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Researcher profile

Markus Klute

Markus Klute contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
hep-exhep-phMachine Learningphysics.comp-phphysics.ins-det

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Published work

6 published item(s)

preprint2026arXiv

An Optimal Observable Machine for reinterpretable measurements in high-energy physics

A machine-learning-based framework for constructing generator-level observables optimized for parameter extraction in particle physics analyses is introduced, referred to as the Optimal Observable Machine (OOM). Unfoldable differential distributions are learned that maximize sensitivity to a parameter of interest while remaining robust against detector effects, systematic uncertainties, and biases introduced by the unfolding procedure. Detector response and systematic uncertainties are explicitly incorporated into the training through a likelihood-based loss function, enabling a direct optimization of the expected measurement precision while minimizing the bias from any assumption on the parameter of interest itself. The approach is demonstrated in an application to top quark physics, focusing on the measurement of a recently observed pseudoscalar excess at the top quark pair production threshold in dilepton final states. It is shown that a generator-level observable with enhanced sensitivity and long-term reinterpretability can be constructed using this method.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

From Information Geometry to Jet Substructure: A Triality of Cumulant Tensors, Energy Correlators, and Hypergraphs

Pairwise Fisher graphs capture local covariance information, but they cannot distinguish an irreducible multi-observable radiation pattern from a collection of ordinary pairwise correlations. We show that this missing structure is naturally supplied by higher-order Fisher tensors. In a finite basis of binned EECs, ECFs, or EFPs, and in the natural exponential-family coordinates generated by that basis, the same local tensor has three equivalent interpretations: a coefficient in the local Kullback-Leibler expansion, a connected cumulant of the chosen correlator observables, and a signed weight on a hyperedge linking those observables. This gives an exact Fisher-correlator-hypergraph triality in the local exponential-family embedding. The triality provides a direct construction of physics-informed hypergraphs from correlator data. Extending the quadratic Fisher matrix to the first non-trivial higher tensor identifies genuinely connected multi-observable radiation patterns, supplies hyperedge weights for higher-order Laplacians and message passing, and gives a principled criterion for compressing observable bases beyond pairwise information. We develop these constructions and spell out why the exact cumulant interpretation is special to natural exponential-family coordinates. We illustrate the framework in four applications. In a minimal local-KL study, the cubic Fisher tensor reduces the KL truncation error and isolates the dominant triplet structure. In a two-versus-three prong jet substructure benchmark, the hypergraph selector improves compressed-basis classification. In a 33-observable basis-design problem, the Fisher hypergraph retains more third-order local response at twelve observables. A low-capacity learning benchmark then shows how the same Fisher hyperedges can be used as an interpretable inductive bias for message passing on correlator observables.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Layout optimization for the LUXE-NPOD experiment

Beam dump experiments represent an effective way to probe new physics in a parameter space, where new particles have feeble couplings to the Standard Model sector and masses below the GeV scale. The LUXE experiment, designed primarily to study strong-field quantum electrodynamics, can be used also as a photon beam dump experiment with a unique reach for new spin-0 particles in the $10-350~\mathrm{MeV}$ mass and $10^{-6}-10^{-3}~\mathrm{GeV}^{-1}$ couplings to photons ranges. This is achieved via the ``New Physics search with Optical Dump'' (NPOD) concept. While prior estimations were obtained with a simplified model of the experimental setup, in this work we present a systematic study of the new physics reach in the full, realistic experimental apparatus, including an existing detector to be used in the LUXE NPOD context. We furthermore investigate updated scenarios of LUXE's experimental plan and confirm that our results are in agreement with the original estimations of a background-free operation.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

End-to-End Detector Optimization with Diffusion models: A Case Study in Sampling Calorimeters

Recent advances in machine learning have opened new avenues for optimizing detector designs in high-energy physics, where the complex interplay of geometry, materials, and physics processes has traditionally posed a significant challenge. In this work, we introduce the $\textit{end-to-end}$ AI Detector Optimization framework (AIDO) that leverages a diffusion model as a surrogate for the full simulation and reconstruction chain, enabling gradient-based design exploration in both continuous and discrete parameter spaces. Although this framework is applicable to a broad range of detectors, we illustrate its power using the specific example of a sampling calorimeter, focusing on charged pions and photons as representative incident particles. Our results demonstrate that the diffusion model effectively captures critical performance metrics for calorimeter design, guiding the automatic search for layer arrangement and material composition that aligns with known calorimeter principles. The success of this proof-of-concept study provides a foundation for future applications of end-to-end optimization to more complex detector systems, offering a promising path toward systematically exploring the vast design space in next-generation experiments.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Opportunities and Challenges of Standard Model Production Cross Section Measurements at 8 TeV using CMS Open Data

The CMS Open Data project offers new opportunities to measure cross sections of standard model (SM) processes which have not been probed so far. In this work, we evaluate the challenges and the opportunities of the CMS Open Data project in the view of cross-section measurements. In particular, we reevaluate SM cross sections of the production of W bosons, Z bosons, top-quark pairs and WZ dibosons in several decay channels at a center of mass energy of 8 TeV with a corresponding integrated luminosity of 1.8 fb-1. Those cross sections have been previously measured by the ATLAS and CMS collaborations and hence can be used to validate our analysis and calibration strategy. This gives an indication to which precision also new, so far unmeasured cross sections can be determined using CMS Open Data by scientists, who are not a member of the LHC collaborations and hence lack detailed knowledge on experimental and detector related effects and their handling.

0 citations0 reviewsNo code linkedOpen access
preprint2018arXiv

A Roadmap for HEP Software and Computing R&D for the 2020s

Particle physics has an ambitious and broad experimental programme for the coming decades. This programme requires large investments in detector hardware, either to build new facilities and experiments, or to upgrade existing ones. Similarly, it requires commensurate investment in the R&D of software to acquire, manage, process, and analyse the shear amounts of data to be recorded. In planning for the HL-LHC in particular, it is critical that all of the collaborating stakeholders agree on the software goals and priorities, and that the efforts complement each other. In this spirit, this white paper describes the R&D activities required to prepare for this software upgrade.

0 citations0 reviewsNo code linkedOpen access