arXiv subjects

Matthias Schott

Publications and source records attributed to Matthias Schott.

At least 19 recordsLinked to original sources

arXiv2026

Learning Minimal-Deviation Corrections for Multi-Dimensional Mismodelling in HEP Simulations

Accurate Monte Carlo (MC) modelling in high-energy physics is challenging, particularly in complex scenarios where simulations fail to reproduce observed data. In practice, experimental information is often limited to one-dimensional (1D) distributions, while mismodelling arises in a multidimensional feature space. This restricts traditional correction methods, as one-dimensional reweighting ignores correlations and fully multidimensional approaches require large target datasets. We propose a neural network-based method that operates under these constraints by learning a transformation of simulated events that reproduces the available 1D target distributions while remaining close to the original simulation. This minimal-deviation principle preserves the global correlation structure of the baseline model while enabling targeted corrections of mismodelled features. Using controlled studies with simulated pseudo-data, we show that the method improves agreement with target distributions and maintains a consistent multidimensional structure. The approach is designed for complex, high-dimensional analyses where traditional techniques are insufficient, providing a scalable way to enhance MC modelling under limited information.

cs.LG↗

arXiv2026

Uncovering Hidden Systematics in Neural Network Models for High Energy Physics

Neural networks (NNs) are inherently multidimensional classifiers that learn complex, non-linear relationships among input observables. While their flexibility enables unprecedented performance in high-energy physics (HEP) analyses, it also makes them sensitive to small variations in their inputs. Consequently, the propagation and estimation of systematic uncertainties in NN-based models remain an open challenge. There are indications that uncertainties derived in control regions or from nominal variations of input features can underestimate the true model uncertainty, potentially leaving biases unaccounted for. Inspired by insights from adversarial-attack studies in machine learning, we explore how subtle perturbations, fully consistent with the experimental uncertainties on the input observables, can lead to substantial changes in NN outputs, while keeping the one-dimensional and correlated input distributions nearly unchanged. Using a set of representative HEP tasks, including event classification and object identification, and testing across a variety of network architectures, we demonstrate that networks can be systematically "fooled" at significant rates within the allowed uncertainty envelopes. Building on this observation, we introduce a quantitative framework to probe and measure the hidden sensitivity of neural networks to realistic experimental variations, providing a practical path to evaluate and control their systematic uncertainty in physics analyses.

cs.LG↗

arXiv2026

Transfer Learning Across Fast- and Full-Simulation Domains in High-Energy Physics

Machine-learning models in high-energy physics are often trained on simulated data, where fully simulated samples are computationally expensive while fast simulation provides large statistics at reduced realism. In this work, we systematically study transfer learning between fast-simulated and fully simulated datasets in a realistic LHC environment. We consider three representative tasks, signal-background classification, quark-gluon jet tagging, and missing transverse energy reconstruction, using dense neural networks, graph neural networks, and transformer-based architectures. Models are pretrained on ATLAS-like fast simulation and adapted to CMS-like fast simulation and to fully simulated ATLAS Open Data. Across all tasks, pretrained models consistently outperform independently trained baselines and require significantly less target-domain training data, typically reducing the needed statistics by about a factor of two. These results demonstrate that fast simulation can be used to learn robust, reusable representations and motivate publishing trained models as reusable scientific assets beyond large foundation models.

cs.LG↗

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

Global detector network to search for high-frequency gravitational waves (GravNet): conceptual design

We propose GravNet (Global detector network to search for high-frequency gravitational waves), a novel experimental scheme enabling the search for gravitational waves in the MHz to GHz frequency range. Such high-frequency gravitational waves could arise from a variety of phenomena connected to some of the most pressing and fundamental questions in modern cosmology. The GravNet concept is based on synchronous measurements of signals from multiple experimental measurement devices operating at geographically separated locations. While gravitational-wave-induced signatures may be present in the signal of a single detector, distinguishing them from instrumental or environmental noise is highly challenging. By analyzing correlations between signals from several distant detectors, the detection significance is substantially enhanced, while simultaneously enabling studies of the nature and origin of the gravitational-wave signal. In this work, we discuss the GravNet concept specifically in the context of cavities operated in strong magnetic fields, as these currently represent the most technically mature and experimentally advanced realization of the scheme. As part of this proposal, a first demonstration experiment using a non-superconducting cavity has been performed, providing the basis for the data-analysis strategies discussed in this work. Finally, we outline the prospects and future development of GravNet as a global network for high-frequency gravitational-wave searches.

