arXiv subjects

Markus Klute

Publications and source records attributed to Markus Klute.

At least 19 recordsLinked to original sources

arXiv2026

Design and performance of the Fast Beam Condition Monitor for luminosity and background measurement at the CMS Experiment in LHC Run 3

The Fast Beam Condition Monitor (BCM1F) has been used at the CMS Experiment since the first LHC circulating beams in 2008. Originally meant as a beam-induced background monitor for fast beam losses detection, it showed a potential also for luminosity measurements in 2012 running, and has been used for luminosity measurements since the beginning of Run 2 data taking in 2015 as a part of the Beam Radiation, Instrumentation and Luminosity (BRIL) system. Over the years, the system has undergone various upgrades to the sensors, the front-end and back-end electronics, providing improvements in the precision of the measurements, that remain valid in the higher pileup conditions of LHC Run 3 (2022-2026). Based on the experience of all BCM1F Run 2 upgrades, the detector was completely rebuilt prior to LHC Run 3 using AC-coupled silicon-pad diodes and active cooling. This latest detector version exhibits excellent linearity with instantaneous luminosity and achieves nanosecond-level timing precision, enabling improved systematic corrections for luminosity and background measurements. This paper presents a detailed overview of the detector system for LHC Run 3, including the selection and qualification of sensors as well as a summary of the readout system. It also outlines the processing and calibration strategy for luminosity data, discussing operational hurdles and comparing BCM1F measurements to other CMS luminosity measurements to assess the system's performance as a luminometer. Lastly, the implications for the design of a future luminosity detector to be used in the envisioned HL-LHC upgrade are discussed.

physics.ins-det↗

arXiv2026

QUIVER: Quantum-Informed Views for Enhanced Representations in Large ML Models

Large machine learning models benefit substantially from multimodal inputs that provide a complementary view of the same example. We introduce QUIVER (QUantum-Informed Views for Enhanced Representations, a paradigm that enriches classical data-driven features with a quantum Fisher view: a geometrically motivated, basis-independent summary of higher-order correlations captured by a variational quantum circuit (VQC) trained to perform the same task. Unlike classical feature augmentation, the quantum Fisher information matrix encodes the intrinsic geometry of the learned quantum state manifold. While this feature map, motivated by quantum information theory, is ordinarily non-trivial to model classically, it can surface statistical structure that additional classical data or model capacity finds difficult to learn. This makes the quantum Fisher view a genuinely complementary modality rather than a redundant one. We demonstrate that QUIVER improves standard performance metrics on two benchmark datasets from very different fields: QM9 for predicting molecule properties, and JetClass for predicting jet flavor at the Large Hadron Collider (LHC). The core contribution, however, is domain-agnostic: the quantum Fisher view can be fused into a broad class of model architectures via targeted modifications to the base architecture, to incorporate information about the quantum geometry of the problem. These results demonstrate that quantum-geometric features, extracted from simulated variational circuits, can deliver measurable value for standard machine learning tasks, well before the advent of fault-tolerant quantum hardware.

cs.LG↗

arXiv2026

Novel Strip-like Readout Geometries in Resistive AC-coupled Silicon Detectors (RSD / AC-LGAD)

Resistive Silicon Detectors (RSD/AC-LGAD) are novel silicon detectors capable of both precise spatial and temporal resolution. Such sensors will be essential for the next generation of particle colliders (EIC, FCC-ee, CEPC, FCC-hh) and would enable the possibility of a 4D tracker. RSD sensors are typically fabricated with a pixel-like geometry that provides excellent spatial resolution in the x and y directions. However, in regions further from the interaction point, high spatial resolution in one direction (strip-like geometry) is often preferred to reduce the number of readout channels. For example, strip AC-LGADs are now the default option for the US electron ion collider (EIC). The second production of RSD sensors by Fondazione Bruno Kessler includes sensors with unconventional readout pad shapes that act as a mixture between strip-like and pixel-like readout. This work presents the first characterization of these new pad designs using the Transient Current Technique (TCT). The measurements demonstrate exceptional one-dimensional spatial resolution, confirming the potential of novel strip-like RSDs for future tracking systems.

