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.
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.
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.
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...
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.
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.
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.
arXiv:2605.29016v2 Announce Type: replace-cross Abstract: We investigate conditional diffusion modeling for three-dimensional 21 cm lightcone emulation, focusing on cubes with a sky-plane size of $64...
arXiv:2605.28916v3 Announce Type: replace-cross Abstract: We report a methodological study of agentic AI in gravitational-wave data analysis: two systems, Claude Code (Anthropic) and Codex (OpenAI),...
arXiv:2608. 00675v1 Announce Type: cross Abstract: Autoregressive models accumulate error over long rollouts, yet at deployment there is no ground truth to measure it against.
arXiv:2607. 16811v4 Announce Type: replace Abstract: Drift detectors that work tend not to explain themselves, and drift detectors that explain themselves tend to fail in high dimension.
The study investigates whether the number of discrete class‑separability jumps (phase transitions) observed during ResNet fine‑tuning can predict final test accuracy. Across 75 experiments on four benchmarks (CIFAR‑10, CIFAR‑100, TinyImageNet, CIFAR‑10‑C) and three ResNet variants, a strong negative correlation is found on standard i.i.d. datasets (r = −0.84 on CIFAR‑10, r = −0.87 on CIFAR‑100), while the correlation weakens under distributional stress. Additional analyses show that the transition count retains predictive power after controlling for architecture depth and outperforms other training‑curve signals on in‑distribution benchmarks, though it is dominated by other signals on stressed datasets.
arXiv:2605. 23434v2 Announce Type: replace Abstract: Approximate inference over inducing variables is the central computational bottleneck of Deep Gaussian Processes (DGPs).