The ALICE experiment at the CERN LHC will feature several upgrades for Run 3, one of which is a new Inner Tracking System (ITS). The ITS upgrade is currently under development and commissioning, and ...will be installed during the ongoing long shutdown 2. A number of factors will have an impact on the performance and readout efficiency of the ITS in run 3, and to that end, a simulation model of the readout logic in the ALPIDE pixel sensor chips for the ITS was developed, using the SystemC library for system level modeling in C++. This simulation model is three orders of magnitude faster than a normal HDL simulation of the chip and facilitates simulations of an increased number of events for a large portion of the detector. In this paper, we present simulation results, where we have been able to quantify detector performance under different running conditions. The results are used for system configuration as well as for the ongoing development of the readout electronics.
A prototype of a new type of calorimeter has been designed and constructed, based on a silicon–tungsten sampling design using pixel sensors with digital readout. It makes use of the ALPIDE sensor ...developed for the ALICE Inner Tracking System (ITS) upgrade. A binary readout is possible due to the pixel size of ≈30×30μm2. This prototype has been successfully tested with cosmic muons and with test beams at DESY and the CERN SPS. We report on performance results obtained at DESY, showing good energy resolution and linearity, and compare to detailed MC simulations. Also shown are preliminary results of the high-energy performance as measured at the SPS. The two-shower separation capabilities are discussed.
•First fully digital electromagnetic calorimeter with high-speed readout built.•ALPIDE pixel sensors work well in high particle-density environment.•Basic calorimetric performance of pixel calorimeter on par with state of the art.•Has unique capabilities in terms of position resolution and two-shower separation.
Abstract
The ALICE detector is undergoing an upgrade for run 3 at the LHC. A new Inner Tracking System (ITS) is part of this upgrade. The upgraded ALICE ITS features the ALPIDE, a monolithic active ...pixel sensor. Due to IC fabrication variations and radiation damage, the threshold values for the ALPIDE chips in the ITS need to be measured and adjusted periodically to ensure the quality of data. The calibration is implemented within the ALICE Online-Offline (O
2
) computing system, thus it runs in the same framework as the normal operations. This paper describes the concept and first implementation of the charge sensitivity scanning procedures for the upgraded ALICE ITS in the ALICE O
2
system, and demonstrates the first results of the scanning of the data taken from the installed ITS.
Abstract
The University of Bergen is involved in developing two calorimeters: the pixel section of
the Electromagnetic Forward Calorimeter for the ALICE Upgrade to be installed during LS3 for
...data-taking in the period 2029–2032 and the Digital Tracking Calorimeter for the proton Computed
Tomography prototype. Both designs utilize pixel sensors and require a reliable connection to the
readout which is positioned approximately 5 meters away. Furthermore, the structural design of
these calorimeters calls for a compact layer assembly. This paper provides the strategies
deployed to meet these critical requirements, focusing on the implementation of the chip-on-flex
assembly, and the ultrasonic welding techniques. It further describes other design considerations
essential for maintaining good signal integrity. Finally, it gives insight into the experiences
and challenges faced while working with the prototypes, both in the laboratory and test beam
setups.
We propose a novel technique for reconstructing charged particles in digital tracking calorimeters using reinforcement learning aiming to benefit from the rapid progress and success of neural network ...architectures without the dependency on simulated or manually-labeled data. Here we optimize by trial-and-error a behavior policy acting as an approximation to the full combinatorial optimization problem, maximizing the physical plausibility of sampled trajectories. In modern processing pipelines used in high energy physics and related applications, tracking plays an essential role allowing to identify and follow charged particle trajectories traversing particle detectors. Due to the high multiplicity of charged particles and their physical interactions, randomly deflecting the particles, the reconstruction is a challenging undertaking, requiring fast, accurate and robust algorithms. Our approach works on graph-structured data, capturing track hypotheses through edge connections between particles in the detector layers. We demonstrate in a comprehensive study on simulated data for a particle detector used for proton computed tomography, the high potential as well as the competitiveness of our approach compared to a heuristic search algorithm and a model trained on ground truth. Finally, we point out limitations of our approach, guiding towards a robust foundation for further development of reinforcement learning based tracking.
Gradient-based optimization using algorithmic derivatives can be a useful technique to improve engineering designs with respect to a computer-implemented objective function. Likewise, uncertainty ...quantification through computer simulations can be carried out by means of derivatives of the computer simulation. However, the effectiveness of these techniques depends on how 'well-linearizable' the software is. In this study, we assess how promising derivative information of a typical proton computed tomography (pCT) scan computer simulation is for the aforementioned applications.
This study is mainly based on numerical experiments, in which we repeatedly evaluate three representative computational steps with perturbed input values. We support our observations with a review of the algorithmic steps and arithmetic operations performed by the software, using debugging techniques.
The model-based iterative reconstruction (MBIR) subprocedure (at the end of the software pipeline) and the Monte Carlo (MC) simulation (at the beginning) were piecewise differentiable. However, the observed high density and magnitude of jumps was likely to preclude most meaningful uses of the derivatives. Jumps in the MBIR function arose from the discrete computation of the set of voxels intersected by a proton path, and could be reduced in magnitude by a 'fuzzy voxels' approach. The investigated jumps in the MC function arose from local changes in the control flow that affected the amount of consumed random numbers. The tracking algorithm solves an inherently non-differentiable problem.
Besides the technical challenges of merely applying AD to existing software projects, the MC and MBIR codes must be adapted to compute smoother functions. For the MBIR code, we presented one possible approach for this while for the MC code, this will be subject to further research. For the tracking subprocedure, further research on surrogate models is necessary.
First experimental results are presented on event-by-event net-proton fluctuation measurements in Pb–Pb collisions at sNN=2.76 TeV, recorded by the ALICE detector at the CERN LHC. The ALICE detector ...is well suited for such studies due to its excellent particle identification capabilities and large acceptance, which is crucial for fluctuation analysis. The studies are focussed on second order cumulants, but the analysis technique used is more general and will be applied, in the near future, also to higher order cumulants.
Abstract
Objective.
Proton therapy is highly sensitive to range uncertainties due to the nature of the dose deposition of charged particles. To ensure treatment quality, range verification methods ...can be used to verify that the individual spots in a pencil beam scanning treatment fraction match the treatment plan. This study introduces a novel metric for proton therapy quality control based on uncertainties in range verification of individual spots.
Approach.
We employ uncertainty-aware deep neural networks to predict the Bragg peak depth in an anthropomorphic phantom based on secondary charged particle detection in a silicon pixel telescope designed for proton computed tomography. The subsequently predicted Bragg peak positions, along with their uncertainties, are compared to the treatment plan, rejecting spots which are predicted to be outside the 95% confidence interval. The such-produced spot rejection rate presents a metric for the quality of the treatment fraction.
Main results.
The introduced spot rejection rate metric is shown to be well-defined for range predictors with well-calibrated uncertainties. Using this method, treatment errors in the form of lateral shifts can be detected down to 1 mm after around 1400 treated spots with spot intensities of 1 × 10
7
protons. The range verification model used in this metric predicts the Bragg peak depth to a mean absolute error of 1.107 ± 0.015 mm.
Significance.
Uncertainty-aware machine learning has potential applications in proton therapy quality control. This work presents the foundation for future developments in this area.
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