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Fiza, N., Khan Chowdhury, N. R., & Masud, M. (2023). Investigating Lorentz Invariance Violation with the long baseline experiment P2O. J. High Energy Phys., 01(1), 076–29pp.
Abstract: One of the basic propositions of quantum field theory is Lorentz invariance. The spontaneous breaking of Lorentz symmetry at a high energy scale can be studied at low energy extensions like the Standard model in a model-independent way through effective field theory (EFT). The present and future Long-baseline neutrino experiments can give a scope to observe such a Planck-suppressed physics of Lorentz invariance violation (LIV). A proposed long baseline experiment, Protvino to ORCA (dubbed “P2O”) with a baseline of 2595 km, is expected to provide good sensitivities to unresolved issues, especially neutrino mass ordering. P2O can offer good statistics even with a moderate beam power and runtime, owing to the very large (similar to 6 Mt) detector volume at KM3NeT/ ORCA. Here we discuss in detail, how the individual LIV parameters affect neutrino oscillations at P2O and DUNE baselines at the level of probability and derive analytical expressions to understand interesting degeneracies and other features. We estimate increment Delta chi(2) sensitivities to the LIV parameters, analyzing their correlations among each other, and also with the standard oscillation parameters. We calculate these results for P2O alone and also carry out a combined analysis of P2O with DUNE. We point out crucial features in the sensitivity contours and explain them qualitatively with the help of the relevant probability expressions derived here. Finally we estimate constraints on the individual LIV parameters at 95% confidence level (C.L.) intervals stemming from the combined analysis of P2O and DUNE datasets, and highlight the improvement over the existing constraints. We also find out that the additional degeneracy induced by the LIV parameter a(ee) around -22 x 10(-23) GeV is lifted by the combined analysis at 95% C.L.
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Cottin, G., Helo, J. C., Hirsch, M., Pena, C., Wang, C. S. A., & Xie, S. (2023). Long-lived heavy neutral leptons with a displaced shower signature at CMS. J. High Energy Phys., 02(2), 011–16pp.
Abstract: We study the LHC discovery potential in the search for heavy neutral leptons (HNL) with a new signature: a displaced shower in the CMS muon detector, giving rise to a large cluster of hits forming a displaced shower. A new Delphes module is used to model the CMS detector response for such displaced decays. We reinterpret a dedicated CMS search for neutral long-lived particles decaying in the CMS muon endcap detectors for the minimal HNL scenario. We demonstrate that this new strategy is particularly sensitive to active-sterile mixings with tau leptons, due to hadronic tau decays. HNL masses between similar to 1-6 GeV can be accessed for mixings as low as vertical bar V-tau N vertical bar(2) similar to 10(-7), probing unique regions of parameter space in the tau sector.
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Bruschini, R., & Gonzalez, P. (2023). chi(c1)(2p): an overshadowed charmoniumlike resonance. J. High Energy Phys., 02(2), 216–23pp.
Abstract: A thorough study of the J(PC )= 1(++) elastic D0 & macr;D*(0) and D+D*(-) scattering, where the form of the meson-meson interaction is inferred from lattice QCD calculations of string breaking, is carried out for center-of-mass energies up to 4 GeV. We show that the presence of chi c1(3872), which can be naturally assigned to either a bound or virtual charmoniumlike state close below the D0 & macr;D*0 threshold, can overshadow a quasiconventional charmoniumlike resonance lying above threshold. This makes difficult the experimental detection of this resonance through the D0 & macr;D*(0) and D+D*(-) channels, despite being its expected main decay modes. We analyze alternative strong and electromagnetic decay modes. Comparison with existing data shows that this resonance may have already been observed through its decay to omega J/psi.
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Caron, S., Ruiz de Austri, R., & Zhang, Z. Y. (2023). Mixture-of-Theories training: can we find new physics and anomalies better by mixing physical theories? J. High Energy Phys., 03(3), 004–37pp.
Abstract: Model-independent search strategies have been increasingly proposed in recent years because on the one hand there has been no clear signal for new physics and on the other hand there is a lack of a highly probable and parameter-free extension of the standard model. For these reasons, there is no simple search target so far. In this work, we try to take a new direction and ask the question: bearing in mind that we have a large number of new physics theories that go beyond the Standard Model and may contain a grain of truth, can we improve our search strategy for unknown signals by using them “in combination”? In particular, we show that a signal hypothesis based on a large, intermingled set of many different theoretical signal models can be a superior approach to find an unknown BSM signal. Applied to a recent data challenge, we show that “mixture-of-theories training” outperforms strategies that optimize signal regions with a single BSM model as well as most unsupervised strategies. Applications of this work include anomaly detection and the definition of signal regions in the search for signals of new physics.
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Gomez Ambrosio, R., ter Hoeve, J., Madigan, M., Rojo, J., & Sanz, V. (2023). Unbinned multivariate observables for global SMEFT analyses from machine learning. J. High Energy Phys., 03(3), 033–66pp.
Abstract: Theoretical interpretations of particle physics data, such as the determination of the Wilson coefficients of the Standard Model Effective Field Theory (SMEFT), often involve the inference of multiple parameters from a global dataset. Optimizing such interpretations requires the identification of observables that exhibit the highest possible sensitivity to the underlying theory parameters. In this work we develop a flexible open source frame-work, ML4EFT, enabling the integration of unbinned multivariate observables into global SMEFT fits. As compared to traditional measurements, such observables enhance the sensitivity to the theory parameters by preventing the information loss incurred when binning in a subset of final-state kinematic variables. Our strategy combines machine learning regression and classification techniques to parameterize high-dimensional likelihood ratios, using the Monte Carlo replica method to estimate and propagate methodological uncertainties. As a proof of concept we construct unbinned multivariate observables for top-quark pair and Higgs+Z production at the LHC, demonstrate their impact on the SMEFT parameter space as compared to binned measurements, and study the improved constraints associated to multivariate inputs. Since the number of neural networks to be trained scales quadratically with the number of parameters and can be fully parallelized, the ML4EFT framework is well-suited to construct unbinned multivariate observables which depend on up to tens of EFT coefficients, as required in global fits.
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