Related papers: Machine Learning for Imaging Cherenkov Detectors
The past years have seen a considerable increase in cancer cases. However, a cancer diagnosis is often complex and depends on the types of images provided for analysis. It requires highly skilled practitioners but is often time-consuming…
A recent study [PRB 100, 075427 (2019)], finally, demonstrated plasmon-analog of refractive index enhancement in metal nanostructures, which has already been studied in atomic clouds for several decades. Here, we simply utilize this…
This paper will argue for continued effort in developing imaging calorimeters for future colliders and/or upgrades to existing detectors. Imaging calorimeters offer a plethora of advantages beyond their application in conjunction with…
Cherenkov radiation provides a valuable way to identify high energy particles in a wide momentum range, through the relation between the particle velocity and the Cherenkov angle. However, since the Cherenkov angle depends only on…
Calibration of a PMT matrix is crucial for the treatment of the data obtained with Cherenkov tracking detector. Furthermore, due to high variability of the aerosol abundance in the atmosphere depending on season, weather etc. A constant…
We report on the characterization of candidate light sensors for use in the next-generation Imaging Atmospheric Cherenkov Telescope project called Cherenkov Telescope Array, a major astro-particle physics project of about 100 telescopes…
Advances in machine learning methods provide tools that have broad applicability in scientific research. These techniques are being applied across the diversity of nuclear physics research topics, leading to advances that will facilitate…
Machine learning plays a crucial role in enhancing and accelerating the search for new fundamental physics. We review the state of machine learning methods and applications for new physics searches in the context of terrestrial high energy…
The observation of high energy cosmic neutrinos can shed light on the astrophysical sites and mechanisms involved in the acceleration of protons and nuclei to the high energies observed at Earth by cosmic ray detectors. More generally, high…
In recent years, machine learning has emerged as a powerful computational tool and novel problem-solving perspective for physics, offering new avenues for studying strongly interacting QCD matter properties under extreme conditions. This…
Recent results on the particle detector R&D for new accelerators are reviewed. Different approaches for the muon systems, hadronic and electromagnetic calorimeters, particle identification devices, and central trackers are discussed. Main…
In order to fully exploit the physics potential of the ILC, it will be necessary to measure (and control) beam parameters to a permille level precision. In case of the beam polarisation, this can only be achieved with dedicated high energy…
The increasing luminosities of future Large Hadron Collider runs and next generation of collider experiments will require an unprecedented amount of simulated events to be produced. Such large scale productions are extremely demanding in…
Recent applications of machine learning and statistical inference provide case studies demonstrating how such approaches can accelerate the discovery process in physical chemistry and related fields. Examples discussed in this review…
Modern machine learning is driving a paradigm shift in particle physics phenomenology at the Large Hadron Collider. This short review examines the transformative role of machine learning across the entire theoretical prediction pipeline,…
We describe a novel photon detector which operates under an intense flux of neutrons. It is composed of lead-aerogel sandwich counter modules. Its salient features are high photon detection efficiency and blindness to neutrons. As a result…
We describe here a new concept of a Cherenkov detector for particle identification by means of measuring the Time-of-Propagation (TOP) of Cherenkov photons.
With the improvement of computer performance and the increase of data volume, the object detection based on convolutional neural network (CNN) has become the main algorithm for object detection. This paper summarizes the research progress…
Advanced detector R&D requires performing computationally intensive and detailed simulations as part of the detector-design optimization process. We propose a general approach to this process based on Bayesian optimization and machine…
Machine learning techniques have found their way into computational chemistry as indispensable tools to accelerate atomistic simulations and materials design. In addition, machine learning approaches hold the potential to boost the…