arXiv subjects

Sabine Kraml

Publications and source records attributed to Sabine Kraml.

At least 19 recordsLinked to original sources

arXiv2026

OperatorToC++: Transpiling Matched EFT Coefficients to Low-Level Routines

In recent years, significant progress has been made in the development of automated tools that match the parameters of new physics models and the appropriate low-energy Effective Field Theories. This work introduces an extensible, hybrid tool, OperatorToC++, that combines the strengths of Mathematica and C++ to facilitate the next steps beyond the matching. OperatorToC++ efficiently tackles the complexities within the analytical matched expressions such as intricate loop-functions and lengthy sums and products involving tensor objects. It then translates and bundles the results into C++ classes and functions which provide a convenient platform for further numerical analyses. Finally, it offers the possibility of calling the compiled Wilson coefficient methods as Python functions, thus enabling to link them with the vast Python library ecosystem for High Energy Physics workflows.

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arXiv2026

Machine learning fully hadronic events with spectral functions

Characterising fully hadronic events is a difficult task at hadron colliders. Signal jets from the hard process are mingled with an arbitrary number of ISR and FSR jets, leading to a large combinatorial background. This also poses a challenge for machine-learning analyses, where the number of input features is fixed while the jet multiplicity fluctuates from event to event due to QCD radiation. In this work, we explore the use of the two-point correlation spectral function as an input feature for machine-learning analyses of such events. The spectral function maps the transverse-momentum data of an event into a one-dimensional function of the angular distance, encoding the event information modulo collider isometries and jet permutations, and is defined independently of the jet multiplicity. As a concrete benchmark we apply the method to discriminate gluino-pair production followed by $\tilde{g} \to t \bar{t} \tilde{\chi}_1^0$ against the fully hadronic $t \bar{t}$ background. With $139~{\rm fb}^{-1}$ of $\sqrt{s} = 13$ TeV $pp$ collision data, a dense neural network supplied with spectral-function features improves the expected reach in gluino-mass by roughly 150 GeV relative to a recent ATLAS analysis, and by roughly 250 GeV relative to the same network trained on jet kinematics alone.

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arXiv2026

Open LHC Monte Carlo Event Generation

The LHC physics programme involves a vast amount of Monte Carlo event simulation. This paper reviews current efforts towards sharing the generated events as Open Data. Open Event Generation helps reduce duplication of effort and resource consumption, and benefits the whole High Energy Physics community. We give examples of use cases and user experiences, discuss financial and environmental savings, and suggest future directions.

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arXiv2025

On the coverage of electroweak-inos within the pMSSM with SModelS -- a comparison with the ATLAS pMSSM study

The ATLAS collaboration has recently performed a vast scan of the phenomenological Minimal Supersymmetric Standard Model (pMSSM) with a focus on the electroweak-ino sector, and analysed how their Run 2 searches for electroweak production of supersymmetric (SUSY) particles constrain this dataset. All the SLHA files from the scan as well as the constraints from the eight individual searches considered by ATLAS were made publicly available. We use this material to study how well the ATLAS constraints can be reproduced with SModelS v3.0. Moreover, we explore how the picture changes when also including CMS results, and what can be gained by the statistical combination of analyses. Finally, we discuss the part of parameter space with light electroweak-inos that remains valid despite the stringent LHC limits. Our results underscore the need of a broad, multifaceted approach for maximising sensitivity and closing loopholes in the extensive SUSY parameter space.

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arXiv2025

SUSY meets SMEFT: Complete one-loop matching of the general MSSM

We present the complete one-loop matching of the Minimal Supersymmetric Standard Model (MSSM) onto the Standard Model Effective Field Theory (SMEFT), considering the most general case for the MSSM with conserved $R$-parity, which has 124 free parameters. The matching is performed with the Matchete package, which integrates out all superpartners at once with non-degenerate masses, while also retaining the most general flavor structure. Our results include all correlations among the different SMEFT Wilson coefficients that are governed by supersymmetry and thus provide a basis for future systematic and global studies of the MSSM parameter space employing EFT methods. A detailed discussion is provided on the treatment of the Higgs sector and electroweak symmetry breaking, along with the reduction of redundant operators in the EFT Lagrangian to the Warsaw basis. Furthermore, we validate against existing results in the literature and present a minimal phenomenological example. As an alternative low-energy scenario, we also provide the complete one-loop matching of the MSSM onto the two-Higgs-doublet-model EFT, where the second Higgs doublet is retained in the infrared spectrum. Extensive auxiliary material, including the code utilized for the matching, is available on GitHub.

