arXiv subjects

Stefan Fröse

Publications and source records attributed to Stefan Fröse.

5 recordsLinked to original sources

arXiv2025

Three-dimensional Search for Annihilating Dark Matter in CBe dSph with the MAGIC Telescopes

Dark matter (DM) candidates, such as Weakly Interacting Massive Particles (WIMPs), can annihilate to Standard Model particles, subsequently producing gamma rays. In this work, we search for DM-induced gamma-ray signals from Coma Berenices dwarf spheroidal galaxy (CBe dSph) using approximately 25 hours of observations carried out by the Major Atmospheric Gamma Imaging Cherenkov (MAGIC) Telescope, located at Roque de los Muchachos Observatory, La Palma, Spain. Building upon preceding analyses in the gamma-ray regime, we extend the DM search into three dimensions by incorporating spatial information from the assumed DM-density distribution. This approach enhances sensitivity by leveraging both energy and spatial characteristics of the expected signal. The three-dimensional search for a faint signal necessitates the construction of a background model, leading to the proposal of the exclusion-rotation method. This method stacks all observations, excludes the source region, and corrects for the Azimuth-dependent acceptance of the telescopes by rotating the model. Furthermore, the open-source Python package TITRATE is presented, introducing Asimov datasets to the high-level analysis tool Gammapy for the approximation of the test statistic. No evidence of a DM-induced signal for annihilation to $b\bar{b}$, $W^+W^-$, $μ^+μ^-$, and $τ^+τ^-$ and DM masses $m_χ$ between 0.17 TeV and 100 TeV in CBe dSph is found. Consequently, the first upper limits on the thermally averaged cross-section in energy and spatial dimensions using TITRATE are set, leading to a sensitivity improvement over previous results by MAGIC with respect to the assumed DM density in the target halo. The development of the Asimov method for DM search reduces the need for Monte Carlo simulations, paving the way for computationally efficient and scalable large-scale analyses across multiple targets and cosmic messengers.

astro-ph.HE↗

arXiv2025

Large Physics Models: Towards a collaborative approach with Large Language Models and Foundation Models

This paper explores ideas and provides a potential roadmap for the development and evaluation of physics-specific large-scale AI models, which we call Large Physics Models (LPMs). These models, based on foundation models such as Large Language Models (LLMs) - trained on broad data - are tailored to address the demands of physics research. LPMs can function independently or as part of an integrated framework. This framework can incorporate specialized tools, including symbolic reasoning modules for mathematical manipulations, frameworks to analyse specific experimental and simulated data, and mechanisms for synthesizing theories and scientific literature. We begin by examining whether the physics community should actively develop and refine dedicated models, rather than relying solely on commercial LLMs. We then outline how LPMs can be realized through interdisciplinary collaboration among experts in physics, computer science, and philosophy of science. To integrate these models effectively, we identify three key pillars: Development, Evaluation, and Philosophical Reflection. Development focuses on constructing models capable of processing physics texts, mathematical formulations, and diverse physical data. Evaluation assesses accuracy and reliability by testing and benchmarking. Finally, Philosophical Reflection encompasses the analysis of broader implications of LLMs in physics, including their potential to generate new scientific understanding and what novel collaboration dynamics might arise in research. Inspired by the organizational structure of experimental collaborations in particle physics, we propose a similarly interdisciplinary and collaborative approach to building and refining Large Physics Models. This roadmap provides specific objectives, defines pathways to achieve them, and identifies challenges that must be addressed to realise physics-specific large scale AI models.

physics.data-an↗

arXiv2024

PyHEP.dev 2024 Workshop Summary Report, August 26-30 2024, Aachen, Germany

The second PyHEP.dev workshop, part of the "Python in HEP Developers" series organized by the HEP Software Foundation (HSF), took place in Aachen, Germany, from August 26 to 30, 2024. This gathering brought together nearly 30 Python package developers, maintainers, and power users to engage in informal discussions about current trends in Python, with a primary focus on analysis tools and techniques in High Energy Physics (HEP). The workshop agenda encompassed a range of topics, such as defining the scope of HEP data analysis, exploring the Analysis Grand Challenge project, evaluating statistical models and serialization methods, assessing workflow management systems, examining histogramming practices, and investigating distributed processing tools like RDataFrame, Coffea, and Dask. Additionally, the workshop dedicated time to brainstorming the organization of future PyHEP.dev events, upholding the tradition of alternating between Europe and the United States as host locations. This document, prepared by the session conveners in the weeks following the workshop, serves as a summary of the key discussions, salient points, and conclusions that emerged.

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↗

arXiv2022

Deep Learning-based Imaging in Radio Interferometry

The sparse layouts of radio interferometers result in an incomplete sampling of the sky in Fourier space which leads to artifacts in the reconstructed images. Cleaning these systematic effects is essential for the scientific use of radiointerferometric images. Established reconstruction methods are often time-consuming, require expert-knowledge, and suffer from a lack of reproducibility. We have developed a prototype Deep Learning-based method that generates reproducible images in an expedient fashion. To this end, we take advantage of the efficiency of Convolutional Neural Networks to reconstruct image data from incomplete information in Fourier space. The Neural Network architecture is inspired by super-resolution models that utilize residual blocks. Using simulated data of radio galaxies that are composed of Gaussian components we train Deep Learning models whose reconstruction capability is quantified using various measures. The reconstruction performance is evaluated on clean and noisy input data by comparing the resulting predictions with the true source images. We find that source angles and sizes are well reproduced, while the recovered fluxes show substantial scatter, albeit not worse than existing methods without fine-tuning. Finally, we propose more advanced approaches using Deep Learning that include uncertainty estimates and a concept to analyze larger images.

astro-ph.IM↗