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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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Batra, A., Bharadwaj, P., Mandal, S., Srivastava, R., & Valle, J. W. F. (2023). Phenomenology of the simplest linear seesaw mechanism. J. High Energy Phys., 07(7), 221–48pp.
Abstract: The linear seesaw mechanism provides a simple way to generate neutrino masses. In addition to Standard Model particles, it includes quasi-Dirac leptons as neutrino mass mediators, and a leptophilic scalar doublet seeding small neutrino masses. Here we review its associated physics, including restrictions from theory and phenomenology. The model yields potentially detectable μ-> e gamma rates as well as distinctive signatures in the production and decay of heavy neutrinos ( N-i) and the charged Higgs boson (H-+/-) arising from the second scalar doublet. We have found that production processes such as e(+) e(-) -> NN, e- gamma -> NH- and e(+) e(-) -> H (+) H- followed by the decay chain H-+/--> l(+/-) (i) N, N -> l`(+/-) (j) W (-/+) leads to striking lepton number violation signatures at high energies which may probe the Majorana nature of neutrinos.
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Martin-Luna, P., Gimeno, B., Gonzalez-Iglesias, D., Esperante, D., Blanch, C., Fuster-Martinez, N., et al. (2023). On the Magnetic Field of a Finite Solenoid. IEEE Trans. Magn., 59(4), 7000106–6pp.
Abstract: The magnetostatic field of a finite solenoid with infinitely thin walls carrying a dc current oriented in the azimuthal direction is calculated everywhere in space in terms of complete elliptic integrals by direct integration of the Biot-Savart law. The solution is particularized near the solenoid axis and in the midplane perpendicular to the axis obtaining expressions that agree with some typical approximations that are made in introductory courses of electromagnetism or in the technical literature. The range of validity of these approximations has been studied comparing them with the obtained general expression.
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Cepedello, R., Esser, F., Hirsch, M., & Sanz, V. (2022). Mapping the SMEFT to discoverable models. J. High Energy Phys., 09(9), 229–34pp.
Abstract: The matching of specific new physics scenarios onto the SMEFT framework is a well-understood procedure. The inverse problem, the matching of the SMEFT to UV scenarios, is more difficult and requires the development of new methods to perform a systematic exploration of models. In this paper we use a diagrammatic technique to construct in an automated way a complete set of possible UV models (given certain, well specified assumptions) that can produce specific groups of SMEFT operators, and illustrate its use by generating models with no tree-level contributions to four-fermion (4F) operators. Those scenarios, which only contribute to 4F at one-loop order, can contain relatively light particles that could be discovered at the LHC in direct searches. For this class of models, we find an interesting interplay between indirect SMEFT and direct searches. We discuss some examples on how this interplay would look like when combining low-energy observables with the SMEFT Higgs-fermion analyses and searches for resonance at the LHC.
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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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