physics.ins-det↗

arXiv2026

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.

hep-ph↗

arXiv2026

Particle Physics and Gravitational Waves as complementary windows on the Universe

Particle physics and gravitational waves provide complementary probes of the deep structure of the Universe. Gravitational waves from the mergers of neutron stars and black holes are sensitive to the structure of dense quark matter and to different dark matter scenarios. Measurements of stochastic gravitational waves backgrounds can teach us about possible first order phase transitions in the early Universe, including providing sensitivity to the TeV scale which is of key interest to future particle collider experiments. Gravitational waves measurements will also give new probes of the evolution and expansion of the Universe, complementary to measurements with electromagnetic radiation. This Perspectives article explores the physics synergies between the science opportunities provided by next generation gravitational waves measurements and particle physics experiments. Gravitational waves can also probe deep into the early Universe reaching physics much above possible collider energies if the signals can be detected.

astro-ph.CO↗

arXiv2026

Bidirectional Cross-Attention Fusion of High-Resolution RGB and Low-Resolution Hyperspectral Inputs for Multimodal Semantic Segmentation

Multimodal semantic segmentation with heterogeneous sensors must reconcile complementary information across modalities that differ in spatial resolution and channel dimensionality. In particular, high-resolution RGB imaging provides detailed spatial structure but often fails to distinguish visually similar materials, whereas hyperspectral imaging (HSI) provides discriminative spectral signatures but at lower spatial resolution. We present Bidirectional Cross-Attention Fusion (BCAF), which aligns high-resolution RGB with low-resolution HSI at their native grids via localized, bidirectional cross-attention, avoiding pre-upsampling or early spectral collapse. BCAF uses two independent backbones: a standard Swin Transformer for RGB and an HSI-adapted Swin backbone that preserves spectral structure through 3D tokenization with spectral self-attention. Although our evaluation targets RGB-HSI fusion, BCAF is modality-agnostic and applies to co-registered RGB with lower-resolution, high-channel auxiliary sensors. On the benchmark SpectralWaste dataset, BCAF delivers strong performance, achieving 75.4% at 55 images/s. We further evaluate a novel industrial dataset: K3I-Cycling (first RGB subset already released on Fordatis). On this dataset, BCAF reaches 62.3% mIoU for material segmentation (paper, metal, plastic, etc.) and 66.2% mIoU for plastic-type segmentation (PET, PP, HDPE, LDPE, PS, etc.). These results show that preserving native-grid spatial detail and spectral structure improves multimodal segmentation under real-time constraints. Code and model checkpoints are publicly available at https://github.com/jonasvilhofunk/BCAF_2026.

cs.CV↗

arXiv2026

CP violation angles from H$\to\tau\tau$ decays at FCC-ee

Measuring charge-parity (CP) violation in Higgs-fermion interactions is a key target of future precision Higgs programs. The decay $H\to\tau\tau$ is particularly sensitive to the CP structure of the Higgs Yukawa coupling via $\tau$ spin correlation, while the clean environment of $e^+e^-$ collisions at the Future Circular Collider (FCC) enables accurate reconstruction of CP-sensitive observables. In this letter, we study the sensitivity of FCC-ee to the Higgs CP state using $H\to\tau\tau$ events produced in associated $ZH$ production at $\sqrt{s}=240$ GeV. In the anomalous-coupling parametrization, we project a precision of $\Delta\phi_{\tau\tau}=\pm 2.5^\circ$ at 68 % CL, with the dominant contribution arising from one-prong hadronic $\tau$ decays. We further interpret the analysis in the Effective Field Theory framework, deriving expected limits on the CP-odd operators, and compare them with electron and magnetic dipole moment measurements and the anomalous-coupling approach.

