arXiv subjects

Kristian Tchiorniy

Publications and source records attributed to Kristian Tchiorniy.

3 recordsLinked to original sources

arXiv2026

On the Codesign of Scientific Experiments and Industrial Systems

The optimization of large experiments in fundamental science, such as detectors for subnuclear physics at particle colliders, shares with the optimization of complex systems for industrial or societal applications the common issue of addressing the inter-relation between parameters describing the hardware used in data production and parameters used to analyse those data. While in many cases this coupling can be ignored -- when the problem can be successfully factored into simpler sub-tasks and the latter addressed serially -- there are situations in which that approach fails to converge to the absolute maximum of expected performance, as it results in a mis-alignment of the optimized hardware and software solutions. In this work we consider a few use cases of interest in fundamental science collected primarily from particle physics and related areas, and a pot-pourri of industrial and societal applications where the matter is similarly of relevance. We discuss the emergence of strong hardware-software coupling in some of those systems, as well as co-design procedures that may be deployed to identify the global maximum of their relevant utility functions. We observe how numerous opportunities exist to advance methods and tools for hardware-software co-design optimization, bridging fundamental science and industry through application- and challenge-driven projects, and shaping the future of scientific experiments and industrial systems.

physics.ins-det↗

arXiv2026

Broken Expectations: The Effects of Modelling Assumptions on the Inferred Dark Matter Distribution in the Milky Way's Satellites

The spherical Jeans equation is commonly used to infer dark matter distributions in dwarf spheroidal satellites of the Milky Way to constrain the nature of dark matter. One of its assumptions is that of dynamical equilibrium while the dwarfs are under the influence of Galactic tides. We carry out tailored simulations of Carina, Draco, Fornax, Sculptor and Ursa Minor and test the accuracy of dark matter density profiles and annihilation rates (J-factors) recovered with the Jeans analysis code pyGravSphere. We find that tides do not significantly affect the quality of density profile inference; however, pyGravSphere tends to underestimate the inner densities of dwarf galaxies, which, together with tidal mass loss, leads to an inference of flatter density slopes, although all of our dwarfs have cuspy Navarro-Frenk-White haloes. This is because the default broken power-law model is unable to describe the outer halo density profile. The recovered J-factors are generally underestimated. While the difference with the true J-factor is small, the error bars are also often underestimated. We also test the accuracy of the Wolf et al. 2010 mass estimator and find that it can be sensitive to orbital stage and eccentricity. Still, for our sample of dwarf galaxies, the estimates agree with the truth within 10 percent. Consistency of our simulated dwarfs with the mass-concentration relation in LambdaCDM requires a light Milky Way, or limited action of tides, which may be in tension with a "tidal stirring" origin of dwarf spheroidals.

astro-ph.GA↗

arXiv2025

flashcurve: A machine-learning approach for the simple and fast generation of adaptive-binning light curves with Fermi-LAT data

Gamma rays measured by the Fermi-LAT satellite tell us a lot about the processes taking place in high-energetic astrophysical objects. The fluxes coming from these objects are, however, extremely variable. Hence, gamma-ray light curves optimally use adaptive bin sizes in order to retrieve most information about the source dynamics and to combine gamma-ray observations in a multi-messenger perspective. However, standard adaptive binning approaches are slow, expensive and inaccurate in highly populated regions. Here, we present a novel, powerful, deep-learning-based approach to estimate the necessary time windows for adaptive binning light curves in Fermi-LAT data using raw photon data. The approach is shown to be fast and accurate. It can also be seen as a prototype to train machine-learning models for adaptive binning light curves for other astrophysical messengers.

astro-ph.IM↗