Trotta, R., Johannesson, G., Moskalenko, I. V., Porter, T. A., Ruiz de Austri, R., & Strong, A. W. (2011). Constraints on Cosmic-Ray Propagation Models from a Global Bayesian Analysis. Astrophys. J., 729(2), 106–16pp.
Abstract: Research in many areas of modern physics such as, e. g., indirect searches for dark matter and particle acceleration in supernova remnant shocks rely heavily on studies of cosmic rays (CRs) and associated diffuse emissions (radio, microwave, X-rays, gamma-rays). While very detailed numerical models of CR propagation exist, a quantitative statistical analysis of such models has been so far hampered by the large computational effort that those models require. Although statistical analyses have been carried out before using semi-analytical models (where the computation is much faster), the evaluation of the results obtained from such models is difficult, as they necessarily suffer from many simplifying assumptions. The main objective of this paper is to present a working method for a full Bayesian parameter estimation for a numerical CR propagation model. For this study, we use the GALPROP code, the most advanced of its kind, which uses astrophysical information, and nuclear and particle data as inputs to self-consistently predict CRs, gamma-rays, synchrotron, and other observables. We demonstrate that a full Bayesian analysis is possible using nested sampling and Markov Chain Monte Carlo methods (implemented in the SuperBayeS code) despite the heavy computational demands of a numerical propagation code. The best-fit values of parameters found in this analysis are in agreement with previous, significantly simpler, studies also based on GALPROP.
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Otten, S., Caron, S., de Swart, W., van Beekveld, M., Hendriks, L., van Leeuwen, C., et al. (2021). Event generation and statistical sampling for physics with deep generative models and a density information buffer. Nat. Commun., 12(1), 2985–16pp.
Abstract: Simulating nature and in particular processes in particle physics require expensive computations and sometimes would take much longer than scientists can afford. Here, we explore ways to a solution for this problem by investigating recent advances in generative modeling and present a study for the generation of events from a physical process with deep generative models. The simulation of physical processes requires not only the production of physical events, but to also ensure that these events occur with the correct frequencies. We investigate the feasibility of learning the event generation and the frequency of occurrence with several generative machine learning models to produce events like Monte Carlo generators. We study three processes: a simple two-body decay, the processes e(+)e(-)-> Z -> l(+)l(-) and pp -> tt<mml:mo><overbar></mml:mover> including the decay of the top quarks and a simulation of the detector response. By buffering density information of encoded Monte Carlo events given the encoder of a Variational Autoencoder we are able to construct a prior for the sampling of new events from the decoder that yields distributions that are in very good agreement with real Monte Carlo events and are generated several orders of magnitude faster. Applications of this work include generic density estimation and sampling, targeted event generation via a principal component analysis of encoded ground truth data, anomaly detection and more efficient importance sampling, e.g., for the phase space integration of matrix elements in quantum field theories. Here, the authors report buffered-density variational autoencoders for the generation of physical events. This method is computationally less expensive over other traditional methods and beyond accelerating the data generation process, it can help to steer the generation and to detect anomalies.
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Feroz, F., Cranmer, K., Hobson, M., Ruiz de Austri, R., & Trotta, R. (2011). Challenges of profile likelihood evaluation in multi-dimensional SUSY scans. J. High Energy Phys., 06(6), 042–23pp.
Abstract: Statistical inference of the fundamental parameters of supersymmetric theories is a challenging and active endeavor. Several sophisticated algorithms have been employed to this end. While Markov-Chain Monte Carlo (MCMC) and nested sampling techniques are geared towards Bayesian inference, they have also been used to estimate frequentist confidence intervals based on the profile likelihood ratio. We investigate the performance and appropriate configuration of MULTINEST, a nested sampling based algorithm, when used for profile likelihood-based analyses both on toy models and on the parameter space of the Constrained MSSM. We find that while the standard configuration previously used in the literarture is appropriate for an accurate reconstruction of the Bayesian posterior, the profile likelihood is poorly approximated. We identify a more appropriate MULTINEST configuration for profile likelihood analyses, which gives an excellent exploration of the profile likelihood (albeit at a larger computational cost), including the identification of the global maximum likelihood value. We conclude that with the appropriate configuration MULTINEST is a suitable tool for profile likelihood studies, indicating previous claims to the contrary are not well founded.
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Folgado, M. G., Gomez-Vargas, G. A., Rius, N., & Ruiz de Austri, R. (2018). Probing the sterile neutrino portal to Dark Matter with gamma rays. J. Cosmol. Astropart. Phys., 08(8), 002–20pp.
Abstract: Sterile neutrinos could provide a link between the Standard Model particles and a dark sector, besides generating active neutrino masses via the seesaw mechanism type I. We show that, if dark matter annihilation into sterile neutrinos determines its observed relic abundance, it is possible to explain the Galactic Center gamma-ray excess reported by the Fermi-LAT Collaboration as due to an astrophysical component plus dark matter annihilations. We observe that sterile neutrino portal to dark matter provides an impressively good fit, with a p-value of 0.78 in the best fit point, to the Galactic Center gamma-ray flux, for DM masses in the range (40-80) GeV and sterile neutrino masses 20 GeV less than or similar to M-N < M-DM. Such values are compatible with the limits from Fermi-LAT observations of the dwarfs spheroidal galaxies in the Milky Way halo, which rule out dark matter masses below similar to 50 GeV ( 90 GeV), for sterile neutrino masses M-N less than or similar to MDM ( M-N << M-DM). We also estimate the impact of AMS-02 anti-proton data on this scenario.
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Cabrera, M. E., Casas, J. A., Ruiz de Austri, R., & Trotta, R. (2011). Quantifying the tension between the Higgs mass and (g-2)(mu) in the constrained MSSM. Phys. Rev. D, 84(1), 015006–7pp.
Abstract: Supersymmetry has often been invoked as the new physics that might reconcile the experimental muon magnetic anomaly, a(mu), with the theoretical prediction (basing the computation of the hadronic contribution on e(+)e(-) data). However, in the context of the constrained minimal supersymmetric standard model (CMSSM), the required supersymmetric contributions (which grow with decreasing supersymmetric masses) are in potential tension with a possibly large Higgs mass (which requires large stop masses). In the limit of very large m(h) supersymmetry gets decoupled, and the CMSSM must show the same discrepancy as the standard model with a(mu). But it is much less clear for which size of m(h) does the tension start to be unbearable. In this paper, we quantify this tension with the help of Bayesian techniques. We find that for m(h) >= 125 GeV the maximum level of discrepancy given the current data (similar to 3.2 sigma) is already achieved. Requiring less than 3 sigma discrepancy, implies m(h) less than or similar to 120 GeV. For a larger Higgs mass we should give up either the CMSSM model or the computation of a(mu) based on e(+)e(-); or accept living with such an inconsistency.
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