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This is a Quantitative Study study.

September 24, 2025Open Access

Log Gaussian Cox Process Background Modeling in High Energy Physics

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Authors

YFY. Y. Frid LBL. BarakUniversidade Federal de Juiz de Fora PJPavani JairamNorthwestern University

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Implication

This research introduces a new method using log gaussian cox processes for accurate background modeling, suggesting improved performance over traditional approaches.

Key Points

Background modeling significantly improves high energy physics data analysis, enhancing accuracy and reliability.
The study compares the log gaussian cox process method with traditional functional forms using synthetic experiments.
Markov chain monte carlo optimization allows for efficient parameter fitting in the background estimation process.
Introducing a non-homogeneous poisson process in background modeling may provide better adaptability to underlying data characteristics.

Cite This Study

Frid et al. (2025) studied this question.

synapsesocial.com/papers/68d6e16f8b2b6861e4c40169 https://doi.org/10.48550/arxiv.2508.11740

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Implication

This research introduces a new method using log gaussian cox processes for accurate background modeling, suggesting improved performance over traditional approaches.

Log Gaussian Cox Process Background Modeling in High Energy Physics

Discussion

Member takes

Implication

Key Points

Cite This Study

Log Gaussian Cox Process Background Modeling in High Energy Physics

Discussion

Member takes

Implication

Key Points

Cite This Study

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