astro-ph.IM↗

arXiv2026

Shapes are not enough: CONSERVAttack and its use for finding vulnerabilities and uncertainties in machine learning applications

In High Energy Physics, as in many other fields of science, the application of machine learning techniques has been crucial in advancing our understanding of fundamental phenomena. Increasingly, deep learning models are applied to analyze both simulated and experimental data. In most experiments, a rigorous regime of testing for physically motivated systematic uncertainties is in place. The numerical evaluation of these tests for differences between the data on the one side and simulations on the other side quantifies the effect of potential sources of mismodelling on the machine learning output. In addition, thorough comparisons of marginal distributions and (linear) feature correlations between data and simulation in "control regions" are applied. However, the guidance by physical motivation, and the need to constrain comparisons to specific regions, does not guarantee that all possible sources of deviations have been accounted for. We therefore propose a new adversarial attack - the CONSERVAttack - designed to exploit the remaining space of hypothetical deviations between simulation and data after the above mentioned tests. The resulting adversarial perturbations are consistent within the uncertainty bounds - evading standard validation checks - while successfully fooling the underlying model. We further propose strategies to mitigate such vulnerabilities and argue that robustness to adversarial effects must be considered when interpreting results from deep learning in particle physics.

cs.LG↗

arXiv2026

First Axion Search Results of the SUPAX Prototype Experiment

The SUPerconduction AXion search experiment (Supax) is a future haloscope-type detector designed to probe axion-like particles (ALPs) as candidates for dark matter and solutions to the strong-CP problem in the mass range between $8\,\mu$eV and $30\,\mu$eV. In the course of the preparation of Supax, a prototype experiment was built and operated. Using a copper cavity, cooled down to a temperature of 2 K and operated in a magnetic field of 12 T, we probe axion masses around $34\,\mu$eV and exclude axion-photon couplings down to $|g_{a\gamma\gamma}|> 1.6\cdot 10^{-13}$GeV$^{-1}$. The data was also used to exclude dark photons in the same mass range with a kinetic mixing parameter of $\chi > 1.4\cdot 10^{-12}$. Details of the experimental setup and the analysis strategy are summarized in this paper.

hep-ex↗

arXiv2025

MiniFool -- Physics-Constraint-Aware Minimizer-Based Adversarial Attacks in Deep Neural Networks

In this paper, we present a new algorithm, MiniFool, that implements physics-inspired adversarial attacks for testing neural network-based classification tasks in particle and astroparticle physics. While we initially developed the algorithm for the search for astrophysical tau neutrinos with the IceCube Neutrino Observatory, we apply it to further data from other science domains, thus demonstrating its general applicability. Here, we apply the algorithm to the well-known MNIST data set and furthermore, to Open Data data from the CMS experiment at the Large Hadron Collider. The algorithm is based on minimizing a cost function that combines a $\chi^2$ based test-statistic with the deviation from the desired target score. The test statistic quantifies the probability of the perturbations applied to the data based on the experimental uncertainties. For our studied use cases, we find that the likelihood of a flipped classification differs for both the initially correctly and incorrectly classified events. When testing changes of the classifications as a function of an attack parameter that scales the experimental uncertainties, the robustness of the network decision can be quantified. Furthermore, this allows testing the robustness of the classification of unlabeled experimental data.

cs.LG↗

arXiv2025

Letter of Intent: The Forward Physics Facility

The Forward Physics Facility (FPF) is a proposed extension of the HL-LHC program designed to exploit the unique scientific opportunities offered by the intense flux of high energy neutrinos, and possibly new particles, in the far-forward direction. Located in a well-shielded cavern 627 m downstream of one of the LHC interaction points, the facility will support a broad and ambitious physics program that significantly expands the discovery potential of the HL-LHC. Equipped with four complementary detectors -- FLArE, FASER$\nu$2, FASER2, and FORMOSA -- the FPF will enable breakthrough measurements that will advance our understanding of neutrino physics, quantum chromodynamics, and astroparticle physics, and will search for dark matter and other new particles. With this Letter of Intent, we propose the construction of the FPF cavern and the construction, integration, and installation of its experiments. We summarize the physics case, the facility design, the layout and components of the detectors, as well as the envisioned collaboration structure, cost estimate, and implementation timeline.

hep-ex↗

arXiv2025

Latest neutrino results from the FASER experiment and their implications for forward hadron production

