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arXiv Machine Learning By 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

Read the original on arXiv Machine Learning →

The paper projects the sensitivity of a hadronic mono‑Z dark‑matter search using CMS Run 2015D HTMHT open data (2.256 fb⁻¹). A conditional flow‑matching normalizing flow models backgrounds, with careful handling of missing features and a sentinel imputation strategy. The baseline analysis yields expected significances of 2.89σ, 7.62σ, and 7.41σ for three benchmark models, and an ablation study shows that extra‑jet kinematics contribute 53–71% of the discriminating power.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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