Conveners
Machine Learning, Neutrino
- Joel Walker (Sam Houston State University)
Transit spectroscopy is the primary tool for inferring the physical parameters and the atmospheric chemical composition of extrasolar planets. I will discuss some recently proposed AI-inspired techniques for exoplanet parameter retrievals, including dimensional analysis, vector component analysis, exploratory data analysis, feature engineering, dimensionality reduction and manifold learning,...
Simulation of particle interactions with detector material, especially in the calorimeters are very time-consuming and resource intensive. In the upcoming LHC runs, these could provide a bottleneck that severely limits our analysis capabilities.
In recent years, approaches based on deep generative models have provided a fresh alternative to "classical" fast simulation. In this talk, I present...
We explore the direct Higgs-top CP structure via the $pp \to t\bar{t}h$ channel with machine learning techniques, considering the clean $h \to \gamma\gamma$ final state at the high luminosity LHC~(HL-LHC). We show that a combination of a comprehensive set of observables, that includes the $t\bar{t}$ spin-correlations, with mass minimization strategies to reconstruct the $t\bar{t}$ rest frame...
