arXiv subjects

Anders Kvellestad

Publications and source records attributed to Anders Kvellestad.

At least 19 recordsLinked to original sources

arXiv2026

What has the LHC told us about the electroweakino sector of the Minimal Supersymmetric Standard Model?

We perform global fits of the electroweak sector of the Minimal Supersymmetric Standard Model (MSSM) using a comprehensive set of LEP searches, 34 Run 2 LHC searches, and 63 Run 2 LHC measurements. Scanning the bino, wino and Higgsino mass parameters, and the ratio of the Higgs vacuum expectation values, we find that for a light, bino $\tildeχ_{1}^0$, the mass of the next-to-lightest neutralino must be $m_{\tildeχ_{2}^0} \gtrsim 760$ GeV. While MSSM electroweakinos can explain individual excesses observed by ATLAS and CMS in searches targeting compressed spectra, we find no scenarios that fit these excesses simultaneously. When we add a light gravitino, neutralinos are further excluded up to about 1 TeV, though this depends on their composition; Higgsino-dominated $\tildeχ_{1}^0$ requires only $m_{\tildeχ_{1}^0} \approx m_{\tildeχ_{2}^0} \gtrsim 650$ GeV. Lastly, the newer LHC searches and measurements exclude a low-mass region that was preferred in a previous study. This is the most complete summary of collider constraints on the electroweakino sector of the MSSM performed to date.

hep-ph↗

arXiv2026

DAMA/LIBRA and dark matter: decisive tension or contrived cancellation

The ANAIS-112 and COSINE-100 experiments were constructed to test the long-observed dark matter-like annual modulation signal reported by DAMA. While they have reported null results in their annual modulation search, it remains possible that the combined effects of quenching, efficiency, resolution and binning could transform a common nuclear recoil rate into a signal that is visible in some detectors but not others. We assess the tension between DAMA/LIBRA and these latest experiments, under a range of hypotheses ranging from physical to general parameterisations of a common nuclear recoil input. We find that, in the most physically-motivated cases, the tension between DAMA and these other NaI experiments exceeds 5$σ$. Lowering the tension to reasonable values requires significant tuning, such as overfitting with large numbers of free parameters, and opposite-sign modulation between recoil signals on sodium versus iodine.

hep-ph↗

arXiv2026

Bring the noise: exact inference from noisy simulations in collider physics

We rely on Monte Carlo (MC) simulations to interpret searches for new physics at the Large Hadron Collider (LHC) and elsewhere. These simulations result in noisy and approximate estimators of selection efficiencies and likelihoods. In this context we pioneer an exact-approximate computational method - exact-approximate Markov Chain Monte Carlo (MCMC), also known as pseudo-marginal MCMC - that returns exact inferences despite noisy simulations. To do so, we introduce an unbiased estimator for a Poisson likelihood. We demonstrate the new estimator and new techniques in examples based on a search for neutralinos and charginos at the LHC using a simplified model. We find attractive performance characteristics - exact inferences are obtained for a similar computational cost to approximate ones from existing methods and inferences are robust with respect to the number of events generated per point. The unbiased estimator uses a Poisson-distributed number of MC events; it is also possible to construct a biased estimator whose bias decays factorially with increasing number of MC events.

hep-ph↗

arXiv2025

The asymptotic distribution of the likelihood ratio test statistic in two-peak discovery experiments

Likelihood ratio tests are widely used in high-energy physics, where the test statistic is usually assumed to follow a chi-squared distribution with a number of degrees of freedom specified by Wilks' theorem. This assumption breaks down when parameters such as signal or coupling strengths are restricted to be non-negative and their values under the null hypothesis lie on the boundary of the parameter space. Based on a recent clarification concerning the correct asymptotic distribution of the likelihood ratio test statistic for cases where two of the parameters are on the boundary, we revisit the the question of significance estimation for two-peak signal-plus-background counting experiments. In the high-energy physics literature, such experiments are commonly analyzed using Wilks' chi-squared distribution or the one-parameter Chernoff limit. We demonstrate that these approaches can lead to strongly miscalibrated significances, and that the test statistic distribution is instead well described by a chi-squared mixture with weights determined by the Fisher information matrix. Our results highlight the need for boundary-aware asymptotics in the analysis of two-peak counting experiments.

