Related papers: Efficient Antihydrogen Detection in Antimatter Phy…
One of the ASACUSA (Atomic Spectroscopy And Collisions Using Slow Antiprotons) collaboration's goals is the measurement of the ground state hyperfine transition frequency in antihydrogen, the antimatter counterpart of one of the best known…
A study of antiproton-nucleus annihilations at rest on a variety of thin solid targets using slow extracted antiprotons is being prepared. To detect the charged annihilation products, the experiment will employ seven Timepix4 ASICs coupled…
The goal of the ASACUSA-CUSP collaboration at the Antiproton Decelerator of CERN is to measure the ground-state hyperfine splitting of antihydrogen using an atomic spectroscopy beamline. A milestone was achieved in 2012 through the…
Hydrogen embrittlement can cause a dramatic deterioration of the mechanical properties of high-strength metallic materials. Despite decades of experimental and modelling studies, the exact underlying mechanisms behind hydrogen embrittlement…
High-precision hydrogen spectroscopy is an active field which helps to determine the Rydberg constant and proton charge radius, tests bound-state QED, and can search for Beyond Standard Model (BSM) Physics. Additionally, with recent…
Studying fundamental symmetries of Nature has proven fruitful in particle physics. I argue that recent results at the LHC, and the naturalness problem highlighted by them, provide a renewed motivation for tests of CPT symmetry as a probe…
The ALPHA collaboration has successfully demonstrated the production and the confinement of cold antihydrogen, $\overline{\mathrm{H}}$. An analysis of trapping data allowed a stringent limit to be placed on the electric charge of the…
The goal of the ASACUSA (Atomic Spectroscopy And Collisions Using Slow Antiprotons) CUSP experiment at CERN's Antiproton Decelerator is to measure the ground state hyperfine splitting of antihydrogen in order to test whether CPT invariance…
The limited statistics of the available (anti)neutrino-hydrogen (H) interactions has been a longstanding impediment for high-energy neutrino physics. We discuss a practical way to achieve accurate (anti)neutrino-hydrogen measurements,…
The PANDA (anti-Proton ANnihilation at DArmstadt) experiment at the Facility for Anti-proton and Ion Research is going to study strong interactions at the scale at which quarks are confined to form hadrons. A continuous beam of antiproton,…
Reliable data quality monitoring is a key asset in delivering collision data suitable for physics analysis in any modern large-scale High Energy Physics experiment. This paper focuses on the use of artificial neural networks for supervised…
We provide a fast approach incorporating the usage of deep learning for evaluating the effects of photon sensors in an antineutrino detector on the event reconstruction performance therein. This work is an attempt to harness the power of…
In the GBAR experiment, cold antihydrogen atoms will be left to fall on an annihilation plate with the aim of measuring the gravitational acceleration of antimatter. Here, we study the quantum reflection of these antiatoms due to the…
The GBAR experiment aims at performing the first free-fall experiment with antihydrogen atoms in order to test the weak equivalence principle with antimatter. Antihydrogen ions are synthesized through a double charge exchange reaction and…
Deep Learning is gaining traction with geophysics community to understand subsurface structures, such as fault detection or salt body in seismic data. This study describes using deep learning method for iceberg or ship recognition with…
Addressing the issue of submerged underwater trash is crucial for safeguarding aquatic ecosystems and preserving marine life. While identifying debris present on the surface of water bodies is straightforward, assessing the underwater…
This talk gives an overview of the recent results on the precision spectroscopy of antiprotonic helium which was performed by the ASACUSA collaboration at the Antiproton Decelerator of CERN. The laser spectroscopy of energy levels of the…
Using deep neural networks for identifying physics objects at the Large Hadron Collider (LHC) has become a powerful alternative approach in recent years. After successful training of deep neural networks, examining the trained networks not…
This work presents advancements in model-agnostic searches for new physics at the Large Hadron Collider (LHC) through the application of event-based anomaly detection techniques utilizing unsupervised machine learning. We discuss the…
Using very long baseline interferometry, the Event Horizon Telescope (EHT) collaboration has resolved the shadows of two supermassive black holes. Model comparison is traditionally performed in image space, where imaging algorithms…