Unbinned Inference with Correlated Events

Fuente: arXiv
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Autores principales: Desai, Krish, Long, Owen, Nachman, Benjamin
Formato: Preprint
Publicado: 2025
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author Desai, Krish
Long, Owen
Nachman, Benjamin
author_facet Desai, Krish
Long, Owen
Nachman, Benjamin
contents Modern machine learning has enabled parameter inference from event-level data without the need to first summarize all events with a histogram. All of these unbinned inference methods make use of the fact that the events are statistically independent so that the log likelihood is a sum over events. However, this assumption is not valid for unbinned inference on unfolded data, where the deconvolution process induces a correlation between events. We explore the impact of event correlations on downstream inference tasks in the context of the OmniFold unbinned unfolding method. We find that uncertainties may be significantly underestimated when event correlations are excluded from uncertainty quantification.
format Preprint
id arxiv_https___arxiv_org_abs_2504_14072
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Unbinned Inference with Correlated Events
Desai, Krish
Long, Owen
Nachman, Benjamin
Data Analysis, Statistics and Probability
High Energy Physics - Experiment
Modern machine learning has enabled parameter inference from event-level data without the need to first summarize all events with a histogram. All of these unbinned inference methods make use of the fact that the events are statistically independent so that the log likelihood is a sum over events. However, this assumption is not valid for unbinned inference on unfolded data, where the deconvolution process induces a correlation between events. We explore the impact of event correlations on downstream inference tasks in the context of the OmniFold unbinned unfolding method. We find that uncertainties may be significantly underestimated when event correlations are excluded from uncertainty quantification.
title Unbinned Inference with Correlated Events
topic Data Analysis, Statistics and Probability
High Energy Physics - Experiment
url https://arxiv.org/abs/2504.14072