The CLAS12 Software Framework and Event Reconstruction

We describe offline event reconstruction for the CEBAF Large Acceptance Spectrometer at 12 GeV (CLAS12), including an overview of the offline reconstruction framework and software tools, a description of the algorithms developed for the individual detector subsystems, and the overall approach for charged and neutral particle identification. We also present the scheme for data processing and the code management procedures

The CLAS12 software framework and event reconstruction

We describe offline event reconstruction for the CEBAF Large Acceptance Spectrometer at 12 GeV (CLAS12), including an overview of the offline reconstruction framework and software tools, a description of the algorithms developed for the individual detector subsystems, and the overall approach for charged and neutral particle identification. We also present the scheme for data processing and the code management procedures

Studies for the Commissioning of the CERN CMS Silicon Strip Tracker

In 2008 the Large Hadron Collider (LHC) at CERN will start producing proton-proton collisions of unprecedented energy. One of its main experiments is the Compact Muon Solenoid (CMS), a general purpose detector, optimized for the search of the Higgs boson and super symmetric particles. The discovery potential of the CMS detector relies on a high precision tracking system, made of a pixel detector and the largest silicon strip Tracker ever built. In order to operate successfully a device as complex as the CMS silicon strip Tracker, and to fully exploit its potential, the properties of the hardware need to be characterized as precisely as possible, and the reconstruction software needs to be commissioned with physics signals. A number of issues were identified and studied to commission the detector, some of which concern the entire Tracker, while some are specific to the Tracker Outer Barrel (TOB): - the time evolution of the signals in the readout electronics need to be precisely measured and correctly simulated, as it affects the expected occupancy and the data volume, critical issues in high-luminosity running; - the electronics coupling between neighbouring channels affects the cluster size and hence the hit resolution, the tracking precision, the occupancy and the data volume; - the mechanical structure of the Rods (the sub-assemblies of the TOB) is mostly made of carbon fiber elements; aluminum inserts glued to the carbon fi ber frame provide efficient cooling contacts between the silicon detectors and the thin cooling pipe, made of a copper-nickel alloy; the different thermal expansion coefficients of the various components induce stresses on the structure when this is cooled down to the operating temperature, possibly causing small deformations; a detailed characterization of the geometrical precision of the rods and of its possible evolution with temperature is a valuable input for track reconstruction in CMS. These and other issues were studied in this thesis. For this purpose, a large scale test setup, designed to study the detector performance by tracking cosmic muons, was operated over several months. A dedicated trigger system was set up, to select tracks synchronous with the fast readout electronics, and to be able to perform a precise measurement of the time evolution of the front-end signals. Data collected at room temperature and at the Tracker operating temperature of -10°C were used to test reconstruction and alignment algorithms for the Tracker, as well as to perform a detailed qualification of the geometry and the functionality of the structures at different temperatures

Image-based terrain modeling with thematic mapper applied to resolving the limit of Holocene Lake expansion in the Great Salt Lake Desert, Utah, part 1

The LANDSAT Thematic Mapper (TM) scanner records reflected solar energy from the earth's surface in six wavelength regions, or bands, and one band that records emitted energy in the thermal region, giving a total of seven bands. Useful research was extracted about terrain morphometry from remote sensing measurements and this information is used in an image-based terrain model for selected coastal geomorphic features in the Great Salt Lake Desert (GSLD). Technical developments include the incorporation of Aerial Profiling of Terrain System (APTS) data in satellite image analysis, and the production and use of 3-D surface plots of TM reflectance data. Also included in the technical developments is the analysis of the ground control point spatial distribution and its affects on geometric correction, and the terrain mapping procedure; using satellite data in a way that eliminates the need to degrade the data by resampling. The most common approach for terrain mapping with multispectral scanner data includes the techniques of pattern recognition and image classification, as opposed to direct measurement of radiance for identification of terrain features. The research approach in this investigation was based on an understanding of the characteristics of reflected light resulting from the variations in moisture and geometry related to terrain as described by the physical laws of radiative transfer. The image-based terrain model provides quantitative information about the terrain morphometry based on the physical relationship between TM data, the physical character of the GSLD, and the APTS measurements

