Modeling Charge Collection in Silicon Pixel Detectors for Proton Therapy Applications.

Monolithic active pixel sensors are used for charged particle tracking in many applications, from medical physics to astrophysics. The Bergen pCT collaboration designed a sampling calorimeter for proton computed tomography, based entirely on the ALICE PIxel DEtector (ALPIDE). The same telescope can be used for in-situ range verification in particle therapy.

Exploration of differentiability in a proton computed tomography simulation framework.

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.

Uncertainty-aware spot rejection rate as quality metric for proton therapy using a digital tracking calorimeter.

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.

Towards Neural Charged Particle Tracking in Digital Tracking Calorimeters With Reinforcement Learning.

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.

Investigating particle track topology for range telescopes in particle radiography using convolutional neural networks.

Background: Proton computed tomography (pCT) and radiography (pRad) are proposed modalities for improved treatment plan accuracy and treatment validation in proton therapy. The pCT system of the Bergen pCT collaboration is able to handle very high particle intensities by means of track reconstruction. However, incorrectly reconstructed and secondary tracks degrade the image quality.