Khosa, C. K., & Sanz, V. (2023). Anomaly Awareness. SciPost Phys., 15(2), 053–24pp.
Abstract: We present a new algorithm for anomaly detection called Anomaly Awareness. The algorithm learns about normal events while being made aware of the anomalies through a modification of the cost function. We show how this method works in different Particle Physics situations and in standard Computer Vision tasks. For example, we apply the method to images from a Fat Jet topology generated by Standard Model Top and QCD events, and test it against an array of new physics scenarios, including Higgs production with EFT effects and resonances decaying into two, three or four subjets. We find that the algorithm is effective identifying anomalies not seen before, and becomes robust as we make it aware of a varied-enough set of anomalies.
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Figueroa, D. G., Florio, A., Opferkuch, T., & Stefanek, B. (2023). Lattice simulations of non-minimally coupled scalar fields in the Jordan frame. SciPost Phys., 15(3), 077–28pp.
Abstract: The presence of scalar fields with non-minimal gravitational interactions of the form & xi;|& phi;|2R may have important implications for the physics of the early universe. We propose a procedure to solve the dynamics of non-minimally coupled scalar fields directly in the Jordan frame, where the non-minimal couplings are maintained explicitly. Our algorithm can be applied to lattice simulations that include minimally coupled fields and an arbitrary number of non-minimally coupled scalars, with the expansion of the universe sourced by all fields present. This includes situations when the dynamics become fully inhomogeneous, fully non-linear (due to e.g. backreaction or mode rescattering effects), and/or when the expansion of the universe is dominated by non-minimally coupled species. As an example, we study geometric preheating with a non-minimally coupled scalar spectator field when the inflaton oscillates following the end of inflation.
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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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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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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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