The reconstruction of electrons and photons in CMS depends on the topological clustering of the energy deposited by an incident particle in different crystals of the electromagnetic calorimeter (ECAL). The currently used algorithm cannot account for the energy deposits coming from the pileup (secondary collisions) efficiently. The performance of this algorithm is expected to degrade during the LHC Run 3 because of the larger average pileup level and the increasing level of noise due to the aging of the ECAL detector. In this paper, we explore new techniques for energy reconstruction in ECAL using state-of-the-art machine learning algorithms like graph neural networks and self-attention modules.
Radiomics features extract quantitative information from medical images, towards the derivation of biomarkers for clinical tasks, such as diagnosis, prognosis, or treatment response assessment. Different image discretization parameters (e.g. bin number or size), convolutional filters, segmentation perturbation, or multi-modality fusion levels can be used to generate radiomics features and ultimately signatures. Commonly, only one set of parameters is used; resulting in only one value or flavour for a given RF. We propose tensor radiomics (TR) where tensors of features calculated with multiple combinations of parameters (i.e. flavours) are utilized to optimize the construction of radiomics signatures. We present examples of TR as applied to PET/CT, MRI, and CT imaging invoking machine learning or deep learning solutions, and reproducibility analyses: (1) TR via varying bin sizes on CT images of lung cancer and PET-CT images of head neck cancer (HNC) for overall survival prediction. A hybrid deep neural network, referred to as TR-Net, along with two ML-based flavour fusion methods showed improved accuracy compared to regular rediomics features. (2) TR built from different segmentation perturbations and different bin sizes for classification of late-stage lung cancer response to first-line immunotherapy using CT images. TR improved predicted patient responses. (3) TR via multi-flavour generated radiomics features in MR imaging showed improved reproducibility when compared to many single-flavour features. (4) TR via multiple PET/CT fusions in HNC. Flavours were built from different fusions using methods, such as Laplacian pyramids and wavelet transforms. TR improved overall survival prediction. Our results suggest that the proposed TR paradigm has the potential to improve performance capabilities in different medical imaging tasks.
A Phase-II upgrade of the ATLAS detector for the High Luminosity LHC will affect most of the detector’s subsystems, including the Trigger and Data Acquisition (TDAQ) subsystem. The core part of the TDAQ subsystem in Phase-II is the Global Trigger, which is capable of performing algorithms on a full-granularity calorimeter cells as well as applying topological requirements and refining the trigger objects from the upstream Feature Extractors. A Global Common Module (GCM) is the main building block of the Global Trigger. A design of an additional, standalone, Global Trigger Versatile Module (GVM) has been completed in compliance with the hardware specifications of the Global Trigger prototype. While possessing high processing and bandwidth capabilities, the GVM is intended to be used as an auxiliary hardware component for operational, testing and development purposes of the Global Trigger as well as other resource and bandwidth critical projects. A high-density PCB of the GVM is designed for high-speed data transmission and features 25.8 Gb/s capable Finisar BOA optical modules, a modern Xilinx Ultrascale+ VU13P FPGA as well as other hardware components essential for the Global Trigger. GCM development firmware tests, VU13P FPGA’s and Finisar BOA optical modules’ performance evaluation as well as the module’s main hardware functionality verification composes a full testing program for the GVM, which was passed successfully.
Many nuclear safety applications need fast, portable, and accurate imagers to better locate radiation sources. The Rotating Scatter Mask (RSM) system is an emerging device with the potential to meet these needs. The main challenge is the under-determined nature of the data acquisition process: the dimension of the measured signal is far less than the dimension of the image to be reconstructed. To address this challenge, this work aims to fuse model-based sparsity-promoting regularization and a data-driven deep neural network denoising image prior to perform image reconstruction. An efficient algorithm is developed and produces superior reconstructions relative to current approaches.
In astrophysics, several key questions in the hard X-/soft Gamma-ray range (>100 keV) require sensitivity and angular resolution that are hardly achievable with current technologies. Therefore, a new kind of instrument able to focus hard X and gamma-rays is essential. Broad band Laue lenses seem to be the only solution to fulfil these requirements, significantly improving the sensitivity and angular resolution of the X-/gamma-ray telescopes. This type of high-energy optics will require highly performing focal plane detectors in terms of detection efficiency, spatial resolution, and spectroscopy. This paper presents the results obtained in the project "Technological Readiness Increase for Laue Lenses (TRILL)" framework using a Caliste-HD detector module. This detector is a pixel spectrometer developed at CEA (Commissariat à Energie Atomique, Saclay, France). It is used to acquire spectroscopic images of the focal spot produced by Laue Lens bent crystals under a hard X-ray beam at the LARIX facility (University of Ferrara, Italy).