Jet Energy Corrections with Graph Neural Network Regression

The LHC particle accelerator at CERN probes the elementary building blocks of matter by colliding protons at a center-of-mass energy of √s = 13 TeV. Collimated sprays of particles arise when quarks and gluons are produced at high energies, that are reconstructed from measured data and clustered together into jets. Accurate measurements of the energy of jets are paramount for sensitive particle physics analyses at the CMS experiment. Jet energy corrections are for that reason used to map measurements towards Monte Carlo simulated truth values, which are independent of detector response. The aim of this thesis is to improve upon the standard jet energy corrections by utilizing deep learning. Recent advancements on learning from point clouds in the machine learning community have been adopted in particle physics studies to improve jet flavor classification accuracy. This includes representing jet constituents as an unordered set, or a so-called “particle cloud”. Two highly performant models suitable for such data are the set-based Particle Flow Network and the graph-based ParticleNet. A natural next step in the advancement of jet energy corrections is to adopt a similar methodology, only changing the problem statement from classification to regression. The deep learning models developed in this work provide energy corrections that are generically applicable to differently flavored jets. Their performance is presented in the form of jet energy response resolution and reduction in flavor dependence. The models achieve state of the art performance for both metrics, significantly surpassing the standard corrections benchmark

Calibration and Alignment of the CMS Silicon Tracking Detector

The Large Hadron Collider (LHC) will dominate the high energy physics program in the coming decade. The discovery of the standard model Higgs boson and the discovery of super-symmetric particles are within the reach at the energy scale explored by the LHC. However, the high luminosity and the high energy of the colliding protons lead to challenging demands on the detectors. The hostile radiation environment requires irradiation hard detectors, where the innermost subdetectors, consisting of silicon modules, are most affected. This thesis is devoted to the calibration and alignment of the silicon tracking detector. Electron test beam data, taken at DESY, have been used to investigate the performance of detector modules which previously were irradiated with protons up to a dose expected after 10 years of operation. The irradiated sensors turned out to be still better than required. The performance of the inner tracking systems will be dominated by the degree to which the positions of the sensors can be determined. Only a track based alignment procedure can reach the required precision. Such an alignment procedure is a major challenge given that about 50000 geometry constants need to be measured. Making use of the novel minimization program Millepede II an alignment strategy has been developed in which all detector components are aligned simultaneously, as many sources of information as possible are used, and all correlations between the position parameters of the detectors are taken into account. Utilizing simulated data, a proof of concept of the alignment strategy is shown

A facility to Search for Hidden Particles (SHiP) at the CERN SPS

A new general purpose fixed target facility is proposed at the CERN SPS accelerator which is aimed at exploring the domain of hidden particles and make measurements with tau neutrinos. Hidden particles are predicted by a large number of models beyond the Standard Model. The high intensity of the SPS 400~GeV beam allows probing a wide variety of models containing light long-lived exotic particles with masses below ${\cal O}$(10)~GeV/c$^2$, including very weakly interacting low-energy SUSY states. The experimental programme of the proposed facility is capable of being extended in the future, e.g. to include direct searches for Dark Matter and Lepton Flavour Violation.Comment: Technical Proposa

