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Deep Sets model enhances jet rejection at Large Hadron Collider · 1 source tracked

Researchers have developed DIPz, a machine learning model utilizing Deep Sets to improve jet rejection in high-energy physics experiments. This model estimates the origin of jets by analyzing charged particle tracks, combining these predictions into an event-level discriminant. Deployed during the Large Hadron Collider's Run 3, DIPz has successfully reduced background rates by over 50% and decreased CPU consumption by approximately one-third, enhancing the selection of multijet final states. AI

IMPACT This model's application in particle physics could accelerate real-time data analysis and improve the efficiency of identifying specific collision events.

RANK_REASON This is a research paper detailing a new machine learning model for physics experiments. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

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

Deep Sets model enhances jet rejection at Large Hadron Collider · 1 source tracked

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This is a research paper detailing a new machine learning model for physics experiments. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Mohammed Aboelela ·

    Deep Sets and event-level maximum-likelihood estimation for fast pile-up jet rejection at the ATLAS High Level Trigger

    arXiv:2512.10819v2 Announce Type: replace-cross Abstract: Multiple proton-proton interactions occur in the same bunch crossing at the LHC ("pile-up"), with the mean number of interactions per bunch crossing reaching up to about 60 in Run 3 and up to about 200 at the High-Luminosi…