Accelerating the BSM interpretation of LHC data with machine learning

The interpretation of Large Hadron Collider (LHC) data in the framework of Beyond the Standard Model (BSM) theories is hampered by the need to run computationally expensive event generators and detector simulators. Performing statistically convergent scans of high-dimensional BSM theories is consequently challenging, and in practice unfeasible for very high-dimensional BSM theories. We present here a new machine learning method that accelerates the interpretation of LHC data, by learning the relationship between BSM theory parameters and data. As a proof-of-concept, we demonstrate that this technique accurately predicts natural SUSY signal events in two signal regions at the High Luminosity LHC, up to four orders of magnitude faster than standard techniques. The new approach makes it possible to rapidly and accurately reconstruct the theory parameters of complex BSM theories, should an excess in the data be discovered at the LHC

Challenges of Profile Likelihood Evaluation in Multi-Dimensional SUSY Scans

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 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.Comment: 21 pages, 9 figures, 1 table; minor changes following referee report. Matches version accepted by JHE

On SUSY GUTs with a degenerate Higgs mass matrix

Certain supersymmetric grand unified models predict that the coefficients of the quadratic terms in the MSSM Higgs potential should be degenerate at the GUT scale. We discuss some examples for such models, and we analyse the implications of this peculiar condition of a GUT-scale degenerate Higgs mass matrix for low-scale MSSM phenomenology. To this end we explore the parameter space which is consistent with existing experimental constraints by means of a Markov Chain Monte Carlo analysis.Comment: 31 pages, 27 figures; v2: typos correcte

Bayesian Fit of Exclusive b→sℓˉℓb \to s \bar\ell\ell Decays: The Standard Model Operator Basis

We perform a model-independent fit of the short-distance couplings $C_{7,9,10}$ within the Standard Model set of $b\to s\gamma$ and $b\to s\bar\ell\ell$ operators. Our analysis of $B \to K^* \gamma$, $B \to K^{(*)} \bar\ell\ell$ and $B_s \to \bar\mu\mu$ decays is the first to harness the full power of the Bayesian approach: all major sources of theory uncertainty explicitly enter as nuisance parameters. Exploiting the latest measurements, the fit reveals a flipped-sign solution in addition to a Standard-Model-like solution for the couplings $C_i$. Each solution contains about half of the posterior probability, and both have nearly equal goodness of fit. The Standard Model prediction is close to the best-fit point. No New Physics contributions are necessary to describe the current data. Benefitting from the improved posterior knowledge of the nuisance parameters, we predict ranges for currently unmeasured, optimized observables in the angular distributions of $B\to K^*(\to K\pi)\,\bar\ell\ell$.Comment: 42 pages, 8 figures; v2: Using new lattice input for f_Bs, considering Bs-mixing effects in BR[B_s->ll]. Main results and conclusion unchanged, matches journal versio

Benchmark data and model independent event classification for the large hadron collider

We describe the outcome of a data challenge conducted as part of the Dark Machines (https://www.darkmachines.org) initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenged aims to detect signals of new physics at the Large Hadron Collider (LHC) using unsupervised machine learning algorithms. First, we propose how an anomaly score could be implemented to define model-independent signal regions in LHC searches. We define and describe a large benchmark dataset, consisting of > 1 billion simulated LHC events corresponding to 10 fb−1 of proton-proton collisions at a center-of-mass energy of 13 TeV. We then review a wide range of anomaly detection and density estimation algorithms, developed in the context of the data challenge, and we measure their performance in a set of realistic analysis environments. We draw a number of useful conclusions that will aid the development of unsupervised new physics searches during the third run of the LHC, and provide our benchmark dataset for future studies at https://www.phenoMLdata.org. Code to reproduce the analysis is provided at https://github.com/bostdiek/DarkMachines-UnsupervisedChallenge

A Profile Likelihood Analysis of the Constrained MSSM with Genetic Algorithms

The Constrained Minimal Supersymmetric Standard Model (CMSSM) is one of the simplest and most widely-studied supersymmetric extensions to the standard model of particle physics. Nevertheless, current data do not sufficiently constrain the model parameters in a way completely independent of priors, statistical measures and scanning techniques. We present a new technique for scanning supersymmetric parameter spaces, optimised for frequentist profile likelihood analyses and based on Genetic Algorithms. We apply this technique to the CMSSM, taking into account existing collider and cosmological data in our global fit. We compare our method to the MultiNest algorithm, an efficient Bayesian technique, paying particular attention to the best-fit points and implications for particle masses at the LHC and dark matter searches. Our global best-fit point lies in the focus point region. We find many high-likelihood points in both the stau co-annihilation and focus point regions, including a previously neglected section of the co-annihilation region at large m_0. We show that there are many high-likelihood points in the CMSSM parameter space commonly missed by existing scanning techniques, especially at high masses. This has a significant influence on the derived confidence regions for parameters and observables, and can dramatically change the entire statistical inference of such scans.Comment: 47 pages, 8 figures; Fig. 8, Table 7 and more discussions added to Sec. 3.4.2 in response to referee's comments; accepted for publication in JHE

SUSY Constraints, Relic Density, and Very Early Universe

The sensitivity of the lightest supersymmetric particle relic density calculation to different cosmological scenarios is discussed. In particular, we investigate the effects of modifications of the expansion rate and of the entropy content in the Early Universe. These effects, even with no observational consequences, can still drastically modify the relic density constraints on the SUSY parameter space. We suggest general parametrizations to evaluate such effects, and derive also constraints from Big-Bang nucleosynthesis. We show that using the relic density in the context of supersymmetric constraints requires a clear statement of the underlying cosmological model assumptions to avoid misinterpretations. On the other hand, we note that combining the relic density calculation with the eventual future discoveries at the LHC will hopefully shed light on the Very Early Universe properties.Comment: 11 pages, 5 figures. v2: new figures adde

The MSSM confronts the precision electroweak data and the muon g-2

We update the electroweak study of the predictions of the Minimal Supersymmetric Standard Model (MSSM) including the recent results on the muon anomalous magnetic moment, the weak boson masses, and the final precision data on the Z boson parameters from LEP and SLC. We find that the region of the parameter space where the slepton masses are a few hundred GeV is favored from the muon g-2 for \tan\beta \ltsim 10, whereas for \tan\beta \simeq 50 heavier slepton mass up to \sim 1000 GeV can account for the reported 3.2 \sigma difference between its experimental value and the Standard Model (SM) prediction. As for the electroweak measurements, the SM gives a good description, and the sfermions lighter than 200 GeV tend to make the fit worse. We find, however, that sleptons as light as 100 to 200 GeV are favored also from the electroweak data, if we leave out the jet asymmetry data that do not agree with the leptonic asymmetry data. We extend the survey of the preferred MSSM parameters by including the constraints from the b \to s \gamma transition, and find favorable scenarios in the minimal supergravity, gauge-, and mirage-mediation models of supersymmetry breaking.Comment: 40 pages, 12 figures. v2: minor changes, references added, version to appear in JHE