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2026-01-27
07:46
Benchmarking neutrino-nucleus quasielastic scattering model predictions against a missing energy profile obtained using a monoenergetic neutrino beam / McKean, Jake (Imperial Coll., London ; Kyoto U. (main)) ; Munteanu, Laura (CERN) ; Abe, Seisho (U. Tokyo (main))
We examine three exclusive nuclear ground state shell models implemented in the NEUT neutrino event generator and benchmark them against the recent JSNS$^2$ measurement of missing energy using a monoenergetic neutrino source. [...]
arXiv:2601.14831. - 2026 - 11.
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2026-01-27
05:34
Machine learning techniques for jet reconstruction at LHCb and application to the search for $H \to b \bar{b}$ and $H \to c \bar{c}$ in $\sqrt{s}=13$ TeV $pp$ collisions / LHCb Collaboration
Two machine learning techniques for jet measurements at the LHCb experiment are presented: a regression-based method for jet-energy calibration and a deep neural network algorithm for jet flavour tagging, distinguishing between $b$-quark, $c$-quark, and light parton jets. [...]
arXiv:2601.16802 ; LHCb-PAPER-2025-034 ; CERN-EP-2025-275. - 34.
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2026-01-26
13:31
Full event interpretation with machine-learning-based particle-flow reconstruction in the CMS detector / CMS Collaboration
The particle-flow (PF) algorithm constructs a global description of each particle collision by producing a comprehensive list of final-state particles, and is central to event reconstruction in the CMS experiment at the CERN LHC. [...]
arXiv:2601.17554 ; CMS-PFT-25-001 ; CERN-EP-2025-300 ; CMS-PFT-25-001-003. - 2026
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2026-01-26
12:06
Search for the pair production of long-lived supersymmetric partners of the tau lepton in proton-proton collisions at $ \sqrt{s}= $ 13 TeV / CMS Collaboration
Gauge-mediated supersymmetry-breaking models provide a strong motivation to search for a supersymmetric partner of the tau lepton (stau) with a macroscopic lifetime. [...]
arXiv:2601.17576 ; CMS-EXO-24-020 ; CERN-EP-2025-296 ; CMS-EXO-24-020-003. - 2026 - 49 p, 49.
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2026-01-26
11:30
Strategy and performance of the CMS long-lived particle trigger program in proton-proton collisions at $ \sqrt{s}= $ 13.6 TeV / CMS Collaboration
In the physics program of the CMS experiment during the CERN LHC Run 3, which started in 2022, the long-lived particle triggers have been improved and extended to expand the scope of the corresponding searches. [...]
arXiv:2601.17544 ; CMS-EXO-23-016 ; CERN-EP-2025-282 ; CMS-EXO-23-016-003. - 2026 - 109 p, 109.
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2026-01-26
10:45
Observation of $CP$ violation in $B^0 \to J/\psi \rho(770)^0$ decays / LHCb Collaboration
The time-dependent $C\!P$ asymmetry in $B^{0}\!\to{J\mskip-3mu/\mskip-2mu\psi}\rho(770)^0$ decays is measured using proton-proton collision data corresponding to an integrated luminosity of 6$fb^{-1}$ collected with the LHCb detector at a center-of-mass energy of 13 TeV during the years 2015–2018. [...]
LHCb-PAPER-2025-059 ; CERN-EP-2025-295 ; arXiv:2601.15646 ; LHCB-PAPER-2025-059. - 2026 - 21.
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2026-01-24
06:36
Symbolic Pauli Propagation for Gradient-Enabled Pre-Training of Quantum Circuits / Monaco, Saverio (Aachen, Tech. Hochsch. ; DESY) ; Slim, Jamal (DESY) ; Rehm, Florian (CERN) ; Krücker, Dirk (DESY) ; Borras, Kerstin (Aachen, Tech. Hochsch. ; DESY)
Quantum Machine Learning models typically require expensive on-chip training procedures and often lack efficient gradient estimation methods. [...]
arXiv:2512.16674. - 2025 - 10.
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2026-01-24
06:36
Towards Tensor Network Models for Low-Latency Jet Tagging on FPGAs / Coppi, Alberto (Padua U. ; U. Padua (main)) ; Puljak, Ema (Barcelona, Autonoma U.) ; Borella, Lorenzo (Padua U. ; U. Padua (main)) ; Jaschke, Daniel (Padua U. ; U. Padua (main) ; Unlisted, DE ; U. Ulm) ; Rico, Enrique (CERN) ; Pierini, Maurizio (CERN) ; Pazzini, Jacopo (Padua U. ; U. Padua (main)) ; Triossi, Andrea (Padua U. ; U. Padua (main)) ; Montangero, Simone (Padua U. ; INFN, Padua)
We present a systematic study of Tensor Network (TN) models $\unicode{x2013}$ Matrix Product States (MPS) and Tree Tensor Networks (TTN) $\unicode{x2013}$ for real-time jet tagging in high-energy physics, with a focus on low-latency deployment on Field Programmable Gate Arrays (FPGAs). [...]
arXiv:2601.10801. - 2026 - 10.
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2026-01-24
06:36
Softly Induced Functional Simplicity: Implications for Neural Network Generalisation, Robustness, and Distillation / Glowacki, Maciej (CERN)
Learning robust and generalisable abstractions from high-dimensional input data is a central challenge in machine learning and its applications to high-energy physics (HEP). [...]
arXiv:2601.06584. - 2026 - 12.
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2026-01-24
06:36
Trigonometric continuous-variable gates and hybrid quantum simulations / Rainaldi, Tommaso (Stony Brook U.) ; Ale, Victor (U. Tennessee, Knoxville) ; Grau, Matt (Old Dominion U.) ; Kharzeev, Dmitri (Stony Brook U. ; Brookhaven Natl. Lab.) ; Rico, Enrique (CERN) ; Ringer, Felix (Stony Brook U.) ; Shome, Pubasha (Stony Brook U.) ; Siopsis, George (U. Tennessee, Knoxville)
Hybrid qubit-qumode quantum computing platforms provide a natural setting for simulating interacting bosonic quantum field theories. [...]
arXiv:2512.19582. - 2025 - 25.
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