled precision and creativity, employing the most advanced tools and techniques at our disposal. Recent advancements in Bayesian Statistics techniques and some preliminary adaptations to LHC observables, show that there may be room for sensitivity and scope improvement in some analyses. We show that, in simplified scenarios, usual data-driven methods can be improved, in particular by instead of using hard-cuts to define a signal region, using prior knowledge on distributions to compute a Mixture Model to obtain signal and background estimations. This works even if –as expected– the prior-info is biased with respect to the real data. We also show that the correlation (dependence) of the objects distribution in an event at the event-by-event level provides very profitable information about the dataset, and show some of the tools to exploit this information. We discuss some current analyses which could be improved by using these tools, although still a detailed study including all the aspects (e.g. systematic uncertainties) is needed to answer the question of whether there is room for improvement in current analyses.

References: 2404.01387, 2402.08001 + work in progress