hep-ex↗

arXiv2025

Global fits and the search for new physics: past, present and future

In this work, we review the history and current role of global fits in the search for physics beyond the Standard Model~(BSM), including precision tests of the Standard Model (SM). Although BSM global fits were initially focused on minimal supersymmetric models, we describe how fits have evolved in response to new data from the Large Hadron Collider (LHC) and elsewhere, expanding to encompass a broad spectrum of BSM scenarios including non-minimal supersymmetry, axion-like particles, extended Higgs sectors, dark matter models, and effective field theories such as SMEFT. We discuss how the role of global fits has shifted from forecasting possible signals of new physics at the LHC to understanding the impact of null results from LHC run-I and II and the discovery of the Higgs boson, and how interest has shifted from global fits for parameter estimation to comprehensive model comparison. We close by discussing potential trends and future applications, emphasizing the potential for machine learning and artificial intelligence to enhance the efficiency of sampling algorithms and comparison between theory and experiment, as well as collaboration and software development.

hep-ph↗

arXiv2025

A note on the asymptotic distribution of the Likelihood Ratio Test statistic under boundary conditions

In the context of likelihood ratio testing with parameters on the boundary, we revisit two situations for which there are some discrepancies in the literature: the case of two parameters of interest on the boundary, with all other parameters in the interior, and the case where one of the two parameters on the boundary is a nuisance parameter. For the former case, we clarify that two seemingly conflicting results are consistent upon closer examination. For the latter, we clarify the source of the discrepancy and explain the different findings. As for this case the closed-form expression is valid only under positive correlation, we further propose a heuristic modification to the asymptotic distribution of the likelihood ratio test that extends its applicability to cases involving negative correlation.

math.ST↗

arXiv2024

GPTreeO: An R package for continual regression with dividing local Gaussian processes

We introduce GPTreeO, a flexible R package for scalable Gaussian process (GP) regression, particularly tailored to continual learning problems. GPTreeO builds upon the Dividing Local Gaussian Processes (DLGP) algorithm, in which a binary tree of local GP regressors is dynamically constructed using a continual stream of input data. In GPTreeO we extend the original DLGP algorithm by allowing continual optimisation of the GP hyperparameters, incorporating uncertainty calibration, and introducing new strategies for how the local partitions are created. Moreover, the modular code structure allows users to interface their favourite GP library to perform the local GP regression in GPTreeO. The flexibility of GPTreeO gives the user fine-grained control of the balance between computational speed, accuracy, stability and smoothness. We conduct a sensitivity analysis to show how GPTreeO's configurable features impact the regression performance in a continual learning setting.

cs.LG↗

arXiv2024

Les Houches guide to reusable ML models in LHC analyses

With the increasing usage of machine-learning in high-energy physics analyses, the publication of the trained models in a reusable form has become a crucial question for analysis preservation and reuse. The complexity of these models creates practical issues for both reporting them accurately and for ensuring the stability of their behaviours in different environments and over extended timescales. In this note we discuss the current state of affairs, highlighting specific practical issues and focusing on the most promising technical and strategic approaches to ensure trustworthy analysis-preservation. This material originated from discussions in the LHC Reinterpretation Forum and the 2023 PhysTeV workshop at Les Houches.

hep-ph↗

arXiv2023

Global fits of simplified models for dark matter with GAMBIT I. Scalar and fermionic models with s-channel vector mediators

Simplified models provide a useful way to study the impacts of a small number of new particles on experimental observables and the interplay of those observables, without the need to construct an underlying theory. In this study, we perform global fits of simplified dark matter models with GAMBIT using an up-to-date set of likelihoods for indirect detection, direct detection and collider searches. We investigate models in which a scalar or fermionic dark matter candidate couples to quarks via an s-channel vector mediator. Large parts of parameter space survive for each model. In the case of Dirac or Majorana fermion dark matter, excesses in LHC monojet searches and relic density limits tend to prefer the resonance region, where the dark matter has approximately half the mass of the mediator. A combination of vector and axial-vector couplings to the Dirac candidate also leads to competing constraints from direct detection and unitarity violation.

hep-ph↗

arXiv2023

Simple, but not simplified: A new approach for optimising beyond-Standard Model physics searches at the Large Hadron Collider

