Related papers: A deep learning method for the trajectory reconstr…
The Plastic Scintillator Detector (PSD) of the DArk Matter Particle Explorer (DAMPE) is designed to measure cosmic ray charge (Z) and to act as a veto detector for gamma-ray identification. In order to fully exploit the charge…
GeV gamma ray is an important observation target of DArk Matter Particle Explorer (DAMPE) for indirect dark matter searching and high energy astrophysics. We present in this work a set of accurate instrument response functions of DAMPE…
High-energy physics is facing increasingly computational challenges in real-time event reconstruction for the near-future high-luminosity era. Using the LHCb vertex detector as a use-case, we explore a new algorithm for particle track…
The VAMOS++ magnetic spectrometer is characterized by a large angular and momentum acceptance and highly non-linear ion optics properties requiring the use of software ion trajectory reconstruction methods to measure the ion magnetic…
We present a method for reconstructing two-dimensional velocity fields at specified length scales using observational data from tracer particles in a flow, without the need for interpolation or smoothing. The algorithm, adapted from…
Machine-learning-based interatomic potential energy surface (PES) models are revolutionizing the field of molecular modeling. However, although much faster than electronic structure schemes, these models suffer from costly computations via…
Pions constitute nearly $70\%$ of final state particles in ultra high energy collisions. They act as a probe to understand the statistical properties of Quantum Chromodynamics (QCD) matter i.e. Quark Gluon Plasma (QGP) created in such…
The DArk Matter Particle Explorer (DAMPE) is a space-borne particle detector designed to probe electrons and gamma-rays in the few GeV to 10 TeV energy range, as well as cosmic-ray proton and nuclei components between 10 GeV and 100 TeV.…
The many ways in which machine and deep learning are transforming the analysis and simulation of data in particle physics are reviewed. The main methods based on boosted decision trees and various types of neural networks are introduced,…
Deep Matching (DM) is a popular high-quality method for quasi-dense image matching. Despite its name, however, the original DM formulation does not yield a deep neural network that can be trained end-to-end via backpropagation. In this…
In the reconstruction of physics events at future e$^+$e$^-$ colliders the calorimeter design has a crucial role in the overall detector performance. The reconstruction of events with many jets in their final state sets stringent…
The large angular and momentum acceptance magnetic spectrometer VAMOS++, at GANIL, France, is frequently used for nuclear structure and reaction dynamics studies. It provides an event-by-event identification of heavy ions produced in…
The present thesis is devoted to the reconstruction of the dark energy models using diverse observational data sets. The parametric approach has been adopted for the reconstruction of cosmological models. The reconstruction of kinematical…
The DArk Matter Particle Explorer (DAMPE) developed in China was designed to search for evidence of dark matter particles by observing primary cosmic rays and gamma rays in the energy range from 5 GeV to 10 TeV. Since its launch in December…
Space exploration missions have seen use of increasingly sophisticated robotic systems with ever more autonomy. Deep learning promises to take this even a step further, and has applications for high-level tasks, like path planning, as well…
This paper presents Deep-PANTHER, a learning-based perception-aware trajectory planner for unmanned aerial vehicles (UAVs) in dynamic environments. Given the current state of the UAV, and the predicted trajectory and size of the obstacle,…
The GammaTPC is an MeV-scale single-phase liquid argon time-projection-chamber gamma-ray telescope concept with a novel dual-scale pixel-based charge-readout system. It promises to enable a significant improvement in sensitivity to…
The major scientific goals of DArk Matter Particle Explorer (DAMPE) are to study cosmic-ray electrons (including positrons) and gamma rays from 5 GeV to 10 TeV and nuclei from Z = 1 to 26 up to 100 TeV. The deposited energy measured by the…
In this paper, we review physics- and data-driven reconstruction techniques for simultaneous positron emission tomography (PET) / magnetic resonance imaging (MRI) systems, which have significant advantages for clinical imaging of cancer,…
New particle acceleration schemes open up exciting opportunities, potentially providing more compact or higher-energy accelerators. The AWAKE experiment at CERN is currently taking data to establish the method of proton-driven plasma…