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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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Balazs, C. et al, Mamuzic, J., & Ruiz de Austri, R. (2021). A comparison of optimisation algorithms for high-dimensional particle and astrophysics applications. J. High Energy Phys., 05(5), 108–46pp.
Abstract: Optimisation problems are ubiquitous in particle and astrophysics, and involve locating the optimum of a complicated function of many parameters that may be computationally expensive to evaluate. We describe a number of global optimisation algorithms that are not yet widely used in particle astrophysics, benchmark them against random sampling and existing techniques, and perform a detailed comparison of their performance on a range of test functions. These include four analytic test functions of varying dimensionality, and a realistic example derived from a recent global fit of weak-scale supersymmetry. Although the best algorithm to use depends on the function being investigated, we are able to present general conclusions about the relative merits of random sampling, Differential Evolution, Particle Swarm Optimisation, the Covariance Matrix Adaptation Evolution Strategy, Bayesian Optimisation, Grey Wolf Optimisation, and the PyGMO Artificial Bee Colony, Gaussian Particle Filter and Adaptive Memory Programming for Global Optimisation algorithms.
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Ghosh, P., Lara, I., Lopez-Fogliani, D. E., Muñoz, C., & Ruiz de Austri, R. (2018). Searching for left sneutrino LSP at the LHC. Int. J. Mod. Phys. A, 33(18-19), 1850110–62pp.
Abstract: We analyze relevant signals expected at the LHC for a left sneutrino as the lightest supersymmetric particle (LSP). The discussion is carried out in the “mu from nu” supersymmetric standard model (mu nu SSM), where the presence of R-parity breaking couplings involving right-handed neutrinos solves the μproblem and reproduces neutrino data. The sneutrinos are pair produced via a virtual W, Z or gamma in the s channel. From the prompt decay of a pair of left sneutrinos LSPs of any family, a significant diphoton signal plus missing transverse energy (MET) from neutrinos can be present in the mass range 118-132 GeV, with 13 TeV center-of-mass energy and an integrated luminosity of 100 fb(-1). In addition, in the case of a pair of tau left sneutrinos LSPs, given the large value of the tau Yukawa coupling diphoton plus leptons and/or multileptons can appear. We find that the number of expected events for the multilepton signal, together with properly adopted search strategies, is sufficient to give a significant evidence for a sneutrino of mass in the range 130-310 GeV, even with the integrated luminosity of 20 fb(-1). In the case of the signal producing diphoton plus leptons, an integrated luminosity of 100 fb(-1) is needed to give a significant evidence in the mass range 95-145 GeV. Finally, we discuss briefly the presence of displaced vertices and the associated range of masses.
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