Phase space sampling and inference from weighted events with autoregressive flows

Stienen, Bob; Verheyen, Rob

doi:10.21468/SciPostPhys.10.2.038

SciPost Physics

Phase space sampling and inference from weighted events with autoregressive flows

Bob Stienen, Rob Verheyen

SciPost Phys. 10, 038 (2021) · published 17 February 2021

doi: 10.21468/SciPostPhys.10.2.038
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Abstract

We explore the use of autoregressive flows, a type of generative model with tractable likelihood, as a means of efficient generation of physical particle collider events. The usual maximum likelihood loss function is supplemented by an event weight, allowing for inference from event samples with variable, and even negative event weights. To illustrate the efficacy of the model, we perform experiments with leading-order top pair production events at an electron collider with importance sampling weights, and with next-to-leading-order top pair production events at the LHC that involve negative weights.

TY  - JOUR
PB  - SciPost Foundation
DO  - 10.21468/SciPostPhys.10.2.038
TI  - Phase space sampling and inference from weighted events with autoregressive flows
PY  - 2021/02/17
UR  - https://scipost.org/SciPostPhys.10.2.038
JF  - SciPost Physics
JA  - SciPost Phys.
VL  - 10
IS  - 2
SP  - 038
A1  - Stienen, Bob
AU  - Verheyen, Rob
AB  - We explore the use of autoregressive flows, a type of generative model with tractable likelihood, as a means of efficient generation of physical particle collider events. The usual maximum likelihood loss function is supplemented by an event weight, allowing for inference from event samples with variable, and even negative event weights. To illustrate the efficacy of the model, we perform experiments with leading-order top pair production events at an electron collider with importance sampling weights, and with next-to-leading-order top pair production events at the LHC that involve negative weights.
ER  -

@Article{10.21468/SciPostPhys.10.2.038,
	title={{Phase space sampling and inference from weighted events with autoregressive flows}},
	author={Bob Stienen and Rob Verheyen},
	journal={SciPost Phys.},
	volume={10},
	pages={038},
	year={2021},
	publisher={SciPost},
	doi={10.21468/SciPostPhys.10.2.038},
	url={https://scipost.org/10.21468/SciPostPhys.10.2.038},
}

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¹ Bob Stienen,
² Rob Verheyen

Funders for the research work leading to this publication