hep-ex

Observation of the electromagnetic radiative decays of the \boldmath{$Λ(1520)$} and \boldmath{$Λ(1670)$} to \boldmath{$γΣ^0$}

Using a large sample of $J/\psi$ events collected with the BESIII detector, this study reports the first observation of the electromagnetic radiative decays $\Lambda(1520) \to \gamma\Sigma^0$ and $\Lambda(1670) \to \gamma\Sigma^0$, measuring their branching fractions and revealing that the $\Lambda(1520)$ decay width challenges predictions from constituent quark and algebraic models.

JEDI-linear: Fast and Efficient Graph Neural Networks for Jet Tagging on FPGAs

This paper introduces JEDI-linear, a novel linear-complexity Graph Neural Network architecture that utilizes fine-grained quantization and multiplier-free operations to achieve record-breaking latency and resource efficiency on FPGAs, successfully meeting the real-time jet tagging requirements for the HL-LHC CMS Level-1 trigger system.

DeepMET: Improving missing transverse momentum estimation with a deep neural network

DeepMET is a novel missing transverse momentum estimator developed by the CMS Collaboration that utilizes deep neural networks to assign weights to reconstructed particles, resulting in improved resolution, greater versatility across final states, and increased resilience to pileup compared to existing methods.

Boosted decision tree reweighting of simulated neutrino interactions for $O(1)$ GeV neutrino cross section measurements

This paper presents a generic method using Boosted Decision Trees to multi-dimensionally reweight existing neutrino Monte Carlo simulations to match a target model, allowing for efficient reuse of legacy data without the need for new event generation.

Learning to Reconstruct: A Differentiable Approach to Muon Tracking at the LHC

This paper introduces a novel end-to-end muon tracking approach that utilizes differentiable programming to integrate physics priors directly into a machine learning model, combining graph attention networks with differentiable clustering and fitting to simultaneously improve hit selection and transverse momentum estimation.

Jet Charge with Global Event Shapes: Probing Quark Flavor Dynamics

This paper proposes a new measurement technique that combines jet charge with global event shapes (specifically 1-jettiness in DIS) to simultaneously probe quark flavor dynamics in both initial-state nucleon structure and final-state hadronization processes.

What can we learn from the radiative decays of the $D_{s1}(2460)$ meson?

This paper argues that simultaneously measuring or constraining the ratio of the branching fractions for the radiative decays $D_{s1}(2460)\to\gamma D^{*}_{s0}(2317)$ and $D_{s1}(2460)\to\gamma DK$ can provide crucial insights into the internal nature of the $D^{*}_{s0}(2317)$ and $D_{s1}(2460)$ mesons.

Novel High-Radiopurity Doped Amorphous Silicon Resistors for Low-Background Detectors

This paper presents the development and testing of lightly doped amorphous silicon resistors designed for ultra-high radiopurity, mechanical stability, and cryogenic performance to be used in low-background detectors like the nEXO experiment.

jBOT: Semantic Jet Representation Clustering Emerges from Self-Distillation

jBOT is a self-distillation pre-training method for LHC jet data that learns semantic representations by combining local particle-level and global jet-level distillation, enabling emergent class clustering that improves performance in downstream anomaly detection and classification tasks.

Generalized Neutrino Interactions: constraints and parametrizations