An unfolding method based on conditional invertible neural networks (cINN) using iterative training

Backes, Mathias; Butter, Anja; Dunford, Monica; Malaescu, Bogdan

doi:10.21468/SciPostPhysCore.7.1.007

SciPost Physics Core

An unfolding method based on conditional invertible neural networks (cINN) using iterative training

Mathias Backes, Anja Butter, Monica Dunford, Bogdan Malaescu

SciPost Phys. Core 7, 007 (2024) · published 21 February 2024

doi: 10.21468/SciPostPhysCore.7.1.007
pdf
Submissions/Reports

Abstract

The unfolding of detector effects is crucial for the comparison of data to theory predictions. While traditional methods are limited to representing the data in a low number of dimensions, machine learning has enabled new unfolding techniques while retaining the full dimensionality. Generative networks like invertible neural networks~(INN) enable a probabilistic unfolding, which map individual data events to their corresponding unfolded probability distribution. The accuracy of such methods is however limited by how well simulated training samples model the actual data that is unfolded. We introduce the iterative conditional INN (IcINN) for unfolding that adjusts for deviations between simulated training samples and data. The IcINN unfolding is first validated on toy data and then applied to pseudo-data for the $pp \to Z \gamma \gamma$ process.

TY  - JOUR
PB  - SciPost Foundation
DO  - 10.21468/SciPostPhysCore.7.1.007
TI  - An unfolding method based on conditional invertible neural networks (cINN) using iterative training
PY  - 2024/02/21
UR  - https://scipost.org/SciPostPhysCore.7.1.007
JF  - SciPost Physics Core
JA  - SciPost Phys. Core
VL  - 7
IS  - 1
SP  - 007
A1  - Backes, Mathias
AU  - Butter, Anja
AU  - Dunford, Monica
AU  - Malaescu, Bogdan
AB  - The unfolding of detector effects is crucial for the comparison of data to theory predictions. While traditional methods are limited to representing the data in a low number of dimensions, machine learning has enabled new unfolding techniques while retaining the full dimensionality. Generative networks like invertible neural networks~(INN) enable a probabilistic unfolding, which map individual data events to their corresponding unfolded probability distribution. The accuracy of such methods is however limited by how well simulated training samples model the actual data that is unfolded. We introduce the iterative conditional INN (IcINN) for unfolding that adjusts for deviations between simulated training samples and data. The IcINN unfolding is first validated on toy data and then applied to pseudo-data for the $pp \to Z \gamma \gamma$ process.
ER  -

@Article{10.21468/SciPostPhysCore.7.1.007,
	title={{An unfolding method based on conditional invertible neural networks (cINN) using iterative training}},
	author={Mathias Backes and Anja Butter and Monica Dunford and Bogdan Malaescu},
	journal={SciPost Phys. Core},
	volume={7},
	pages={007},
	year={2024},
	publisher={SciPost},
	doi={10.21468/SciPostPhysCore.7.1.007},
	url={https://scipost.org/10.21468/SciPostPhysCore.7.1.007},
}

Cited by 5

Authors / Affiliations: mappings to Contributors and Organizations

See all Organizations.

¹ Mathias Backes,
¹ ² Anja Butter,
¹ Monica Dunford,
² Bogdan Malaescu

Funders for the research work leading to this publication