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arXiv2025

t-channel dark matter at the LHC -- a whitepaper

This report, summarising work achieved in the context of the LHC Dark Matter Working Group, investigates the phenomenology of $t$-channel dark matter models, spanning minimal setups with a single dark matter candidate and mediator to more complex constructions closer to UV-complete models. For each considered class of models, we examine collider, cosmological and astrophysical implications. In addition, we explore scenarios with either promptly decaying or long-lived particles, as well as featuring diverse dark matter production mechanisms in the early universe. By providing a unified analysis framework, numerical tools and guidelines, this work aims to support future experimental and theoretical efforts in exploring $t$-channel dark matter models at colliders and in cosmology.

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arXiv2025

Reinterpretation and preservation of data and analyses in HEP

Data from particle physics experiments are unique and are often the result of a very large investment of resources. Given the potential scientific impact of these data, which goes far beyond the immediate priorities of the experimental collaborations that obtain them, it is imperative that the collaborations and the wider particle physics community publish and preserve sufficient information to ensure that this impact can be realised, now and into the future. The information to be published and preserved includes the algorithms, statistical information, simulations and the recorded data. This publication and preservation requires significant resources, and should be a strategic priority with commensurate planning and resource allocation from the earliest stages of future facilities and experiments.

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arXiv2025

Data Preservation in High Energy Physics

Data preservation significantly increases the scientific output of high-energy physics experiments during and after data acquisition. For new and ongoing experiments, the careful consideration of long-term data preservation in the experimental design contributes to improving computational efficiency and strengthening the scientific activity in HEP through Open Science methodologies. This contribution is based on 15 years of experience of the DPHEP collaboration in the field of data preservation and focuses on aspects relevant for the strategic programming of particle physics in Europe: the preparation of future programs using data sets preserved from previous similar experiments (e.g. HERA for EIC), and the use of LHC data long after the end of the data taking. The lessons learned from past collider experiments and recent developments open the way to a number of recommendations for the full exploitation of the investments made in large HEP experiments.

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arXiv2024

SModelS v3: Going Beyond Z2 Topologies

SModelS is a public tool for fast reinterpretation of LHC searches for new physics based on a large database of simplified model results. While previous versions were limited to models with a Z2-type symmetry, such as R-parity conserving supersymmetry, version 3 can now handle arbitrary signal topologies. To this end, the tool was fully restructured and now relies on a graph-based description of simplified model topologies. In this work, we present the main conceptual changes and novel features of SModelS v3, together with the inclusion of new experimental searches for resonant production of spin-1 and spin-0 mediators with decays to quarks or to dark matter. Applying these results to a model containing two mediators, we discuss the interplay of resonance and missing energy searches, and the model's coverage by the currently available simplified model results.

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arXiv2024

Universal New Physics Latent Space

We develop a machine learning method for mapping data originating from both Standard Model processes and various theories beyond the Standard Model into a unified representation (latent) space while conserving information about the relationship between the underlying theories. We apply our method to three examples of new physics at the LHC of increasing complexity, showing that models can be clustered according to their LHC phenomenology: different models are mapped to distinct regions in latent space, while indistinguishable models are mapped to the same region. This opens interesting new avenues on several fronts, such as model discrimination, selection of representative benchmark scenarios, and identifying gaps in the coverage of model space.

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arXiv2024

Revisiting the decoupling limit of the Georgi-Machacek model with a scalar singlet

We study the connection between collider and dark matter phenomenology in the singlet extension of the Georgi-Machacek model. In this framework, the singlet scalar serves as a suitable thermal dark matter (DM) candidate. Our focus lies on the region $v_{\chi}<1$ GeV, where $v_{\chi}$ is the common vacuum expectation value of the neutral components of the scalar triplets of the model. Setting bounds on the model parameters from theoretical, electroweak precision and LHC experimental constraints, we find that the BSM Higgs sector is highly constrained. Allowed values for the masses of the custodial fiveplets, triplets and singlet are restricted to the range $140~ {\rm GeV }< M_{H_5} < 350~ {\rm GeV }$, $150~ {\rm GeV }< M_{H_3} < 270 ~{\rm GeV }$ and $145~ {\rm GeV }< M_{H} < 300~ {\rm GeV }$. The extended scalar sector provides new channels for DM annihilation into BSM scalars that allow to satisfy the observed relic density constraint while being consistent with direct DM detection limits. The allowed region of the parameter space of the model can be explored in the upcoming DM detection experiments, both direct and indirect. In particular, the possible high values of BR$(H^0_5\to\gamma\gamma)$ can lead to an indirect DM signal within the reach of CTA. The same feature also provides the possibility of exploring the model at the High-Luminosity run of the LHC. In a simple cut-based analysis, we find that a signal of about $4\sigma$ significance can be achieved in final states with at least two photons for one of our benchmark points.