hep-ph↗

arXiv2026

Projections of H$\to\tau\tau$ cross-section at FCC-ee

The Future Circular Collider (FCC) stands at the forefront of the European Strategy for Particle Physics as the future flagship project at CERN. The H$\to\tau\tau$ decay, featuring a large branching ratio, clean identification in the FCC-ee environment, and the possibility to reconstruct polarization information, is an excellent channel to measure Higgs boson properties. This work shows the expected precision for the H$\to\tau\tau$ cross-section measurement at the FCC-ee in the ZH production mechanism at $\sqrt{s}=$240 GeV and $\sqrt{s}=$365 GeV, as well as via the vector boson fusion process at $\sqrt{s}=$365 GeV. Furthermore, we explore and evaluate a set of methods for reconstructing tau decays. These techniques are critical for unlocking the full physics potential of the FCC-ee and for improving the understanding of tau-related observables in both Standard Model measurements and New Physics searches. The results obtained significantly enhance the FCC-ee outlook in the H$\to\tau\tau$ channel, improving it by at least an order of magnitude compared to the current sensitivity of measurements' performance at the LHC.

hep-ph↗

arXiv2026

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.

hep-ph↗

arXiv2025

QINNs: Quantum-Informed Neural Networks

Classical deep neural networks can learn rich multi-particle correlations in collider data, but their inductive biases are rarely anchored in physics structure. We propose quantum-informed neural networks (QINNs), a general framework that brings quantum information concepts and quantum observables into purely classical models. While the framework is broad, in this paper, we study one concrete realisation that encodes each particle as a qubit and uses the Quantum Fisher Information Matrix (QFIM) as a compact, basis-independent summary of particle correlations. Using jet tagging as a case study, QFIMs act as lightweight embeddings in graph neural networks, increasing model expressivity and plasticity. The QFIM reveals distinct patterns for QCD and hadronic top jets that align with physical expectations. Thus, QINNs offer a practical, interpretable, and scalable route to quantum-informed analyses, that is, tomography, of particle collisions, particularly by enhancing well-established deep learning approaches.

hep-ph↗

arXiv2025

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.

hep-ex↗

arXiv2025

Irradiation Studies of the Resistive AC-coupled Silicon Detector (RSD/AC-LGAD)

Resistive AC-coupled Silicon Detectors (RSDs) are silicon sensors which provide high temporal and spatial resolution. The RSD is a candidate sensor to be used in future tracking detectors with the objective of obtaining '4D' tracking, where timing information can be used along with spatial hits during track finding. 4D tracking will be an essential part of any future lepton or hadron collider and may even be feasible at the HL-LHC. For applications at hadron colliders, RSD sensors must be able to operate in high fluence environments in order to provide 4D tracking. However, the effects of radiation on RSDs have not been extensively studied. In this study, RSDs were irradiated to $1.0$, $2.0$, and $3.5 \times 10^{15}$~cm$^{-2}$ (1~MeV neutron equivalents) with both protons and neutrons. The sensors were then characterized electrically to study the acceptor removal and, for the first time in this doping concentration range, the donor removal. Then, the Transient Current Technique was used to begin investigating the signal charge sharing after irradiation. The results suggest an interesting trend between acceptor and donor removal, which is worthy of further study and could assist in improving radiation hardness of Low Gain Avalanche Diodes (LGADs).

physics.ins-det↗

arXiv2025

End-to-End Optimal Detector Design with Mutual Information Surrogates

We introduce a novel approach for end-to-end black-box optimization of high energy physics (HEP) detectors using local deep learning (DL) surrogates. These surrogates approximate a scalar objective function that encapsulates the complex interplay of particle-matter interactions and physics analysis goals. In addition to a standard reconstruction-based metric commonly used in the field, we investigate the information-theoretic metric of mutual information. Unlike traditional methods, mutual information is inherently task-agnostic, offering a broader optimization paradigm that is less constrained by predefined targets. We demonstrate the effectiveness of our method in a realistic physics analysis scenario: optimizing the thicknesses of calorimeter detector layers based on simulated particle interactions. The surrogate model learns to approximate objective gradients, enabling efficient optimization with respect to energy resolution. Our findings reveal three key insights: (1) end-to-end black-box optimization using local surrogates is a practical and compelling approach for detector design, providing direct optimization of detector parameters in alignment with physics analysis goals; (2) mutual information-based optimization yields design choices that closely match those from state-of-the-art physics-informed methods, indicating that these approaches operate near optimality and reinforcing their reliability in HEP detector design; and (3) information-theoretic methods provide a powerful, generalizable framework for optimizing scientific instruments. By reframing the optimization process through an information-theoretic lens rather than domain-specific heuristics, mutual information enables the exploration of new avenues for discovery beyond conventional approaches.

