Related papers: Performance of the FARICH-based particle identific…
Accurate description of crystal structures is a prerequisite for predicting the physicochemical properties of materials. However, conventional X-ray diffraction (XRD) characterization often encounters intrinsic bottlenecks when applied to…
A novel technique based on machine learning is introduced to reconstruct the decays of highly Lorentz-boosted particles. Using an end-to-end deep learning strategy, the technique bypasses existing rule-based particle reconstruction methods…
AMS-02 is a detector currently in operation onboard the International Space Station (ISS). One of the main scientific goals of the spectrometer is the measurement of charged particle fluxes. The detector design makes possible the…
We show that a very precise neutrino/anti-neutrino event separation is not mandatory to cover the physics program of a low energy neutrino factory and thus non-magnetized detectors like water Cerenkov or liquid Argon detectors can be used.…
Imaging Cherenkov detectors are largely used in modern nuclear and particle physics experiments where cutting-edge solutions are needed to face always more growing computing demands. This is a fertile ground for AI-based approaches and at…
We show that deep learning algorithms can be deployed to study bifurcations of particle trajectories. We demonstrate this for two physical systems, the unperturbed Duffing equation and charged particles in magnetic reversal by using the AI…
Quantum machine learning is arguably one of the most explored applications of near-term quantum devices. Much focus has been put on notions of variational quantum machine learning where parameterized quantum circuits (PQCs) are used as…
Using aerogel as radiator and multianode PMTs for photon detection, a proximity focusing Cherenkov ring imaging detector has been constructed and tested in the KEK $\pi$2 beam. The aim is to experimentally study the basic parameters such as…
The sensitivity of ground-based imaging atmospheric Cherenkov gamma-ray observatories depends critically on the primary particle identification methods which are used to retain photon-initiated events and suppress the spurious background…
A method for object classification that is based on distribution analysis is proposed. In addition, a method for finding relevant features and the unification of this algorithm with another classification algorithm is proposed. The…
The construction and use of a dual radiator Ring Imaging Cerenkov(RICH) detector is described. This instrument was developed for the HERMES experiment at DESY which emphasizes measurements of semi-inclusive deep-inelastic scattering. It…
Many matrices associated with fast transforms posess a certain low-rank property characterized by the existence of several block partitionings of the matrix, where each block is of low rank. Provided that these partitionings are known,…
Machine learning methods are being introduced at all stages of data reconstruction and analysis in various high-energy physics experiments. We present the development and application of convolutional neural networks with modified…
Cerenkov technology is often the optimal choice for particle identification in high energy particle collision applications. Typically, the most challenging regime is at high pseudorapidity (forward) where particle identification must…
The CLEO-III Detector upgrade for charged particle identification is discussed. The RICH design uses solid LiF crystal radiators coupled with multi-wire chamber photon detectors, using TEA as the photosensor, and low-noise Viking readout…
We report on the charged particle identification (PID) systems for the upcoming Belle II experiment. The time of propagation counter in the central region and the proximity focusing ring imaging Cherenkov counters with aerogel radiator in…
The granularity of calorimeter has been revolutionary boosted for future collider experiments. The calorimeter has been pushed to a stage that the sub structure of showers especially hadronic showers can be recorded to a high precision. New…
In this paper, we present PARTICUL, a novel algorithm for unsupervised learning of part detectors from datasets used in fine-grained recognition. It exploits the macro-similarities of all images in the training set in order to mine for…
Imaging systems' performance at low light intensity is affected by shot noise, which becomes increasingly strong as the power of the light source decreases. In this paper we experimentally demonstrate the use of deep neural networks to…
This report reviews methods of pattern recognition and event reconstruction used in modern high energy physics experiments. After a brief introduction into general concepts of particle detectors and statistical evaluation, different…