1. Parameter inference from event ensembles and the top-quark mass
- Author
-
Charles Hutchison, Matthew D. Schwartz, Bryan Ostdiek, Katherine Fraser, and Forrest Flesher
- Subjects
Physics ,Nuclear and High Energy Physics ,Top quark ,Large Hadron Collider ,Artificial neural network ,010308 nuclear & particles physics ,Estimation theory ,FOS: Physical sciences ,QC770-798 ,01 natural sciences ,High Energy Physics - Phenomenology ,High Energy Physics - Phenomenology (hep-ph) ,Top physics ,Hadron-Hadron scattering (experiments) ,Histogram ,Nuclear and particle physics. Atomic energy. Radioactivity ,0103 physical sciences ,Linear regression ,Nuisance parameter ,Statistical physics ,010306 general physics ,Event (probability theory) - Abstract
One of the key tasks of any particle collider is measurement. In practice, this is often done by fitting data to a simulation, which depends on many parameters. Sometimes, when the effects of varying different parameters are highly correlated, a large ensemble of data may be needed to resolve parameter-space degeneracies. An important example is measuring the top-quark mass, where other physical and unphysical parameters in the simulation must be marginalized over when fitting the top-quark mass parameter. We compare three different methodologies for top-quark mass measurement: a classical histogram fitting procedure, similar to one commonly used in experiment optionally augmented with soft-drop jet grooming; a machine-learning method called DCTR; and a linear regression approach, either using a least-squares fit or with a dense linearly-activated neural network. Despite the fact that individual events are totally uncorrelated, we find that the linear regression methods work most effectively when we input an ensemble of events sorted by mass, rather than training them on individual events. Although all methods provide robust extraction of the top-quark mass parameter, the linear network does marginally best and is remarkably simple. For the top study, we conclude that the Monte-Carlo-based uncertainty on current extractions of the top-quark mass from LHC data can be reduced significantly (by perhaps a factor of 2) using networks trained on sorted event ensembles. More generally, machine learning from ensembles for parameter estimation has broad potential for collider physics measurements., Comment: v1: 27 + 5 pages, 14 + 3 figures. v2: Matches version accepted to JHEP
- Published
- 2021