arXiv subjects

Johannes Erdmann

Publications and source records attributed to Johannes Erdmann.

At least 19 recordsLinked to original sources

arXiv2026

Effects of near-surface sedimentary structure on Newtonian noise for the Einstein Telescope: a 2-D numerical study

Near-surface low-velocity sediments can strongly modify seismic wavefields and therefore affect estimates of Newtonian noise at underground gravitational-wave observatories. We investigate these effects using 2-D viscoelastic simulations of a sediment layer overlying hard-rock basement. Controlled experiments examine the influence of sediment-basement interface geometry, sediment shear-wave velocity, attenuation, and test-mass position. Relative to a homogeneous model, the sediment layer produces frequency-dependent changes through wave trapping, interference, and attenuation. A constant-thickness layer generates a more laterally coherent wavefield and a sharper spectral enhancement than a basin-shaped interface, whereas lateral thickness variations broaden and shift the response. Sediment shear-wave velocity primarily controls the frequencies of the spectral features, while attenuation mainly controls their amplitudes. Newtonian noise is most sensitive to sediment structure for test masses located within or several hundred meters below the sediment layer. The sensitivity to lateral variations in sediment thickness decreases with burial depth and is weak at 2 km depth in the present model. These results demonstrate that near-surface sedimentary structure should be represented explicitly when assessing site-dependent Newtonian noise, particularly for test masses located between 200 m and 300 m depths.

astro-ph.IM↗

arXiv2026

Optimization and robustness of cost-efficient seismic arrays for Newtonian noise cancellation at the Einstein Telescope

Newtonian noise is expected to be the dominating noise source for low frequencies at the Einstein Telescope. It originates from seismic waves that cause density fluctuations in the rock around the interferometer. The mitigation strategy for Newtonian noise relies on an array of seismometers, placed at depth in boreholes, which provides measurements of the seismic wave field. We optimize the positions of the individual seismometers for the mitigation capabilities of the array for a full corner of the Einstein Telescope. We find that the mitigation capabilities of arrays with multiple seismometers in each borehole match the capabilities of only somewhat smaller arrays but with only one seismometer per borehole. Mitigation is further improved by extending the array with seismometers in the interferometer tunnels. Such configurations may hence provide a cost-effective way towards realizing an efficient seismic array. In each case, we quantify the broadband mitigation performance in the range from 1 to 10 Hz for arrays that are optimized for a frequency of 10 Hz, as well as the robustness of the arrays with respect to variations from their optimized positions. We find that larger arrays with several seismometers per borehole and additional seismometers in the tunnels provide promising broadband performance above 3 to 4 Hz and that such arrays are particularly stable against variations in the seismometer positions with mitigation factors $>6$ for an array of 20 boreholes with 3 seismometers each and $>15$ for a large array of 50 boreholes with 10 seismometers.

astro-ph.IM↗

arXiv2026

NNNN: Neural Networks for Newtonian Noise Mitigation at the Einstein Telescope

The gravitational effects of seismic waves, so-called Newtonian noise, will likely limit the low-frequency sensitivity of future ground-based gravitational wave detectors, such as the Einstein Telescope. It has been proposed to mitigate this noise source by predicting it from measurements of the surrounding seismic displacement field using an array of seismometers. In this paper, we investigate the Newtonian noise prediction abilities of neural networks based on synthetic data from such seismometer arrays and compare the results with the Wiener filter as benchmark. We developed a simulation that generates density fluctuations of random plane waves and Gaussian wave packets, and that calculates the resulting Newtonian noise and displacement field. We investigate the performance on approximately stationary wave fields and single dominating long- and short-term events. For the first case, we observe comparable performance of neural networks and the Wiener filter with the networks performing slightly better. For the second case, however, we find that convolutional neural networks and graph neural networks can outperform the Wiener filter by factors of 15-80, depending on the frequency and the array configuration, and that they can reduce the corresponding Newtonian noise amplitude spectral density by factors of 10-30.

astro-ph.IM↗

arXiv2026

Enabling users to work sustainably on shared institute computing resources

The VISPA project is a self-managed, mid-scale computing cluster that supports physics data analysis in research and teaching. Because the cluster is housed in a 1970s institute building with limited retrofit options, conventional efficiency upgrades would yield only minor energy savings. We therefore target sustainability primarily through user-centric measures. A monitoring system now records per-job energy consumption, while real-time data on the renewable share of the German power grid enable `green-window' scheduling. Users can query their individual energy consumption and carbon footprints, receive weekly reports, and tag jobs by project for aggregate accounting; memory records from previous runs help avoid oversubscription. All options are voluntary, fostering a cultural shift rather than imposing hard constraints. A simulation framework evaluates the potential impact of these measures. Together, the technological and behavioral interventions aim at medium- to long-term reductions in greenhouse-gas emissions by increasing resource awareness within the scientific community.

