Segmentation of EM showers for neutrino experiments with deep graph neural networks

We introduce a first-ever algorithm for the reconstruction of multiple showers from the data collected with electromagnetic (EM) sampling calorimeters. Such detectors are widely used in High Energy Physics to measure the energy and kinematics of in-going particles. In this work, we consider the case when many electrons pass through an Emulsion Cloud Chamber (ECC) brick, initiating electron-induced electromagnetic showers, which can be the case with long exposure times or large input particle flux. For example, SHiP experiment is planning to use emulsion detectors for dark matter search and neutrino physics investigation. The expected full flux of SHiP experiment is about 1020 particles over five years. To reduce the cost of the experiment associated with the replacement of the ECC brick and off-line data taking (emulsion scanning), it is decided to increase exposure time. Thus, we expect to observe a lot of overlapping showers, which turn EM showers reconstruction into a challenging point cloud segmentation problem. Our reconstruction pipeline consists of a Graph Neural Network that predicts an adjacency matrix and a clustering algorithm. We propose a new layer type (EmulsionConv) that takes into account geometrical properties of shower development in ECC brick. For the clustering of overlapping showers, we use a modified hierarchical density-based clustering algorithm. Our method does not use any prior information about the incoming particles and identifies up to 87% of electromagnetic showers in emulsion detectors. The achieved energy resolution over 16,577 showers is σE/E = (0.095 ± 0.005) + (0.134 ± 0.011)/√(E). The main test bench for the algorithm for reconstructing electromagnetic showers is going to be SND@LHC.

Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed

Accurate simulation of physical processes is crucial for the success of modern particle physics. However, simulating the development and interaction of particle showers with calorimeter detectors is a time consuming process and drives the computing needs of large experiments at the LHC and future colliders. Recently, generative machine learning models based on deep neural networks have shown promise in speeding up this task by several orders of magnitude. We investigate the use of a new architecture—the Bounded Information Bottleneck Autoencoder—for modelling electromagnetic showers in the central region of the Silicon-Tungsten calorimeter of the proposed International Large Detector. Combined with a novel second post-processing network, this approach achieves an accurate simulation of differential distributions including for the first time the shape of the minimum-ionizing-particle peak compared to a full Geant4 simulation for a high-granularity calorimeter with 27k simulated channels. The results are validated by comparing to established architectures. Our results further strengthen the case of using generative networks for fast simulation and demonstrate that physically relevant differential distributions can be described with high accuracy.

Analytical investigations of high-energy electromagnetic showers in strong magnetic fields

The diffusion equation of high-energy electromagnetic showers in strong magnetic fields is solved analytically, under the condition that the product of particle energy and field strength considerably exceeds $mc^2H_{\rm c}\simeq 2.3 \times 10^{7}$ TeV G, by applying Mellin and Laplace transforms. Differential and integral energy spectra of shower electrons/positrons and photons are evaluated by applying the saddle point method. Both spectra expressed by asymptotic expansions are also derived based on singularities of the Laplace–Mellin transform of the spectrum. The results are compared with those derived by a Monte Carlo method and numerical integration methods. Energy flows, peak positions, and peak values of transition curves, as well as track lengths of shower particles, are predicted and discussed, together with other characteristic properties of showers in strong magnetic fields, wherein good agreement between the low-energy limit of the power-law index for our differential energy spectra and the low-energy photon index of $\Gamma$ observed in Fermi LAT is pointed out and discussed.

The Heavy Photon Search (HPS) Software Environment

The Heavy Photon Search (HPS) is an experiment at the Thomas Jefferson National Accelerator Facility (JLab) designed to search for a hidden sector photon (A’) in fixed-target electro-production. It uses a silicon microstrip tracking and vertexing detector placed inside a dipole magnet to measure charged particle trajectories and a fast lead-tungstate crystal calorimeter located just downstream of the magnet to provide a trigger and to identify electromagnetic showers. The HPS experiment uses both invariant mass and secondary vertex signatures to search for the A’. The experimental collaboration is small and quite heterogeneous: it is composed of members of the nuclear physics as well as particle physics communities, from universities and national labs from around the US and Europe. Enabling such a disparate group to concentrate on the physics aspects of the experiment required that the software be easy to install and use, and having such limited manpower meant that existing solutions had to be exploited. HPS has successfully completed two engineering runs and completed its first physics run in the summer of 2019. We begin with an overview of the physics goals of the experiment followed by a short description of the detector design. We then describe the software tools used to design the detector layout and simulate the expected detector performance. Event reconstruction involving track, cluster and vertex finding and fitting for both simulated and real data was, to first order, adopted from existing software originally developed for Linear Collider studies. Bringing it all together into a cohesive whole involved the use of multiple software solutions with common interfaces.

CaloCube and “Tracker In Calorimeter” projects for the direct measurement of high energy charged astro-particles and gamma rays.

Measurements of high energy cosmic rays in the “knee” region (about 1015 eV) are currently available only with ground detectors: new observations of cosmic particles up to these energies with direct measurements are one of the main goals of the next generation space experiments. To achieve those aims, a large acceptance, good energy resolution and particle identification are needed. CaloCube is the design of a space borne calorimeter which is capable to accept particles coming from any direction, increasing the acceptance with respect to traditional telescopes. A good performance for both hadronic and electromagnetic showers is achieved with a 3-D sampling capability: the basic picture of CaloCube is a cubic homogeneous calorimeter which consists of cubic scintillating crystals. MC simulations, concerning different materials and geometrical configurations, and several beam tests with different versions of the CaloCube prototype have been employed to optimize both the detector design and the data analysis method. Taking advantage of the CaloCube project, the space experiment HERD (“High Energy Cosmic Radiation Detection”) will include a large acceptance cubic calorimeter with cubic LYSO crystals. It will be installed on-board of the Chinese space station around 2025. Beside the charged particle observations, high energy gamma-rays provide direct information about the galactic cosmic ray sources. A new project named “Tracker In Calorimeter” (TIC) was approved by INFN in 2017 with the main purpose of the optimization of the calorimeter design for the reconstruction of the gamma-ray direction, without the requirement of additional not homogeneous pre-shower detector. A TIC prototype was recently assembled and tested at the PS-CERN and SPS-CERN accelerators.