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 simulation.
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.
By Hitesh Rasineni (VIT-AP University, Amaravati, India), Bhavishya Chebrolu (Mohan Babu University, Tirupati, India)
arXiv:2609.06686v1 Announce Type: cross
Abstract: We present an unsupervised search for anomalous dijet events in proton--proton collision data using neural spline flow density estimation. A normaliz...
By Bhavishya Chebrolu (VIT-AP University, Amaravati, India), Hitesh Rasineni (VIT-AP University, Amaravati, India), Prajwal Aaryan Immadi (VIT-AP University, Amaravati, India)
arXiv:2606. 01078v1 Announce Type: new Abstract: Transport MCMC trains a normalizing flow to precondition Metropolis--Hastings proposals, achieving high empirical efficiency on challenging posteriors; yet no prior work produces a numerically non-vacuous, rigorous spectral-gap bound for such samplers.
By Jun Hu
arXiv:2607. 03809v1 Announce Type: new Abstract: Normalising flows provide a powerful variational family for approximate inference, yet individual architectures often fail to generalise across heterogeneous posterior geometries.
By Benjamin Wiriyapong, Oktay Karakus, Can Eyupoglu, Kirill Sidorov
arXiv:2606. 00219v1 Announce Type: cross Abstract: We are witnessing a surge in observations of the cosmic dawn (CD) and epoch of reionisation (EoR), driving an increasing demand for fast and robust theoretical interpretation frameworks.
By Daniela Breitman, Andrei Mesinger, Steven G. Murray, Ivan Nikolic, Roberto Trotta