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LHCb Collaboration(Aaij, R. et al), Jaimes Elles, S. J., Jashal, B. K., Martinez-Vidal, F., Oyanguren, A., Rebollo De Miguel, M., et al. (2024). Prompt and nonprompt ψ(2S) production in pPb collisions at √sNN = 8.16 TeV. J. High Energy Phys., 04(4), 111–52pp.
Abstract: The production of psi(2S) mesons in proton-lead collisions at a centre-of-mass energy per nucleon pair of root s(NN) = 8.16TeV is studied with the LHCb detector using data corresponding to an integrated luminosity of 34 nb(-1). The prompt and nonprompt psi(2S) production cross-sections and the ratio of the psi(2S) to J/psi cross-section are measured as a function of the meson transverse momentum and rapidity in the nucleon-nucleon centre-of-mass frame, together with forward-to-backward ratios and nuclear modification factors. The production of prompt psi(2S) is observed to be more suppressed compared to pp collisions than the prompt J/psi production, while the nonprompt productions have similar suppression factors.
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Anzivino, G. et al, Gonzalez-Alonso, M., Passemar, E., & Pich, A. (2024). Workshop summary: Kaons@CERN 2023. Eur. Phys. J. C, 84(4), 377–34pp.
Abstract: Kaon physics is at a turning point – while the rare-kaon experiments NA62 and KOTO are in full swing, the end of their lifetime is approaching and the future experimental landscape needs to be defined. With HIKE, KOTO-II and LHCb-Phase-II on the table and under scrutiny, it is a very good moment in time to take stock and contemplate about the opportunities these experiments and theoretical developments provide for particle physics in the coming decade and beyond. This paper provides a compact summary of talks and discussions from the Kaons@CERN 2023 workshop, held in September 2023 at CERN.
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CALICE Collaboration(Lai, S. et al), & Irles, A. (2024). Software compensation for highly granular calorimeters using machine learning. J. Instrum., 19(4), P04037–28pp.
Abstract: A neural network for software compensation was developed for the highly granular CALICE Analogue Hadronic Calorimeter (AHCAL). The neural network uses spatial and temporal event information from the AHCAL and energy information, which is expected to improve sensitivity to shower development and the neutron fraction of the hadron shower. The neural network method produced a depth-dependent energy weighting and a time-dependent threshold for enhancing energy deposits consistent with the timescale of evaporation neutrons. Additionally, it was observed to learn an energy-weighting indicative of longitudinal leakage correction. In addition, the method produced a linear detector response and outperformed a published control method regarding resolution for every particle energy studied.
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Lin, J. X., Chen, H. X., Liang, W. H., Xiao, C. W., & Oset, E. (2024). (B)over-bars0 → Ds1(2460)+ K-, Ds1(2536)+ K- and the nature of the two Ds1 resonances. Eur. Phys. J. C, 84(4), 439–8pp.
Abstract: Starting from the molecular picture for the D-s1(2460) and D-s1(2536) resonances, which are dynamically generated by the interaction of coupled channels, the most important of which are the D* K for the D-s1(2460) and DK* for the D-s1(2536), we evaluate the ratio of decay widths for the (B) over bar (0)(s) -> D-s1(2460)(+) K- and (B) over bar (0)(s) -> D-s1(2536)(+) K- decays, the latter of which has been recently investigated by the LHCb collaboration, and we obtain a ratio of the order of unity. The present results should provide an incentive for the related decay into the D-s1(2460) resonance to be performed, which would provide valuable information on the nature of these two resonances.
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Kalliokoski, M., Mitsou, V. A., de Montigny, M., Mukhopadhyay, A., Ouimet, P. P. A., Pinfold, J., et al. (2024). Searching for minicharged particles at the energy frontier with the MoEDAL-MAPP experiment at the LHC. J. High Energy Phys., 04(4), 137–22pp.
Abstract: The MoEDAL's Apparatus for Penetrating Particles (MAPP) Experiment is designed to expand the search for new physics at the LHC, significantly extending the physics program of the baseline MoEDAL Experiment. The Phase-1 MAPP detector (MAPP-1) is currently undergoing installation at the LHC's UA83 gallery adjacent to the LHCb/MoEDAL region at Interaction Point 8 and will begin data-taking in early 2024. The focus of the MAPP experiment is on the quest for new feebly interacting particles – avatars of new physics with extremely small Standard Model couplings, such as minicharged particles (mCPs). In this study, we present the results of a comprehensive analysis of MAPP-1's sensitivity to mCPs arising in the canonical model involving the kinetic mixing of a massless dark U(1) gauge field with the Standard Model hypercharge gauge field. We focus on several dominant production mechanisms of mCPs at the LHC across the mass-mixing parameter space of interest to MAPP: Drell-Yan pair production, direct decays of heavy quarkonia and light vector mesons, and single Dalitz decays of pseudoscalar mesons. The 95% confidence level background-free sensitivity of MAPP-1 for mCPs produced at the LHC's Run 3 and the HL-LHC through these mechanisms, along with projected constraints on the minicharged strongly interacting dark matter window, are reported. Our results indicate that MAPP-1 exhibits sensitivity to sizable regions of unconstrained parameter space and can probe effective charges as low as 8 x 10 -4 e and 6 x 10 -4 e for Run 3 and the HL-LHC, respectively.
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