Conveners
3.1 Pattern recognition & Image analysis
- Gabrijela Zaharijas (Center for Astrophysics and Cosmology, University of Nova Gorica)
One of the main challenges in astronomical imaging is getting as much signal as possible with as little noise as possible. The better the signal, the more sure one can be that the science done with the images is sound. However increasing the signal-to-noise-ratio on the detector is hard and expensive. Therefore a lot of research is focused on improving post-processing techniques to gain as...
The Dark Matter Particle Explorer (DAMPE) is the largest calorimeter-based space-borne experiment. Since its launch in December 2015, DAMPE detects electrons, positrons and gamma rays from few GeV to 10 TeV, as well as protons and heavier nuclei from 10 GeV to 100 TeV. The study of galactic and extragalactic gamma-ray sources and diffuse emissions as well as the search for dark-matter...
The Cherenkov Telescope Array (CTA) is entering its production phase and the upcoming data will drastically improve the point source sensitivity compared to previous imaging atmospheric Cherenkov telescopes. The Galactic Plane Survey (GPS), proposed as one of the Key Science Projects for CTA observation will focus on the observation of the inner galactic region ($|b|<6^∘$).
Here we discuss...
The Ring Imaging Cherenkov (RICH) detector is integral to the CBM experiment's electron identification process, aiming to distinguish electrons and suppress pions in the study of dielectronic decay channels of vector mesons. This study is crucial for exploring the phase diagram of strongly interacting matter under conditions of high net baryon densities and moderate temperatures, as...
This research introduces a physics-driven graph neural network (GNN) [1] tailored for the identification and reconstruction of $\Lambda$ hyperons in the WASA-FRS [2] experiment. The reconstructed $\Lambda$ hyperons serve as calibration processes, essential for the primary objective of the experiment, namely to detect hypertritons. This GNN is based upon successfully developed machine learning...
The upcoming silicon-based sampling calorimeters, such as the high-granularity calorimeter of the CMS experiment, will have unprecedented granularity in both the lateral and longitudinal dimensions. We expect these calorimeters to greatly benefit from machine learning-based reconstruction techniques. With the novel idea of interpreting the multiple sampling layers of calorimeters in the $\eta$...
Recent experiments with high-energy heavy ion beams challenge the current understanding of light hypernuclei (sub-atomic nuclei exhibiting strangeness), particularly the hypertriton [1,2,3,4,5,6,7,8]. This perplexing situation, known as the "hypertriton puzzle," is the focal point of our European-Japanese collaboration between CSIC – Spain, GSI-FAIR – Germany and RIKEN – Japan within the...