cs.HC↗

arXiv2026

A numerical framework for Newtonian-noise estimation at the Einstein Telescope: 2-D simulations beyond the plane-wave approximation

The Einstein Telescope (ET) is a third-generation underground gravitational-wave observatory designed to extend the detection sensitivity down to a few Hertz. Newtonian noise is expected to limit the low-frequency sensitivity of ET, particularly in the 3-15 Hz band. Most existing estimates rely on analytical or semi-analytical models assuming homogeneous or layered media, neglecting geological heterogeneity and complex wave interactions. In this work, we present a numerical framework for Newtonian-noise estimation based on spectral-element simulations of a seismic wave field. As a proof of concept, we first benchmark the numerical results against analytical plane-wave predictions in a two-dimensional homogeneous medium with a single surface source, demonstrating excellent agreement for both bulk and cavern contributions. We then extend the model to an array of 30 stochastic surface sources to approximate stationary ambient seismic excitation. The P-wave fraction inferred from the simulated wave field is, in this simple homogeneous case, significantly lower than commonly assumed, indicating enhanced prospects for Newtonian-noise mitigation. The framework is readily applicable to three-dimensional simulations and to integration of detailed local seismic models and topography, offering strong potential for site-specific Newtonian-noise estimation.

astro-ph.IM↗

arXiv2026

Shaping the Digital Future of ErUM Research: Sustainability & Ethics

This workshop report from "Shaping the Digital Future of ErUM Research: Sustainability & Ethics" (Aachen, 2025) reviews progress on sustainability measures in data-intensive ErUM-Data research since the 2023 call-to-action on resource-aware research. It evaluates short-, medium-, and long-term actions around monitoring and reducing CO2 emissions, improving data and software FAIRness, optimizing workflows and computing infrastructures, and aligning operations with low-carbon energy availability, including concepts such as "breathing" computing centers, long-term data storage strategies, and software efficiency certification. The report stresses the need for systematic teaching, training, mentoring, and new support formats to establish sustainable coding and computing practices, particularly among students and early-career researchers, and highlights the importance of dedicated steering and funding instruments to embed sustainability in project planning. Ethical discussions focus on the transformative use of AI in ErUM-Data, addressing autonomy, bias, transparency, explainability, attribution of responsibility, and the risk of deskilling, while reaffirming that accountability for scientific outcomes remains with human researchers. Finally, the report emphasizes that sustainable transformation requires not only technical measures but also targeted awareness-building, communication strategies, incentives, and community-driven initiatives to move from awareness to action and to integrate sustainability and ethics into everyday scientific practice.

physics.comp-ph↗

arXiv2026

Learning to bin: differentiable and Bayesian optimization for multi-dimensional discriminants in high-energy physics

Categorizing events using discriminant observables is central to many high-energy physics analyses. Yet, bin boundaries are often chosen by hand. A simple, popular choice is to apply argmax projections of multi-class scores and equidistant binning of one-dimensional discriminants. We propose a binning optimization for signal significance directly in multi-dimensional discriminants. We use a Gaussian Mixture Model (GMM) to define flexible bin boundary shapes for multi-class scores, while in one dimension (binary classification) we move bin boundaries directly. On this binning model, we study two optimization strategies: a differentiable and a Bayesian optimization approach. We study two toy setups: a binary classification and a three-class problem with two signals and backgrounds. In the one-dimensional case, both approaches achieve similar gains in signal sensitivity compared to equidistant binnings for a given number of bins. In the multi-dimensional case, the GMM-based binning defines sensitive categories as well, with the differentiable approach performing best. We show that, in particular for limited separability of the signal processes, our approach outperforms argmax classification even with optimized binning in the one-dimensional projections. Both methods are released as lightweight Python plugins intended for straightforward integration into existing analyses.

physics.data-an↗

arXiv2025

ParaFlow: fast calorimeter simulations parameterized in upstream material configurations