Searches for beyond-Standard Model physics scenarios, such as supersymmetry (SUSY), at the Large Hadron Collider (LHC) are frequently optimised on simplified models. After assuming particular particle production and decay processes, analyses are optimised by tuning event selections on benchmark models generated in 2D planes of parameters, with all other parameters held fixed. Motivated by recent evidence that this removes sensitivity to a large volume of viable SUSY models, we propose an alternative approach based on dimensional reduction of global fit results. Starting from the results of a global fit of the 4D electroweak minimal supersymmetric standard model performed by the GAMBIT collaboration, we show how to define a 2D plane by using a variational autoencoder to map points in the original 4D parameter space to a 2D latent space. This allows for easy visualisation of the 4D global fit results, which generates insights into what we may be missing at the LHC. Furthermore, the invertible nature of the map to the 2D plane allows experimentalists to choose and simulate benchmark models in a 2D plane (as they could for a simplified model) whilst still accessing the full range of phenomenological scenarios identified by the global fit in the 4D model. We provide a demonstration of how our visualisation and benchmark model simulation process works, and develop an analysis that is able to exclude four benchmark models not excluded by the ATLAS and CMS collaborations in the set of results used for the global fit.

hep-ph↗

arXiv2023

Collider constraints on electroweakinos in the presence of a light gravitino

Using the GAMBIT global fitting framework, we constrain the MSSM with an eV-scale gravitino as the lightest supersymmetric particle, and the six electroweakinos (neutralinos and charginos) as the only other light new states. We combine 15 ATLAS and 12 CMS searches at 13\,TeV, along with a large collection of ATLAS and CMS measurements of Standard Model signatures. This model, which we refer to as the $\tilde G$-EWMSSM, exhibits quite varied collider phenomenology due to its many permitted electroweakino production processes and decay modes. Characteristic $\tilde G$-EWMSSM signal events have two or more Standard Model bosons and missing energy due to the escaping gravitinos. While much of the $\tilde G$-EWMSSM parameter space is excluded, we find several viable parameter regions that predict phenomenologically rich scenarios with multiple neutralinos and charginos within the kinematic reach of the LHC during Run 3, or the High Luminosity LHC. In particular, we identify scenarios with Higgsino-dominated electroweakinos as light as 140 GeV that are consistent with our combined set of collider searches and measurements. The full set of $\tilde G$-EWMSSM parameter samples and GAMBIT input files generated for this work is available via Zenodo.

hep-ph↗

arXiv2022

Simple and statistically sound recommendations for analysing physical theories

Physical theories that depend on many parameters or are tested against data from many different experiments pose unique challenges to statistical inference. Many models in particle physics, astrophysics and cosmology fall into one or both of these categories. These issues are often sidestepped with statistically unsound ad hoc methods, involving intersection of parameter intervals estimated by multiple experiments, and random or grid sampling of model parameters. Whilst these methods are easy to apply, they exhibit pathologies even in low-dimensional parameter spaces, and quickly become problematic to use and interpret in higher dimensions. In this article we give clear guidance for going beyond these procedures, suggesting where possible simple methods for performing statistically sound inference, and recommendations of readily-available software tools and standards that can assist in doing so. Our aim is to provide any physicists lacking comprehensive statistical training with recommendations for reaching correct scientific conclusions, with only a modest increase in analysis burden. Our examples can be reproduced with the code publicly available at https://doi.org/10.5281/zenodo.4322283.

hep-ph↗

arXiv2022

Data and Analysis Preservation, Recasting, and Reinterpretation

We make the case for the systematic, reliable preservation of event-wise data, derived data products, and executable analysis code. This preservation enables the analyses' long-term future reuse, in order to maximise the scientific impact of publicly funded particle-physics experiments. We cover the needs of both the experimental and theoretical particle physics communities, and outline the goals and benefits that are uniquely enabled by analysis recasting and reinterpretation. We also discuss technical challenges and infrastructure needs, as well as sociological challenges and changes, and give summary recommendations to the particle-physics community.

