# Parameter inference from event ensembles and the top-quark mass

@article{Flesher2021ParameterIF, title={Parameter inference from event ensembles and the top-quark mass}, author={Forrest Flesher and Katherine Fraser and Charles Hutchison and Bryan Ostdiek and Matthew D. Schwartz}, journal={Journal of High Energy Physics}, year={2021} }

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 profiled when fitting the top-quark…

## 5 Citations

Bayesian probabilistic modeling for four-top production at the LHC

- PhysicsPhysical Review D
- 2022

Monte Carlo (MC) generators are crucial for analyzing data in particle collider experiments. However, often even a small mismatch between the MC simulations and the measurements can undermine the…

A method for approximating optimal statistical significances with machine-learned likelihoods

- Computer Science
- 2022

A new method is presented that combines the power of current machine-learning techniques to face high-dimensional data with the likelihood-based inference tests used in traditional analyses, which allows us to estimate the sensitivity for both discovery and exclusion limits through a single parameter of interest, the signal strength.

A $W^\pm$ polarization analyzer from Deep Neural Networks

- Physics
- 2021

In this paper we train a Convolutional Neural Network to classify longitudinally and transversely polarized hadronic W± using the images of boosted W± jets as input. The images capture angular and…

Mixture Density Network Estimation of Continuous Variable Maximum Likelihood Using Discrete Training Samples

- MathematicsThe European Physical Journal C
- 2021

The usage of MDNs for parameter estimation is demonstrated, the origins of the biases are discussed, and a corrective method is proposed for each issue.

Learning from many collider events at once

- Computer Science
- 2021

It is found that training a single-event classifier is more effective than training a multievent (per-ensemble) classifier, as least for the cases the authors studied, and this fact is related to properties of the loss function gradient in the two cases.

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