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ATLAS Collaboration(Aad, G. et al), Amos, K. R., Aparisi Pozo, J. A., Bailey, A. J., Cabrera Urban, S., Cardillo, F., et al. (2022). A search for an unexpected asymmetry in the production of e(+)mu(-) and e(-)mu(+) pairs in proton-proton collisions recorded by the ATLAS detector at root s=13 TeV. Phys. Lett. B, 830, 137106–22pp.
Abstract: This search, a type not previously performed at ATLAS, uses a comparison of the production cross sections for e(+)mu(-) and e(-)mu(+) pairs to constrain physics processes beyond the Standard Model. It uses 139 fb(-1) of proton-proton collision data recorded at root s = 13 TeV at the LHC. Targeting sources of new physics which prefer final states containing e(+)mu(-) and e(-)mu(+), the search contains two broad signal regions which are used to provide model-independent constraints on the ratio of cross sections at the 2% level. The search also has two special selections targeting supersymmetric models and leptoquark signatures. Observations using one of these selections are able to exclude, at 95% confidence level, singly produced smuons with masses up to 640 GeV in a model in which the only other light sparticle is a neutralino when the R-parity-violating coupling lambda(23)(1)' is close to unity. Observations using the other selection exclude scalar leptoquarks with masses below 1880 GeV when g(1R)(eu) = g(1R)(mu c) = 1, at 95% confidence level. The limit on the coupling reduces to g(1R)(eu) = g(1R)(mu c) = 0.46 for a mass of 1420 GeV.
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Herrero-Garcia, J., Patrick, R., & Scaffidi, A. (2022). A semi-supervised approach to dark matter searches in direct detection data with machine learning. J. Cosmol. Astropart. Phys., 02, 039–19pp.
Abstract: The dark matter sector remains completely unknown. It is therefore crucial to keep an open mind regarding its nature and possible interactions. Focusing on the case of Weakly Interacting Massive Particles, in this work we make this general philosophy more concrete by applying modern machine learning techniques to dark matter direct detection. We do this by encoding and decoding the graphical representation of background events in the XENONnT experiment with a convolutional variational autoencoder. We describe a methodology that utilizes the `anomaly score' derived from the reconstruction loss of the convolutional variational autoencoder as well as a pre-trained standard convolutional neural network, in a semi-supervised fashion. Indeed, we observe that optimum results are obtained only when both unsupervised and supervised anomaly scores are considered together. A data set that has a higher proportion of anomaly score is deemed anomalous and deserves further investigation. Contrary to classical analyses, in principle all information about the events is used, preventing unnecessary information loss. Lastly, we demonstrate the reach of learning-focused anomaly detection in this context by comparing results with classical inference, observing that, if tuned properly, these techniques have the potential to outperform likelihood-based methods.
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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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Centelles Chulia, S., Cepedello, R., & Medina, O. (2022). Absolute neutrino mass scale and dark matter stability from flavour symmetry. J. High Energy Phys., 10(10), 080–23pp.
Abstract: We explore a simple but extremely predictive extension of the scotogenic model. We promote the scotogenic symmetry Z(2) to the flavour non-Abelian symmetry sigma(81), which can also automatically protect dark matter stability. In addition, sigma(81) leads to striking predictions in the lepton sector: only Inverted Ordering is realised, the absolute neutrino mass scale is predicted to be m(lightest)approximate to 7.5x10(-4) eV and the Majorana phases are correlated in such a way that vertical bar m(ee)vertical bar approximate to 0.018 eV. The model also leads to a strong correlation between the solar mixing angle theta(12) and delta(CP), which may be falsified by the next generation of neutrino oscillation experiments. The setup is minimal in the sense that no additional symmetries or flavons are required.
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Pompa, F., Capozzi, F., Mena, O., & Sorel, M. (2022). Absolute nu Mass Measurement with the DUNE Experiment. Phys. Rev. Lett., 129(12), 121802–6pp.
Abstract: Time of flight delay in the supernova neutrino signal offers a unique tool to set model-independent constraints on the absolute neutrino mass. The presence of a sharp time structure during a first emission phase, the so-called neutronization burst in the electron neutrino flavor time distribution, makes this channel a very powerful one. Large liquid argon underground detectors will provide precision measurements of the time dependence of the electron neutrino fluxes. We derive here a new v mass sensitivity attainable at the future DUNE far detector from a future supernova collapse in our galactic neighborhood, finding a sub-eV reach under favorable scenarios. These values are competitive with those expected for laboratory direct neutrino mass searches.
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