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Dark matter sensitivity explored using flow matching on CMS Open Data

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a method to study dark matter using flow matching techniques on CMS Open Data. The approach models backgrounds using a conditional flow-matching continuous normalizing flow trained on selected events. This method achieved projected significances of up to 7.62σ for specific dark matter benchmarks, with an ablation study indicating that extra-jet kinematics significantly contribute to discrimination. AI

IMPACT This research demonstrates novel applications of generative modeling techniques in high-energy physics, potentially influencing future data analysis methods.

RANK_REASON The item is an academic paper detailing a new methodology for particle physics research. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Dark matter sensitivity explored using flow matching on CMS Open Data

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The item is an academic paper detailing a new methodology for particle physics research. [lever_c_demoted from research: ic=1 ai=0.4]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Hitesh Rasineni (VIT-AP University, Amaravati, India), Bhavishya Chebrolu (Mohan Babu University, Tirupati, India) ·

    Hadronic Mono-Z Dark Matter Sensitivity with Flow Matching on CMS Open Data

    arXiv:2609.02923v1 Announce Type: cross Abstract: We present a projected sensitivity study for hadronic mono-$Z$ dark-matter production using CMS Run~2015D HTMHT open data corresponding to 2.256382381~\invfb, from which 1{,}439{,}523 events satisfy the hadronic mono-$Z$ selection…

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