The muon puzzle -- an excess of muons relative to simulation predictions in ultra-high-energy cosmic-ray air showers -- has been reported by many experiments. This suggests that forward particle production in hadronic interactions is not fully understood. Some of the scenarios proposed to resolve this predict reduced production of forward neutral pions and enhanced production of forward kaons (or other particles). The FASER experiment at the LHC is located 480 m downstream of the ATLAS interaction point and is sensitive to neutrinos and muons, which are the decay products of forward charged pions and kaons. In this study, the latest measurements of electron and muon neutrino fluxes are presented using the data corresponding to 9.5 $\mathrm{fb^{-1}}$ and 65.6 $\mathrm{fb^{-1}}$ of proton-proton collisions with $\sqrt{s}=13.6~\mathrm{TeV}$ by the FASER$\nu$ and the FASER electronic detector, respectively. These fluxes are compared with predictions from recent hadronic interaction models, including EPOS-LHCr, SIBYLL 2.3e, and QGSJET 3. The predictions are generally consistent with the measured fluxes from FASER, although some discrepancies appear in certain energy bins. More precise flux measurements with additional data will follow soon, enabling validation of pion, kaon, and charm meson production with finer energy binning, reduced uncertainties, and multi-differential analyses.

hep-ex↗

arXiv2025

Modeling Uncertainties on the Z Boson Background in the Context of High Precision W Boson Mass Measurements

The precise determination of the $W$ boson mass, $m_W$, is a cornerstone of electroweak precision tests and a critical input to global fits of the Standard Model (SM). While most existing measurements of $m_W$ are in agreement with SM predictions, the recent result from the CDF collaboration exhibits a significant deviation from the SM prediction but also exceeding $4\sigma$ from the global average of all other high precision measurements. This prompts renewed scrutiny of potential sources of systematic bias. In this study, we investigate the modeling of the $Z$ boson background, which is particularly relevant in the muon decay channel of the $W$ boson, where undetected muons can mimic missing transverse momentum. Using updated theoretical predictions and high-precision simulations, we construct reweighted templates to evaluate the impact of possible mismodeling in the $Z$ background. Our analysis suggests that such effects can induce a shift in the extracted $m_W$ value of up to 8~MeV in the muon decay channel. While this shift is non-negligible and exceeds the originally quoted uncertainty from this source, it remains insufficient to explain the full discrepancy observed in the CDF measurement.

hep-ex↗

arXiv2025

Status of the W boson mass and the future of the electroweak fit in the next decades

A precise determination of the W boson mass is an essential test for the Standard Model of particle physics: the comparison of experimental value and theoretical prediction allows to probe the internal consistency of the electroweak sector and could possibly highlight signals of New Physics. We provide a concise and up-to-date summary of past and recent measurements at lepton and hadron colliders, a discussion of the known perturbative and non-perturbative theoretical ingredients used to provide predictions for the relevant observables, and an overview of future prospects to reduce systematic uncertainties and to compare different measurements in a consistent way. We conclude with a brief discussion on the relevance of the global electroweak fit at present and future colliders.

hep-ph↗

arXiv2025

Conceptual Design Report of the SUPAX Experiment

The SUPerconduction AXion search experiment (Supax) is a haloscope designed to probe axion-like particles (ALPs) as candidates for dark matter and solutions to the strong CP problem. ALPs are predicted to couple to photons, allowing their detection through resonant conversion in electromagnetic cavities placed within strong magnetic fields. \Supax employs a 12 T magnetic field and tunable superconducting cavities with resonance frequencies ranging from 2\,GHz to 7.2\,GHz, enabling the exploration of axion masses between 8\,$\mu$eV and 30\,$\mu$eV. The tuning mechanism, based on piezo motors and gas-pressure regulation, allows for simultaneous scanning of up to three frequencies, significantly improving search efficiency. This paper presents the technical design of the Supax experiment, preliminary R\&D efforts, and results from prototype experiments. In particular, we exclude dark photons with masses around $35\,\mu$eV with a kinetic mixing parameter $\chi > 5\cdot 10^{-14}$, i.e. a region of parameter space which has not been previously explored.

hep-ex↗

arXiv2025

Prospects and Opportunities with an upgraded FASER Neutrino Detector during the HL-LHC era: Input to the EPPSU