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arXiv2024

Global LHC constraints on electroweak-inos with SModelS v2.3

Electroweak-inos, superpartners of the electroweak gauge and Higgs bosons, play a special role in supersymmetric theories. Their intricate mixing into chargino and neutralino mass eigenstates leads to a rich phenomenology, which makes it difficult to derive generic limits from LHC data. In this paper, we present a global analysis of LHC constraints for promptly decaying electroweak-inos in the context of the minimal supersymmetric standard model, exploiting the SModelS software package. Combining up to 16 ATLAS and CMS searches for which electroweak-ino efficiency maps are available in SModelS, we study which combinations maximise the sensitivity in different regions of the parameter space, how fluctuations in the data in individual analyses influence the global likelihood, and what is the resulting exclusion power of the combination compared to the analysis-by-analysis approach.

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arXiv2024

Toward global fits using Higgs STXS data with Lilith

In this talk, we present the program Lilith, a python package for constraining new physics from Higgs measurements. We discuss the usage of signal strength results in the latest published version of Lilith, which allows for constraining deviations from SM Higgs couplings through coupling modifiers. Moreover, we discuss the on-going development to include Higgs STXS data and SMEFT parametrizations in Lilith with the aim of performing global fits of the ATLAS and CMS data. As we point out, detailed information on Standard Model uncertainties and their correlations is important to enable the proper reuse of the experimental results.

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arXiv2023

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.

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arXiv2023

SModelS v2.3: enabling global likelihood analyses

We present version 2.3 of SModelS, a public tool for the fast reinterpretation of LHC searches for new physics on the basis of simplified-model results. The main new features are a database update with the latest available experimental results for full Run 2 luminosity, comprising in particular a large variety of electroweak-ino searches, and the ability to combine likelihoods from different analyses. This enables statistically more rigorous constraints and opens the way for global likelihood analyses for LHC searches. The physics impact is demonstrated for the electroweak-ino sector of the minimal supersymmetric standard model.

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arXiv2022

Machine Learning and LHC Event Generation

First-principle simulations are at the heart of the high-energy physics research program. They link the vast data output of multi-purpose detectors with fundamental theory predictions and interpretation. This review illustrates a wide range of applications of modern machine learning to event generation and simulation-based inference, including conceptional developments driven by the specific requirements of particle physics. New ideas and tools developed at the interface of particle physics and machine learning will improve the speed and precision of forward simulations, handle the complexity of collision data, and enhance inference as an inverse simulation problem.

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arXiv2022

Co-scattering in micrOMEGAs: a case study for the singlet-triplet dark matter model

In scenarios with very small dark matter (DM) couplings and small mass splittings between the DM and other dark sector particles, so-called "co-scattering" or "conversion-driven freeze-out" can be the dominant mechanism for DM production. We present the inclusion of this mechanism in micrOMEGAs together with a case study of the phenomenological implications in the fermionic singlet-triplet model. For the latter, we focus on the transition between co-annihilation and co-scattering processes. We observe that co-scattering is needed to describe the thermal behaviour of the DM for very small couplings, opening up a new region in the parameter space of the model. The triplet states are often long-lived in this region; we therefore also discuss LHC constraints from long-lived signatures obtained with SModelS.

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arXiv2022

Signal region combination with full and simplified likelihoods in MadAnalysis 5

The statistical combination of disjoint signal regions in reinterpretation studies uses more of the data of an analysis and gives more robust results than the single signal region approach. We present the implementation and usage of signal region combination in MadAnalysis 5 through two methods: an interface to the pyhf package making use of statistical models in JSON-serialised format provided by the ATLAS collaboration, and a simplified likelihood calculation making use of covariance matrices provided by the CMS collaboration. The gain in physics reach is demonstrated 1.) by comparison with official mass limits for 4 ATLAS and 5 CMS analyses from the Public Analysis Database of MadAnalysis 5 for which signal region combination is currently available, and 2.) by a case study for an MSSM scenario in which both stops and sbottoms can be produced and have a variety of decays into charginos and neutralinos.

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