hep-ph↗

arXiv2021

The GAMBIT Universal Model Machine: from Lagrangians to Likelihoods

We introduce the GAMBIT Universal Model Machine (GUM), a tool for automatically generating code for the global fitting software framework GAMBIT, based on Lagrangian-level inputs. GUM accepts models written symbolically in FeynRules and SARAH formats, and can use either tool along with MadGraph and CalcHEP to generate GAMBIT model, collider, dark matter, decay and spectrum code, as well as GAMBIT interfaces to corresponding versions of SPheno, micrOMEGAs, Pythia and Vevacious (C++). In this paper we describe the features, methods, usage, pathways, assumptions and current limitations of GUM. We also give a fully worked example, consisting of the addition of a Majorana fermion simplified dark matter model with a scalar mediator to GAMBIT via GUM, and carry out a corresponding fit.

hep-ph↗

arXiv2021

Thermal WIMPs and the Scale of New Physics: Global Fits of Dirac Dark Matter Effective Field Theories

We assess the status of a wide class of WIMP dark matter (DM) models in light of the latest experimental results using the global fitting framework $\textsf{GAMBIT}$. We perform a global analysis of effective field theory (EFT) operators describing the interactions between a gauge-singlet Dirac fermion and the Standard Model quarks, the gluons and the photon. In this bottom-up approach, we simultaneously vary the coefficients of 14 such operators up to dimension 7, along with the DM mass, the scale of new physics and several nuisance parameters. Our likelihood functions include the latest data from $\mathit{Planck}$, direct and indirect detection experiments, and the LHC. For DM masses below 100 GeV, we find that it is impossible to satisfy all constraints simultaneously while maintaining EFT validity at LHC energies. For new physics scales around 1 TeV, our results are influenced by several small excesses in the LHC data and depend on the prescription that we adopt to ensure EFT validity. Furthermore, we find large regions of viable parameter space where the EFT is valid and the relic density can be reproduced, implying that WIMPs can still account for the DM of the universe while being consistent with the latest data.

hep-ph↗

arXiv2021

Publishing statistical models: Getting the most out of particle physics experiments

The statistical models used to derive the results of experimental analyses are of incredible scientific value and are essential information for analysis preservation and reuse. In this paper, we make the scientific case for systematically publishing the full statistical models and discuss the technical developments that make this practical. By means of a variety of physics cases -- including parton distribution functions, Higgs boson measurements, effective field theory interpretations, direct searches for new physics, heavy flavor physics, direct dark matter detection, world averages, and beyond the Standard Model global fits -- we illustrate how detailed information on the statistical modelling can enhance the short- and long-term impact of experimental results.

hep-ph↗

arXiv2021

A new software implementation of the Oslo method with rigorous statistical uncertainty propagation

The Oslo method comprises a set of analysis techniques designed to extract nuclear level density and average $γ$-decay strength function from a set of excitation-energy tagged $γ$-ray spectra. Here we present a new software implementation of the entire Oslo method, called OMpy. We provide a summary of the theoretical basis and derive the essential equations used in the Oslo method. In addition to the functionality of the original analysis code, the new implementation includes novel components such as a rigorous method to propagate uncertainties throughout all steps of the Oslo method using a Monte Carlo approach. The resulting level density and $γ$-ray strength function have to be normalized to auxiliary data. The normalization is performed simultaneously for both quantities, thus preserving all correlations. The software is verified by the analysis of a synthetic spectrum and compared to the results of the previous implementation, the oslo-method-software.

physics.comp-ph↗

arXiv2020

$\textsf{Xsec}$: the cross-section evaluation code

The evaluation of higher-order cross-sections is an important component in the search for new physics, both at hadron colliders and elsewhere. For most new physics processes of interest, total cross-sections are known at next-to-leading order (NLO) in the strong coupling $α_s$, and often beyond, via either higher-order terms at fixed powers of $α_s$, or multi-emission resummation. However, the computation time for such higher-order cross-sections is prohibitively expensive, and precludes efficient evaluation in parameter-space scans beyond two dimensions. Here we describe the software tool $\textsf{xsec}$, which allows for fast evaluation of cross-sections based on the use of machine-learning regression, using distributed Gaussian processes trained on a pre-generated sample of parameter points. This first version of the code provides all NLO Minimal Supersymmetric Standard Model strong-production cross-sections at the LHC, for individual flavour final states, evaluated in a fraction of a second. Moreover, it calculates regression errors, as well as estimates of errors from higher-order contributions, from uncertainties in the parton distribution functions, and from the value of $α_s$. While we focus on a specific phenomenological model of supersymmetry, the method readily generalises to any process where it is possible to generate a sufficient training sample.

hep-ph↗