Characterization of a CMOS pixel sensor for charged particle tracking

A prototype of the CMOS pixel sensor named Supix-1 has been fabricated and tested in order to investigate the feasibility of a pixelated tracker for a proposed Higgs factory, namely, the Circular Electron-Positron Collider (CEPC). The sensor, taped out with a 180 nm CMOS Image Sensor (CIS) process, consists of nine different pixel arrays varying in pixel pitches, diode sizes and geometries in order to study the particle detection performance of enlarged pixels. The test was carried out with a 55Fe radioactive source. Two soft X-ray peaks observed were used to calibrate the charge to voltage factor of the sensor. The pixel-wise equivalent noise charge, charge collection efficiency and signal-to-noise ratio were evaluated. A reconstruction method for clustering pixels of a signal has been developed and the cluster-wise performance was studied as well. The test results show that pixels with the area as large as of 21 × 84 μm have satisfactory noise level and charge collection performance, meeting general requirements for a pixel sensor. This contribution demonstrates that the CMOS pixel sensor with enlarged pitches, using the CIS technology, can be used in tracking for upcoming collider detectors akin to the CEPC.

Charged particle tracking with quantum annealing optimization

At the High Luminosity Large Hadron Collider (HL-LHC), traditional track reconstruction techniques that are critical for physics analysis will need to be upgraded to scale with track density. Quantum annealing has shown promise in its ability to solve combinatorial optimization problems amidst an ongoing effort to establish evidence of a quantum speedup. As a step towards exploiting such potential speedup, we investigate a track reconstruction approach by adapting the existing geometric Denby-Peterson (Hopfield) network method to the quantum annealing framework for HL-LHC conditions. We develop additional techniques to embed the problem onto existing and near-term quantum annealing hardware. Results using simulated annealing and quantum annealing with the D-Wave 2X system on the TrackML open dataset are presented, demonstrating the successful application of a quantum annealing algorithm to the track reconstruction challenge. We find that combinatorial optimization problems can effectively reconstruct tracks, suggesting possible applications for fast hardware-specific implementations at the HL-LHC while leaving open the possibility of a quantum speedup for tracking.

Towards a computer vision particle flow

In High Energy Physics experiments Particle Flow (PFlow) algorithms are designed to provide an optimal reconstruction of the nature and kinematic properties of the particles produced within the detector acceptance during collisions. At the heart of PFlow algorithms is the ability to distinguish the calorimeter energy deposits of neutral particles from those of charged particles, using the complementary measurements of charged particle tracking devices, to provide a superior measurement of the particle content and kinematics. In this paper, a computer vision approach to this fundamental aspect of PFlow algorithms, based on calorimeter images, is proposed. A comparative study of the state of the art deep learning techniques is performed. A significantly improved reconstruction of the neutral particle calorimeter energy deposits is obtained in a context of large overlaps with the deposits from charged particles. Calorimeter images with augmented finer granularity are also obtained using super-resolution techniques.

On the radiation tolerance of CMOS Monolithic Active Pixel Sensors with partially depleted active volume

CMOS Monolithic Active Pixel Sensors for charged particle tracking (CPS) form are ultra-light and highly granular silicon pixel detectors suited for highly sensitive charged particle tracking. Unlike to most other silicon radiation detectors, they rely on standard CMOS technology. This cost efficient approach allows for building particularly small and thin pixels but also introduced, until recently, substantially constraints on the design of the sensors. The most important among them is the missing compatibility with the use of PMOS transistors and depleted charge collection diodes in the pixel. Traditional CPS were thus first of all suited for vertex detectors of relativistic heavy ion and particle physics experiments, which require highest tracking accuracy in combination with moderate time resolution and radiation tolerance. This work reviews the R&D on understanding and improving the radiation tolerance of traditional CPS with non- and partially depleted active medium as pioneered by the MIMOSA-series developed by the IPHC Strasbourg. It introduces the specific measurement methods used to assess the radiation tolerance of those non-standard pixels. Moreover, it discusses the major mechanisms of radiation damage and procedures for radiation hardening, which allowed to extend the radiation tolerance of the devices by more than an order of magnitude.

ML Track Fitting in Nuclear Physics

Charged particle tracking represents the largest consumer of CPU resources in high data volume Nuclear Physics (NP) experiments. An effort is underway to develop machine learning (ML) networks that will reduce the resources required for charged particle tracking. Tracking in NP experiments represent some unique challenges compared to high energy physics (HEP). In particular, track finding typically represents only a small fraction of the overall tracking problem in NP. This presentation will outline the differences and similarities between NP and HEP charged particle tracking and areas where ML learning may provide a benefit. The status of the specific effort taking place at Jefferson Lab will also be shown.

The Silicon Tracking System of the CBM Experiment at FAIR

The Compressed Baryonic Matter (CBM) experiment at FAIR (Darmstadt, Germany) is designed to study the dense nuclear matter in a fixed target configuration with heavy ion beams up to kinetic energies of 11 AGeV for Au+Au collision. The charged particle tracking with below 2% momentum resolution will be performed by the Silicon Tracking System (STS) located in the aperture of a dipole magnet. The detector will be able to reconstruct secondary decay vertices of rare probes, e.g., multistrange hyperons, with 50 мm spatial resolution in the heavy-ion collision environment with up to 1000 charged particle per inelastic interaction at the 10 MHz collision rate. This task requires a highly granular fast detector with radiation tolerance enough to withstand a particle fluence of up to 1014 neq/cm2 1-MeV equivalent accumulated over several years of operation. The system comprises 8 tracking stations based on double-sided silicon microstrip sensors with 58 мm pitch and strips oriented at 7.5∘ stereo angle. The analog signals are read out via stacked microcables (up to 50 cm long) by the front-end electronics based on the STS-XYTER ASIC with self-triggering architecture. Detector modules with this structure will have a material budget between 0.3% and 1.5% radiation length increasing towards the periphery. First detector modules and ladders built from pre-final components have been operated in the demonstrator experiment mCBM at GSI-SIS18 (FAIR Phase-0) providing a test stand for the performance evaluation and system integration. The results of mSTS detector commissioning and the performance in the beam will be presented.

Feasibility Study of a New Cherenkov Detector for Improving Volcano Muography

Muography is an expanding technique for the investigation of the internal structure of targets of interest in geophysics. The flux of high penetrating muons produced by primary cosmic rays is attenuated by traversing kilometer size objects like X-ray flux is attenuated through the human body. This gives the possibility to study the internal structure of volcanoes or underground cavities, e.g., starting from the measure of the muon flux transmission through the target. Many groups of researchers working with this technique have to face with high background level that afflicts the reconstruction of muon tracks near the horizontal direction. An important source of background is the scattering of particles near the detector that produces a wrong reconstruction of the incoming direction. An innovative technique based on Cherenkov radiation was investigated by means of Monte Carlo simulations developed in Geant4 toolkit and MATLAB. The results reported in this paper show that the presented technique is able to correctly distinguish the incoming direction of particles with an efficiency higher than 98%. This new kind of detector could represent an alternative for high resolution charged particle tracking also for other applications.