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Dark matter search uses Neural Spline Flows on CMS data

Researchers have conducted a search for dark matter produced in association with a Z boson using CMS Run 2015D open data. The study employed Neural Spline Flows to model background and signal densities, reducing observational data into a 37-dimensional feature vector. While the analysis did not find evidence for dark matter, it established upper limits on signal-strength parameters for scalar, vector, and axial-vector mediators, noting that observed limits were weaker than expected due to a background modeling discrepancy. AI

IMPACT This research applies advanced machine learning techniques, specifically Neural Spline Flows, to particle physics data, potentially paving the way for more sophisticated analysis in complex scientific domains.

RANK_REASON The cluster contains an arXiv paper detailing a scientific research methodology and findings.

Read on arXiv cs.LG →

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Dark matter search uses Neural Spline Flows on CMS data

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COVERAGE [2]

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

    Mono-Z Dark Matter Search with Neural Spline Flows Using CMS Run 2015D Open Data

    arXiv:2607.13771v1 Announce Type: new Abstract: We report a search for dark matter (DM) produced in association with a leptonically decaying \(Z\) boson at \(\sqrt{s}=13\) TeV using CMS Run 2015D open data corresponding to an integrated luminosity of \(2.32\,\mathrm{fb}^{-1}\) to…

  2. arXiv cs.LG TIER_1 English(EN) · Bhavishya Chebrolu ·

    Mono-Z Dark Matter Search with Neural Spline Flows Using CMS Run 2015D Open Data

    We report a search for dark matter (DM) produced in association with a leptonically decaying \(Z\) boson at \(\sqrt{s}=13\) TeV using CMS Run 2015D open data corresponding to an integrated luminosity of \(2.32\,\mathrm{fb}^{-1}\) together with simplified-model Monte Carlo simulat…