hep-ph · 2026-07-15 · No. 54

High Energy Physics - Phenomenology, 2026-07-15.

1 new papers in hep-ph. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

1 entries
  1. 01

    Physically Consistent Parameter Inference: Transparent Machine Learning Emulation in High Energy Physics and Cosmology

    Jorge Alda, Jacobo Asorey, Alejandro Mir, Siannah Peñaranda

    hep-ph · cs.LG

    Global fits in high energy physics and cosmology often face the challenge of exploring high-dimensional parameter spaces with computationally expensive or topologically complex likelihood functions. In this work, we present a Machine Learning framework designed to emulate complex, often non-Gaussian likelihood landscapes using gradient-boosted regression trees (XGBoost). We discuss the advantages of the Machine Learning approach in terms of...

    arxiv.org/abs/2607.12726 · PDF

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