Speaker
Description
(contribution via Zoom)
The practice of collider physics typically involves
the marginalization of multi-dimensional collider data to uni-dimensional observables relevant for some physics task. In many cases, such as discrimination or anomaly detection, the observable can be arbitrarily complicated, such as the output of a neural network. However, for precision measurements, the observable must correspond to something computable systematically beyond the level of current simulation tools.
In this work, we demonstrate that precision-theory-compatible observable space exploration can be systematized by using neural simulation-based inference techniques from machine learning. We illustrate this approach by exploring the space of marginalizations of the energy 3-point correlation function to optimize sensitivity to the the top quark mass.
We first learn the energy-weighted probability density from simulation, then search in the space of marginalizations for an optimal triangle shape. We
find that isosceles triangles with a side ratio of $1:1:\sqrt{2}$ (i.e. right triangles) improves over the equilateral triangles used previously.
Although simulations are used as a leading-order approximation to theory, and machine learning is used to find an optimal observable, the output is an observable definition which can be then computed to high precision and compared directly to data without any memory of the computations which produced it.