We study whether machine-learning models for fast calorimeter simulations can learn meaningful representations of calorimeter signatures that account for variations in the full particle detector's configuration. This may open new opportunities in high-energy physics measurements, for example in the assessment of systematic uncertainties that are related to the detector geometry, in the inference of properties of the detector configuration, or in the automated design of experiments. As a concrete example, we parameterize normalizing-flow-based simulations in configurations of the material upstream of a toy calorimeter. We call this model ParaFlow, which is trained to interpolate between different material budgets and positions, as simulated with Geant4. We study ParaFlow's performance in terms of photon shower shapes that are directly influenced by the properties of the upstream material, in which photons can convert to an electron-positron pair. In general, we find that ParaFlow is able to reproduce the dependence of the shower shapes on the material properties at the few-percent level with larger differences only in the tails of the distributions.

physics.ins-det↗

arXiv2025

KAN we improve on HEP classification tasks? Kolmogorov-Arnold Networks applied to an LHC physics example

Recently, Kolmogorov-Arnold Networks (KANs) have been proposed as an alternative to multilayer perceptrons, suggesting advantages in performance and interpretability. We study a typical binary event classification task in high-energy physics including high-level features and comment on the performance and interpretability of KANs in this context. Consistent with expectations, we find that the learned activation functions of a one-layer KAN resemble the univariate log-likelihood ratios of the respective input features. In deeper KANs, the activations in the first layer differ from those in the one-layer KAN, which indicates that the deeper KANs learn more complex representations of the data, a pattern commonly observed in other deep-learning architectures. We study KANs with different depths and widths and we compare them to multilayer perceptrons in terms of performance and number of trainable parameters. For the chosen classification task, we do not find that KANs are more parameter efficient. However, small KANs may offer advantages in terms of interpretability that come at the cost of only a moderate loss in performance.

hep-ph↗

arXiv2025

Fighting Newtonian Noise with Gradient-Based Optimization at the Einstein Telescope

Newtonian noise in gravitational wave detectors originates from density fluctuations in the adjacency of the interferometer mirrors. At the Einstein Telescope, this noise source is expected to be dominant for low frequencies. Its impact is proposed to be reduced with the help of an array of seismometers that will be placed around the interferometer endpoints. We reformulate and implement the problem of finding the optimal seismometer positions in a differentiable way. We then explore the use of first-order gradient-based optimization for the design of the seismometer array for 1 Hz and 10 Hz and compare its performance and computational cost to two metaheuristic algorithms. For 1 Hz, we introduce a constraint term to prevent unphysical optimization results in the gradient-based method. In general, we find that it is an efficient strategy to initialize the gradient-based optimizer with a fast metaheuristic algorithm. For a small number of seismometers, this strategy results in approximately the same noise reduction as with the metaheuristics. For larger numbers of seismometers, gradient-based optimization outperforms the two metaheuristics by a factor of 2.25 for the faster of the two and a factor of 1.4 for the other one, which is significantly outperformed by gradient-based optimization in terms of computational efficiency.

astro-ph.IM↗

arXiv2025

Anomaly Detection for Automated Data Quality Monitoring in the CMS Detector

Successful operation of large particle detectors like the Compact Muon Solenoid (CMS) at the CERN Large Hadron Collider requires rapid, in-depth assessment of data quality. We introduce the ``AutoDQM'' system for Automated Data Quality Monitoring using advanced statistical techniques and unsupervised machine learning. Anomaly detection algorithms based on the beta-binomial probability function, principal component analysis, and neural network autoencoder image evaluation are tested on the full set of proton-proton collision data collected by CMS in 2022. AutoDQM identifies anomalous ``bad'' data affected by significant detector malfunction at a rate 4 -- 6 times higher than ``good'' data, demonstrating its effectiveness as a general data quality monitoring tool.

hep-ex↗

arXiv2024

One flow to correct them all: improving simulations in high-energy physics with a single normalising flow and a switch

Simulated events are key ingredients in almost all high-energy physics analyses. However, imperfections in the simulation can lead to sizeable differences between the observed data and simulated events. The effects of such mismodelling on relevant observables must be corrected either effectively via scale factors, with weights or by modifying the distributions of the observables and their correlations. We introduce a correction method that transforms one multidimensional distribution (simulation) into another one (data) using a simple architecture based on a single normalising flow with a boolean condition. We demonstrate the effectiveness of the method on a physics-inspired toy dataset with non-trivial mismodelling of several observables and their correlations.

hep-ph↗

arXiv2023

SR-GAN for SR-gamma: super resolution of photon calorimeter images at collider experiments

