Related papers: Point Cloud Deep Learning Methods for Particle Sho…
We present an end-to-end reconstruction algorithm to build particle candidates from detector hits in next-generation granular calorimeters similar to that foreseen for the high-luminosity upgrade of the CMS detector. The algorithm exploits…
We describe the design concept and estimated performance of an iron-scintillator sampling calorimeter for the future Electron Ion Collider. The novel aspect of this detector is a multi-dimensional readout coupled with foreseen excellent…
This paper proposes a Graph Neural Network(GNN)-based method for exploiting semantics and local geometry to guide the identification of reliable pointcloud registration candidates. Semantic and morphological features of the environment…
The Semi-Digital Hadronic Calorimeter (SDHCAL) is proposed to equip the future ILC detector. A technological prototype of the SDHCAL developed within the CALICE collaboration has been extensively tested in test beams. We review here the…
We present the study of a fuzzy clustering algorithm for the Belle II electromagnetic calorimeter using Graph Neural Networks. We use a realistic detector simulation including simulated beam backgrounds and focus on the reconstruction of…
The SDHCAL technological prototype is a $1 \times 1 \times 1.3$ m$^3$ high-granularity Semi-Digital Hadronic CALorimeter using Glass Resistive Plate Chambers as sensitive medium. It is one of the two HCAL options considered by the ILD…
The software compensation algorithms developed for the CALICE Analog Hadron Calorimeter are extended to incorporate time information on the cell level, and the performance is studied in GEANT4 simulations with a detector model of a…
Three-dimensional (3D) point clouds are important data representations in visualization applications. The rapidly growing utility and popularity of point cloud processing strongly motivate a plethora of research activities on large-scale…
In this article we describe a new convolutional neural network (CNN) to classify 3D point clouds of urban or indoor scenes. Solutions are given to the problems encountered working on scene point clouds, and a network is described that…
A graph neural network (GCN) is employed in the deep energy method (DEM) model to solve the momentum balance equation in 3D for the deformation of linear elastic and hyperelastic materials due to its ability to handle irregular domains over…
Calorimeters for particle physics experiments with integration time of a few ns will substantially improve the capability of the experiment to resolve event pileup and to reject backgrounds. In this paper the time development of hadronic…
The extension of the longitudinal hadronic shower profile parameterization which takes into account non-compensations of calorimeters and the algorithm of the longitudinal hadronic shower profile curve making for a combined calorimeter are…
The demands placed on computational resources by the simulation requirements of high energy physics experiments motivate the development of novel simulation tools. Machine learning based generative models offer a solution that is both fast…
Precision physics at future colliders requires highly granular calorimeters to support the Particle Flow Approach for event reconstruction. This article presents a review of about 10 - 15 years of R\&D, mainly conducted within the CALICE…
Graph Neural Networks (GNNs) have gained significant momentum recently due to their capability to learn on unstructured graph data. Dynamic GNNs (DGNNs) are the current state-of-the-art for point cloud applications; such applications (viz.…
The General Antiparticle Spectrometer (GAPS) is a balloon-borne experiment that aims to measure low-energy cosmic-ray antiparticles. GAPS has developed a new antiparticle identification technique based on exotic atom formation caused by…
Charged particle track reconstruction is a foundational task in collider experiments and the main computational bottleneck in particle reconstruction. Graph neural networks (GNNs) have shown strong performance for this problem, but costly…
Generative diffusion models have shown empirical successes in point cloud resampling, generating a denser and more uniform distribution of points from sparse or noisy 3D point clouds by progressively refining noise into structure. However,…
Chromatic calorimetry introduces a novel approach to calorimeter design in High Energy Physics (HEP) by integrating Quantum Dot (QD) technology into traditional homogeneous calorimeters. The tunable emission spectra of QDs provide new…
In this paper, we present new feature encoding methods for Detection of 3D objects in point clouds. We used a graph neural network (GNN) for Detection of 3D objects namely cars, pedestrians, and cyclists. Feature encoding is one of the…