Peter Wissmann | Searcharxiv

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↗

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↗