We study single-image super-resolution algorithms for photons at collider experiments based on generative adversarial networks. We treat the energy depositions of simulated electromagnetic showers of photons and neutral-pion decays in a toy electromagnetic calorimeter as 2D images and we train super-resolution networks to generate images with an artificially increased resolution by a factor of four in each dimension. The generated images are able to reproduce features of the electromagnetic showers that are not obvious from the images at nominal resolution. Using the artificially-enhanced images for the reconstruction of shower-shape variables and of the position of the shower center results in significant improvements. We additionally investigate the utilization of the generated images as a pre-processing step for deep-learning photon-identification algorithms and observe improvements in the case of training samples of small size.

hep-ex↗

arXiv2023

Resource-aware Research on Universe and Matter: Call-to-Action in Digital Transformation

Given the urgency to reduce fossil fuel energy production to make climate tipping points less likely, we call for resource-aware knowledge gain in the research areas on Universe and Matter with emphasis on the digital transformation. A portfolio of measures is described in detail and then summarized according to the timescales required for their implementation. The measures will both contribute to sustainable research and accelerate scientific progress through increased awareness of resource usage. This work is based on a three-days workshop on sustainability in digital transformation held in May 2023.

physics.comp-ph↗

arXiv2023

Leveraging on-shell interference to search for FCNCs of the top quark and the Z boson

Flavour-changing-neutral currents (FCNCs) involving the top quark are highly suppressed within the Standard Model (SM). Hence, any signal in current or planned future collider experiments would constitute a clear manifestation of physics beyond the SM. We propose a novel, interference-based strategy to search for top-quark FCNCs involving the $Z$ boson that has the potential to complement traditional search strategies due to a more favourable luminosity scaling. The strategy leverages on-shell interference between the FCNC and SM decay of the top quark into hadronic final states. We estimate the feasibility of the most promising case of anomalous $tZc$ couplings using Monte Carlo simulations and a simplified detector simulation. We consider the main background processes and discriminate the signal from the background with a deep neural network that is parametrised in the value of the anomalous $tZc$ coupling. We present sensitivity projections for the HL-LHC and the FCC-hh. We find an expected $95\%$ CL upper limit of $\mathcal{B}_{\mathrm{excl}}(t\rightarrow Zc) = 6.4 \times 10^{-5}$ for the HL-LHC. In general, we conclude that the interference-based approach has the potential to provide both competitive and complementary constraints to traditional multi-lepton searches and other strategies that have been proposed to search for $tZc$ FCNCs.

hep-ph↗

arXiv2021

Reformulation of a likelihood approach to fake-lepton estimation in the framework of Bayesian inference

Prompt isolated leptons are essential in many analyses in high-energy particle physics but are subject to fake-lepton background, i.e. objects that mimic the lepton signature. The fake-lepton background is difficult to estimate from simulation and is often directly determined from data. A popular method is the matrix method, which however suffers from several limitations. This paper recapitulates an alternative approach based on a likelihood with Poisson constraints and reformulates the problem from a different starting point in the framework of Bayesian statistics. The equality of both approaches is shown and several cases are studied in which the matrix method is limited. In addition, the fake lepton background is recalculated and compared to the estimate with the matrix method in an example top-quark measurement.

hep-ph↗

arXiv2021

Maximum performance of strange-jet tagging at hadron colliders

The maximum achievable performance of strange-jet tagging at hadron colliders and the loss in performance in different detector designs is estimated based on simulated truth jets from strange-quark and down-quark hadronisation. Both jet types are classified with a recurrent neural network using long short-term memory units, at first using all available truth particles and then applying selections to study the impacts of ideal tracking detectors, Cherenkov detectors, and calorimeters. Additionally, a manual reconstruction of strange hadron decays such as $K_S\rightarrow π^+ π^-$ from charged tracks is considered.

hep-ex↗

arXiv2020

Correlating uncertainties in global analyses within SMEFT matters

We investigate the impact of correlations between (theoretical and experimental) uncertainties on multi-experiment, multi-observable analyses within the Standard Model Effective Field Theory (SMEFT). To do so, we perform a model-independent analysis of $t$-channel single top-quark production and top-quark decay data from ATLAS, CMS, CDF and D0. We show quantitatively how the fit changes when different experimental or theoretical correlations are assumed. Scaling down statistical uncertainties according to the luminosities of future colliders with $300 \, {\rm fb}^{-1}$ and higher, we find that this effect becomes a matter of life and death: assuming no correlations returns a fit in agreement with the Standard Model while a 'best guess'-ansatz taking into account correlations would observe new physics. At the same time, modelling the impact of higher order SMEFT-corrections the latter turn out to be a subleading source of uncertainty only.

hep-ph↗