Related papers: Direct Photon Identification with Artificial Neura…

Identified particle spectra represent a crucial tool to understand the behavior of the matter created in high-energy heavy-ion collisions. The transverse momentum p_T distributions of identified hadrons contain informations about the…

A neural network technique is used to discriminate between quark and gluon jets produced in the qg->q+photon and q q->g+photon processes at the LHC. Considering the network as a trigger and using the PYTHIA event generator and the full…

The dark photon is a new gauge boson that naturally arises in many beyond the Standard Model theoretical models, featuring interactions that resemble quantum electrodynamics. Due to this feature, it is often considered the portal between…

This paper presents a novel approach to detect F0 through Convolutional Neural Networks and image processing techniques to directly estimate pitch from spectrogram images. Our new approach demonstrates a very good detection accuracy; a…

The understanding of the reconstruction and calibration of electrons and photons is one of the key steps at the start-up of data-taking with ATLAS at the LHC (Large Hadron Collider). The calorimeter cells are electronically calibrated…

In the years between 2000 and 2002 several pre-series and series modules of the ATLAS EM barrel and end-cap calorimeter were exposed to electron, photon and pion beams. The performance of the calorimeter with respect to its finely segmented…

The ALPS II experiment, Any Light Particle Search II at DESY in Hamburg, will look for light (m< 10-4 eV) new fundamental bosons (e.g., axion-like particles, hidden photons and other WISPs) in the next years by the mean of a…

Conventional LIDAR systems require hundreds or thousands of photon detections to form accurate depth and reflectivity images. Recent photon-efficient computational imaging methods are remarkably effective with only 1.0 to 3.0 detected…

We demonstrate the importance and utility of Monte Carlo simulation of single-photon detectors. Devising an optimal simulation is strongly influenced by the particular application because of the complexity of modern, avalanche-diode-based…

Muon identification is crucial for elementary particle physics experiments. At the Belle II experiment, muons and pions with momenta greater than 0.7 GeV/c are distinguished by their penetration ability through the $K_L$ and Muon (KLM)…

Low transverse momentum direct photon measurements have been carried out by the ALICE experiment at the CERN LHC in small collision systems (pp, $\sqrt{s}=2.76$ and 8 TeV and p--Pb, $\sqrt{s_\text{NN}}=5.02$ TeV) as well as in heavy-ion…

Particle identification (PID) is one of the main strengths of the ALICE experiment at the LHC. It is a crucial ingredient for detailed studies of the strongly interacting matter formed in ultrarelativistic heavy-ion collisions. ALICE…

The main focus of the ALICE experiment, quark--gluon plasma measurements, requires accurate particle identification (PID). The ALICE subdetectors allow identifying particles over a broad momentum interval ranging from about 100 MeV/c up to…

We develop a novel algorithm for characterizing Deep Sub-Electron Read Noise (DSERN) image sensors. This algorithm is able to simultaneously compute maximum likelihood estimates of quanta exposure, conversion gain, bias, and read noise of…

Comparing the measurements of the hadronic final state from partonic showers in proton-proton and heavy-ion collisions will reveal the modifications generated by the medium on partons produced in hard scatterings. This can be achieved by…

Axion-photon conversion is a prime mechanism to detect axion-like particles that share a coupling to the photon. We point out that in the vicinity of neutron stars with strong magnetic fields, magnetars, the effective photon mass receives…

CNNs have become one of the most commonly used computational tool in the past two decades. One of the primary downsides of CNNs is that they work as a ``black box", where the user cannot necessarily know how the image data are analyzed, and…

Acceleration processes that occur in astrophysical plasmas produce cosmic rays that are observed on Earth. To study particle acceleration, fully-kinetic particle-in-cell (PIC) simulations are often used as they can unveil the microphysics…

This paper presents the custom implementation, optimization, and performance evaluation of convolutional neural networks on field programmable gate arrays, for the purposes of accelerating deep neural network inference on large,…

We propose a method to use artificial neural networks to approximate light scattering by multilayer nanoparticles. We find the network needs to be trained on only a small sampling of the data in order to approximate the simulation to high…