The FASER experiment at CERN has opened a new window in collider neutrino physics by detecting TeV-energy neutrinos produced in the forward direction at the LHC. Building on this success, this document outlines the scientific case and design considerations for an upgraded FASER neutrino detector to operate during LHC Run 4 and beyond. The proposed detector will significantly enhance the neutrino physics program by increasing event statistics, improving flavor identification, and enabling precision measurements of neutrino interactions at the highest man-made energies. Key objectives include measuring neutrino cross sections, probing proton structure and forward QCD dynamics, testing lepton flavor universality, and searching for beyond-the-Standard Model physics. Several detector configurations are under study, including high-granularity scintillator-based tracking calorimeters, high-precision silicon tracking layers, and advanced emulsion-based detectors for exclusive event reconstruction. These upgrades will maximize the physics potential of the HL-LHC, contribute to astroparticle physics and QCD studies, and serve as a stepping stone toward future neutrino programs at the Forward Physics Facility.

hep-ex↗

arXiv2025

Introduction to the Usage of Open Data from the Large Hadron Collider for Computer Scientists in the Context of Machine Learning

Deep learning techniques have evolved rapidly in recent years, significantly impacting various scientific fields, including experimental particle physics. To effectively leverage the latest developments in computer science for particle physics, a strengthened collaboration between computer scientists and physicists is essential. As all machine learning techniques depend on the availability and comprehensibility of extensive data, clear data descriptions and commonly used data formats are prerequisites for successful collaboration. In this study, we converted open data from the Large Hadron Collider, recorded in the ROOT data format commonly used in high-energy physics, to pandas DataFrames, a well-known format in computer science. Additionally, we provide a brief introduction to the data's content and interpretation. This paper aims to serve as a starting point for future interdisciplinary collaborations between computer scientists and physicists, fostering closer ties and facilitating efficient knowledge exchange.

cs.LG↗

arXiv2025

Enforcing Fundamental Relations via Adversarial Attacks on Input Parameter Correlations

Correlations between input parameters play a crucial role in many scientific classification tasks, since these are often related to fundamental laws of nature. For example, in high energy physics, one of the common deep learning use-cases is the classification of signal and background processes in particle collisions. In many such cases, the fundamental principles of the correlations between observables are often better understood than the actual distributions of the observables themselves. In this work, we present a new adversarial attack algorithm called Random Distribution Shuffle Attack (RDSA), emphasizing the correlations between observables in the network rather than individual feature characteristics. Correct application of the proposed novel attack can result in a significant improvement in classification performance - particularly in the context of data augmentation - when using the generated adversaries within adversarial training. Given that correlations between input features are also crucial in many other disciplines. We demonstrate the RDSA effectiveness on six classification tasks, including two particle collision challenges (using CERN Open Data), hand-written digit recognition (MNIST784), human activity recognition (HAR), weather forecasting (Rain in Australia), and ICU patient mortality (MIMIC-IV), demonstrating a general use case beyond fundamental physics for this new type of adversarial attack algorithms.

cs.LG↗

arXiv2024

Study of NbN as superconducting material for the usage in superconducting radio frequency cavities

A new axion-haloscope is setup at the Johannes Gutenberg university of Mainz, named the Supax (a SUPerconducting AXion search) experiment. This setup is used to characterize the behaviour of a NbN coated superconducting cavity in a 2.5T strong magnetic field, at a resonance frequency of 8.4GHz. We observe an increasing surface resistance with increasing magnetic field, leading to a decreasing quality factor. The behaviour is similar to that of previously studied cavities using Nb3Tn.

cond-mat.supr-con↗

arXiv2024

First Measurement of the Muon Neutrino Interaction Cross Section and Flux as a Function of Energy at the LHC with FASER

This letter presents the measurement of the energy-dependent neutrino-nucleon cross section in tungsten and the differential flux of muon neutrinos and anti-neutrinos. The analysis is performed using proton-proton collision data at a center-of-mass energy of $13.6 \, {\rm TeV}$ and corresponding to an integrated luminosity of $(65.6 \pm 1.4) \, \mathrm{fb^{-1}}$. Using the active electronic components of the FASER detector, $338.1 \pm 21.0$ charged current muon neutrino interaction events are identified, with backgrounds from other processes subtracted. We unfold the neutrino events into a fiducial volume corresponding to the sensitive regions of the FASER detector and interpret the results in two ways: We use the expected neutrino flux to measure the cross section, and we use the predicted cross section to measure the neutrino flux. Both results are presented in six bins of neutrino energy, achieving the first differential measurement in the TeV range. The observed distributions align with Standard Model predictions. Using this differential data, we extract the contributions of neutrinos from pion and kaon decays.

hep-ex↗