arXiv subjects

S. Dugad

Publications and source records attributed to S. Dugad.

5 recordsLinked to original sources

arXiv2026

Particle tracking at high luminosities using a novel reconstruction approach

Tracking charged particles with high precision is of vital importance for collider experiment like those operating at the Large Hadron Collider (LHC), CERN. The tracking detector in the CMS experiment is composed of multi-layer silicon based tracker with 3-dimensional position sensitivity. High precision position data from tracker operated in high magnetic field, is used to reconstruct the trajectories of charged particles and obtain their kinematic parameters ($p_t$,$\eta_0$, $\phi_0$) with high accuracy. In this paper, for Phase2 CMS tracker design, we present a novel track reconstruction algorithm for high luminosity (HL) era of the LHC. The algorithm identifies hits associated with each track and utilizes them to accurately determine the kinematic parameters of each track using machine learning (ML) architecture. The proposed algorithm has been applied on a large sample of hard interactions simulated at high luminosity (HL) era of the LHC using Pythia8 and Geant4 framework for equivalent geometry of the outer tracker of the CMS experiment. Performance of the proposed algorithm has been studied using the key indicators such as reconstruction efficiency, fake rate and resolution. Comparison with traditional methods demonstrates robust performance with excellent efficiency and resolution with minimal fake rate.

hep-ex↗

arXiv2022

Deep learning applications for quality control in particle detector construction

The growing complexity of particle detectors makes their construction and quality control a new challenge. We present studies that explore the use of deep learning-based computer vision techniques to perform quality checks of detector components and assembly steps, which will automate procedures and minimize the need for human interventions. This study focuses on the construction steps of a silicon detector, which involve forming a mechanical structure with the sensor and wire bonding individual cells to electronics for reading out signals. Silicon detectors in high energy physics experiments today have millions of channels. Manual quality control of these and other high channel-density detectors requires enormous amounts of labor and can be prone to errors. Here, we explore computer vision applications to either augment or fully replace visual inspections done by humans. We investigated convolutional neural networks for image classification and autoencoders for anomalies detection. Two proof-of-concept studies will be presented.

hep-ex↗

arXiv2021

Response of a CMS HGCAL silicon-pad electromagnetic calorimeter prototype to 20-300 GeV positrons

The Compact Muon Solenoid Collaboration is designing a new high-granularity endcap calorimeter, HGCAL, to be installed later this decade. As part of this development work, a prototype system was built, with an electromagnetic section consisting of 14 double-sided structures, providing 28 sampling layers. Each sampling layer has an hexagonal module, where a multipad large-area silicon sensor is glued between an electronics circuit board and a metal baseplate. The sensor pads of approximately 1 cm$^2$ are wire-bonded to the circuit board and are readout by custom integrated circuits. The prototype was extensively tested with beams at CERN's Super Proton Synchrotron in 2018. Based on the data collected with beams of positrons, with energies ranging from 20 to 300 GeV, measurements of the energy resolution and linearity, the position and angular resolutions, and the shower shapes are presented and compared to a detailed Geant4 simulation.

physics.ins-det↗

arXiv2020

Construction and commissioning of CMS CE prototype silicon modules

As part of its HL-LHC upgrade program, the CMS Collaboration is developing a High Granularity Calorimeter (CE) to replace the existing endcap calorimeters. The CE is a sampling calorimeter with unprecedented transverse and longitudinal readout for both electromagnetic (CE-E) and hadronic (CE-H) compartments. The calorimeter will be built with $\sim$30,000 hexagonal silicon modules. Prototype modules have been constructed with 6-inch hexagonal silicon sensors with cell areas of 1.1~$cm^2$, and the SKIROC2-CMS readout ASIC. Beam tests of different sampling configurations were conducted with the prototype modules at DESY and CERN in 2017 and 2018. This paper describes the construction and commissioning of the CE calorimeter prototype, the silicon modules used in the construction, their basic performance, and the methods used for their calibration.

physics.ins-det↗

arXiv2020

The DAQ system of the 12,000 Channel CMS High Granularity Calorimeter Prototype

The CMS experiment at the CERN LHC will be upgraded to accommodate the 5-fold increase in the instantaneous luminosity expected at the High-Luminosity LHC (HL-LHC). Concomitant with this increase will be an increase in the number of interactions in each bunch crossing and a significant increase in the total ionising dose and fluence. One part of this upgrade is the replacement of the current endcap calorimeters with a high granularity sampling calorimeter equipped with silicon sensors, designed to manage the high collision rates. As part of the development of this calorimeter, a series of beam tests have been conducted with different sampling configurations using prototype segmented silicon detectors. In the most recent of these tests, conducted in late 2018 at the CERN SPS, the performance of a prototype calorimeter equipped with ${\approx}12,000\rm{~channels}$ of silicon sensors was studied with beams of high-energy electrons, pions and muons. This paper describes the custom-built scalable data acquisition system that was built with readily available FPGA mezzanines and low-cost Raspberry PI computers.

physics.ins-det↗