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Machine learning framework enhances parameter inference in physics and cosmology

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new machine learning framework to emulate complex likelihood landscapes in high energy physics and cosmology. This framework utilizes XGBoost to efficiently explore high-dimensional parameter spaces, offering advantages in computational speed and the resolution of confidence regions. The methodology was validated on semileptonic B meson decays and is adaptable to other systems like axion-like particles. SHAP values are employed to ensure the machine learning predictions are transparent and physically interpretable. AI

IMPACT This framework could accelerate research in fields like high energy physics and cosmology by improving the efficiency and interpretability of complex simulations.

RANK_REASON The cluster contains a research paper detailing a new machine learning methodology for scientific applications.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Machine learning framework enhances parameter inference in physics and cosmology

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Jorge Alda, Jacobo Asorey, Alejandro Mir, Siannah Pe\~naranda ·

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

    arXiv:2607.12726v1 Announce Type: cross Abstract: 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 Mach…

  2. arXiv cs.LG TIER_1 English(EN) · Siannah Peñaranda ·

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

    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…

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