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Bourguille, B., Nieves, J., & Sanchez, F. (2021). Inclusive and exclusive neutrino-nucleus cross sections and the reconstruction of the interaction kinematics. J. High Energy Phys., 04(4), 004–42pp.
Abstract: We present a full kinematic analysis of neutrino-nucleus charged current quasielastic interactions based on the Local Fermi Gas model and the Random Phase Approximation. The model was implemented in the NEUT Monte Carlo framework, which allows us to investigate potentially measurable observables, including hadron distributions. We compare the predictions simultaneously to the most recent T2K and MINERvA charged current (CC) inclusive, CC0 pi and transverse kinematic-imbalance variable results. We pursuit a microscopic interpretation of the relevant reaction mechanisms, with the aim to achieving in neutrino oscillation experiments a correct reconstruction of the incoming neutrino kinematics, free of conceptual biasses. Such study is of the utmost importance for the ambitious experimental program which is underway to precisely determine neutrino properties, test the three-generation paradigm, establish the order of mass eigenstates and investigate leptonic CP violation.
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van Beekveld, M., Caron, S., Hendriks, L., Jackson, P., Leinweber, A., Otten, S., et al. (2021). Combining outlier analysis algorithms to identify new physics at the LHC. J. High Energy Phys., 09(9), 024–33pp.
Abstract: The lack of evidence for new physics at the Large Hadron Collider so far has prompted the development of model-independent search techniques. In this study, we compare the anomaly scores of a variety of anomaly detection techniques: an isolation forest, a Gaussian mixture model, a static autoencoder, and a beta-variational autoencoder (VAE), where we define the reconstruction loss of the latter as a weighted combination of regression and classification terms. We apply these algorithms to the 4-vectors of simulated LHC data, but also investigate the performance when the non-VAE algorithms are applied to the latent space variables created by the VAE. In addition, we assess the performance when the anomaly scores of these algorithms are combined in various ways. Using supersymmetric benchmark points, we find that the logical AND combination of the anomaly scores yielded from algorithms trained in the latent space of the VAE is the most effective discriminator of all methods tested.
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Rinaldi, M. (2017). GPDs at non-zero skewness in ADS/QCD model. Phys. Lett. B, 771, 563–567.
Abstract: We study Generalized Parton Distribution functions (GPDs) usually measured in hard exclusive processes and encoding information on the three dimensional partonic structure of hadrons and their spin decomposition, for non-zeroskewness within the AdS/QCD formalism. To this aim the canonical scheme to calculate GPDs at zero skewness has been properly generalized. Furthermore, we show that the latter quantities, in this non-forwardregime, are sensitive to non-trivialdetails of the hadronic light front wave function, such as a kind of parton correlations usually not accessible in studies of form factors and GPDs at zero skewness.
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