cs.LG↗

arXiv2025

1 Particle - 1 Qubit: Particle Physics Data Encoding for Quantum Machine Learning

We introduce 1P1Q, a novel quantum data encoding scheme for high-energy physics (HEP), where each particle is assigned to an individual qubit, enabling direct representation of collision events without classical compression. We demonstrate the effectiveness of 1P1Q in quantum machine learning (QML) through two applications: a Quantum Autoencoder (QAE) for unsupervised anomaly detection and a Variational Quantum Circuit (VQC) for supervised classification of top quark jets. Our results show that the QAE successfully distinguishes signal jets from background QCD jets, achieving superior performance compared to a classical autoencoder while utilizing significantly fewer trainable parameters. Similarly, the VQC achieves competitive classification performance, approaching state-of-the-art classical models despite its minimal computational complexity. Furthermore, we validate the QAE on real experimental data from the CMS detector, establishing the robustness of quantum algorithms in practical HEP applications. These results demonstrate that 1P1Q provides an effective and scalable quantum encoding strategy, offering new opportunities for applying quantum computing algorithms in collider data analysis.

hep-ph↗

arXiv2025

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.

physics.ins-det↗

arXiv2025

A lightweight analysis farm for fundamental physics experiments

Scientific collaborations require a strong computing infrastructure to successfully process and analyze data. While large-scale collaborations have access to resources such as Analysis Facilities, small-scale collaborations often lack the resources to establish and maintain such an infrastructure and instead operate with fragmented analysis environments, resulting in inefficiencies, hindering reproducibility and thus creating additional challenges for the collaboration that are not related to the experiment itself. We present a scalable, lightweight and maintainable Analysis Facility developed for the DARWIN collaboration as an example study case. Grid computing and storage resources are integrated into the facility, allowing for distributed computing and a common entry point for storage. The authentication and authorization infrastructure for all services is token-based, using an Indigo IAM instance. We discuss the architecture of the facility, its provided services, the user experience, and how it can serve as a sustainable blueprint for small-scale collaborations.

hep-ex↗

arXiv2024

Signal partitioning in superfluid ${}^4$He: a Monte Carlo approach

Superfluid ${}^4$He is an ideal candidate for the direct detection of light dark matter via nuclear recoils thanks to its low nuclear mass and the possibility to reach a low detection energy threshold by exploiting the generated quasiparticles. The design of future detectors based on this target, such as the DELight experiment, requires a proper understanding of the formation and partitioning of the signal for different energy depositions from various sources. This work presents an overview of the physical processes involved in the energy deposition of recoiling electrons and ions, and describes a Monte Carlo approach to the partitioning of the signal into different channels. Despite an overall good agreement with existing literature, differences in the region of interest for light dark matter searches below 200 eV are observed.

hep-ex↗

arXiv2024

Searching for type I seesaw mechanism in a two Heavy Neutral Leptons scenario at FCC-ee

This paper investigates the search for heavy neutral leptons (HNL) in the type I seesaw mechanism at the Future Circular Collider in its $e^+e^-$ stage (FCC-ee), considering a luminosity of 125 ab$^{-1}$ collected at $\sqrt{s}=$91.2 GeV. The study examines two generations of heavy neutral leptons produced in association with Standard Model (SM) neutrinos and decaying to a purely leptonic final state. This theoretical framework can explain neutrino oscillations and other open questions of the SM, providing a broader perspective on the relevance of this experimental search. The analysis is performed using a fast simulation of the IDEA detector concept to study potential HNL interactions at the FCC-ee. The sensitivity contours are obtained from a selection of kinematic variables aimed at improving the signal-to-background ratio for the prompt production case. In the case of long-lived HNLs, the background can be almost fully eliminated by exploiting their displaced decay vertices. The study shows that the FCC-ee has a significant sensitivity to observing these objects in a region of the phase space not accessible by other experiments.

hep-ph↗