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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De Romeri, V., & Hirsch, M. (2012). Sneutrino dark matter in low-scale seesaw scenarios. J. High Energy Phys., 12(12), 106–28pp.
Abstract: We consider supersymmetric models in which sneutrinos are viable dark matter candidates. These are either simple extensions of the Minimal Supersymmetric Standard Model with additional singlet superfields, such as the inverse or linear seesaw, or a model with an additional U(1) group. All of these models can accomodate the observed small neutrino masses and large mixings. We investigate the properties of sneutrinos as dark matter candidates in these scenarios. We check for phenomenological bounds, such as correct relic abundance, consistency with direct detection cross section limits and laboratory constraints, among others lepton flavour violating (LFV) charged lepton decays. While inverse and linear seesaw lead to different results for LFV, both models have very similar dark matter phenomenology, consistent with all experimental bounds. The extended gauge model shows some additional and peculiar features due to the presence of an extra gauge boson Z' and an additional light Higgs. Specifically, we point out that for sneutrino LSPs there is a strong constraint on the mass of the Z' due to the experimental bounds on the direct detection scattering cross section.
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