LHCb Collaboration(Aaij, R. et al), Jaimes Elles, S. J., Jashal, B. K., Libralon, S., Martinez-Vidal, F., Oyanguren, A., et al. (2025). Measurement of exclusive J/ψ and ψ(2S) production at √s = 13 TeV. SciPost Phys., 18(2), 071–33pp.
Abstract: Measurements are presented of the cross-section for the central exclusive production of J/psi -> mu(+) mu(-) and psi(2S) -> mu(+)mu(-) processes in proton-proton collisions at root s = 13 TeV with 2016-2018 data. They are performed by requiring both muons to be in the LHCb acceptance (with pseudorapidity 2 < eta(mu +/-) < 4.5) and mesons in the rapidity range 2.0 < y < 4.5. The integrated cross-section results are sigma(J/psi ->mu+ mu-) (2.0 < y(J/psi) < 4.5, 2.0 < eta(mu +/-) < 4.5) = 400 +/- 2 +/- 5 +/- 12 pb, sigma(psi(2S)->mu+mu-)(2.0 < y(psi(2S)) < 4.5, 2.0 < eta(mu +/-) < 4.5) = 9.40 +/- 0.15 +/- 0.13 +/- 0.27 pb, where the uncertainties are statistical, systematic and due to the luminosity determination. In addition, a measurement of the ratio of psi(2S) and J/psi cross-sections, at an average photon-proton centre-of-mass energy of 1 TeV, is performed, giving sigma(psi(2S))/sigma(J/psi) = 0.1763 +/- 0.0029 +/- 0.0008 +/- 0.0039, where the first uncertainty is statistical, the second systematic and the third due to the knowledge of the involved branching fractions. For the first time, the dependence of the J/psi and psi(2S) cross-sections on the total transverse momentum transfer is determined in pp collisions and is found consistent with the behaviour observed in electron-proton collisions.
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Banerjee, P., Coutinho, A., Engel, T., Gurgone, A., Signer, A., & Ulrich, Y. (2023). High-precision muon decay predictions for ALP searches. SciPost Phys., 15(1), 021–38pp.
Abstract: We present an improved theoretical prediction of the positron energy spectrum for the polarised Michel decay & mu;+ & RARR; e+ & nu;e & nu; over bar & mu;. In addition to the full next-to-next-to-leading order correction of order & alpha;2 in the electromagnetic coupling, we include logarithmically enhanced terms at even higher orders. Logarithms due to collinear emission are included at next-to-leading accuracy up to order & alpha;4. At the endpoint of the Michel spectrum, soft photon emission results in large logarithms that are resummed up to next-to-next-to leading logarithmic accuracy. We apply our results in the context of the MEG II and Mu3e experiments to estimate the impact of the theory error on the branching ratio sensitivity for the lepton-flavour-violating decay & mu;+ & RARR; e+X of a muon into an axion-like particle X.
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Baamara, Y., Gessner, M., & Sinatra, A. (2023). Quantum-enhanced multiparameter estimation and compressed sensing of a field. SciPost Phys., 14(3), 050–18pp.
Abstract: We show that a significant quantum gain corresponding to squeezed or over-squeezed spin states can be obtained in multiparameter estimation by measuring the Hadamard coefficients of a 1D or 2D signal. The physical platform we consider consists of twolevel atoms in an optical lattice in a squeezed-Mott configuration, or more generally by correlated spins distributed in spatially separated modes. Our protocol requires the possibility to locally flip the spins, but relies on collective measurements. We give examples of applications to scalar or vector field mapping and compressed sensing.
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van Beekveld, M., Beenakker, W., Caron, S., Kip, J., Ruiz de Austri, R., & Zhang, Z. Y. (2023). Non-standard neutrino spectra from annihilating neutralino dark matter. SciPost Phys. Core, 6(1), 006–23pp.
Abstract: Neutrino telescope experiments are rapidly becoming more competitive in indirect de-tection searches for dark matter. Neutrino signals arising from dark matter annihilations are typically assumed to originate from the hadronisation and decay of Standard Model particles. Here we showcase a supersymmetric model, the BLSSMIS, that can simulta-neously obey current experimental limits while still providing a potentially observable non-standard neutrino spectrum from dark matter annihilation.
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Khosa, C. K., Sanz, V., & Soughton, M. (2022). A simple guide from machine learning outputs to statistical criteria in particle physics. SciPost Phys. Core, 5(4), 050–31pp.
Abstract: In this paper we propose ways to incorporate Machine Learning training outputs into a study of statistical significance. We describe these methods in supervised classification tasks using a CNN and a DNN output, and unsupervised learning based on a VAE. As use cases, we consider two physical situations where Machine Learning are often used: high-pT hadronic activity, and boosted Higgs in association with a massive vector boson.
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