Calorimeter Reconstruction Innovations for the LHCb Experiment

Aquesta tesi se centra en contribucions de software per l'experiment LHCb al CERN, específicament pel sistema de calorímetres en el context de l'anomenat Upgrade I. Les contribucions principals se centren en l'estudi d'algoritmes alternatius per la reconstrucció de dades del calorímetre d'LHCb. En el primer enfocament, s'utilitza una tècnica d'aprenentatge profund segmentat, basada en descompondre el problema de reconstrucció en passos que són apresos per xarxes neuronals convolucionals petites. Tot i que els resultats són prometedors, el mètode manca d'un motor d'inferència eficient dins del marc de software d'LHCb. En el segon enfocament, es presenta un algoritme de reconstrucció basat en grafs, que presenta una resolució dels clústers reconstruits equivalent al mètode usat a l'expeiment però amb una eficiència més alta i un temps d'execució significativament millorat. Aquesta proposta ha passat a ser la solució predeterminada per la reconstrucció del calorímetre durant el període de presa de dades actual anomenat Run 3. A més a més, aquesta tesis també inclou una primera proposta per millorar l'algoritme actual de reconstrucció del calorímetre en el marc del sistema de trigger en GPU, anomenat Allen, mentres que les dues propostes anteriors estan dissenyats per la seqüència de reconstrucció del trigger en CPU, anomenat HLT2. D'altra banda, la tesi aborda part de la posada en marxa del calorímetre pel Run 3, detallant la tasca de time alignment per al calorímetre Electromagnètic i l'Hadrònic. El que implica l'adaptació del mètode utilitzat anteriorment a la nova electrònica, la recopilació i l'anàlisi de dades, i donar un alineament temporal als aproximadament 10,000 canals dels calorímetres amb una precisió d'1 ns.Esta tesis se centra en contribuciones de software para el experimento LHCb en el CERN, específicamente para el sistema de calorímetros en el contexto del llamado Upgrade I. Las contribuciones principales se centran en el estudio de algoritmos alternativos para la reconstrucción de datos de los calorímetros. El primer enfoque utiliza una técnica de aprendizaje profundo segmentado, descomponiendo el problema de reconstrucción en pasos que son aprendidos por pequeñas redes neuronales convolucionales. Aunque los resultados son prometedores, el método carecía de un motor de inferencia eficiente dentro del marco de software de LHCb. El segundo enfoque presenta un algoritmo de reconstrucción basado en grafos, con una resolución de los clústeres reconstruidos equivalente al método existente en LHCb, pero con una mayor eficiencia y un tiempo de ejecución significativamente mejorado. Esta propuesta ha pasado a ser la solución predeterminada para la reconstrucción del calorímetro durante el período de toma de datos actual llamado Run 3. Además, esta tesis también incluye una primera propuesta para mejorar el algoritmo actual de reconstrucción del calorímetro en el marco del sistema de trigger en GPU, llamado Allen, mientras que los dos enfoques anteriores están diseñados para la secuencia de reconstrucción del trigger en CPU, llamado HLT2. Por otro lado, la tesis aborda parte de la puesta en marcha de los calorímetros para el Run 3, detallando la tarea de time alignment para los calorímetros Electromagnético y Hadrónico. Esto implica la adaptación del método anterior a la nueva electrónica, la recopilación y el análisis de datos, y hacer la alineación temporal de los aproximadamente 10,000 canales de los calorímetros con una precisión de 1 ns.This thesis focuses on software contributions to the LHCb experiment at CERN, specifically for the Calorimeter system in LHCb Upgrade I context. The main contributions concern the study of alternative algorithms for calorimeter data reconstruction. The first approach employs a segmented deep learning technique, breaking down the reconstruction problem into steps learned by small convolutional neural networks. Although promising, it lacked an efficient inference engine inside the LHCb framework. The second approach presents a graph-based clustering algorithm, showing equivalent cluster resolution to the LHCb's existing method, but with higher efficiency and significantly improved execution time, which is now the default solution for calorimeter reconstruction in the Run 3 period. This work also comprises a first approach to improve the current calorimeter reconstruction algorithm in the GPU Allen framework for HLT1, while the other approaches aim to the CPU reconstruction sequence in HLT2. Additionally, the thesis addresses part of the calorimeter commissioning for Run 3, detailing the time alignment task for the Electromagnetic and Hadronic calorimeters. This involves the adaptation of the previous method to new electronics, gathering and analyzing data, and providing fine time alignment for the around 10,000 calorimeter channels within 1 ns precision

Space-Based Cosmic-Ray and Gamma-Ray Detectors: a Review

Prepared for the 2014 ISAPP summer school, this review is focused on space-borne and balloon-borne cosmic-ray and gamma-ray detectors. It is meant to introduce the fundamental concepts necessary to understand the instrument performance metrics, how they tie to the design choices and how they can be effectively used in sensitivity studies. While the write-up does not aim at being complete or exhaustive, it is largely self-contained in that related topics such as the basic physical processes governing the interaction of radiation with matter and the near-Earth environment are briefly reviewed.Comment: 86 pages, 70 figures, prepared for the 2014 ISAPP summer school. Change log in the writeup, ancillary material at https://bitbucket.org/lbaldini/crdetector

Navigating in Patient Space Using Camera Pose Estimation Relative to the External Anatomy

Ultrasound probe localization is essential for volumetric imaging with a 2D ultrasound probe, and for establishing a recorded anatomical context for ultrasound-guided surgery and for longitudinal studies. The existing techniques for probe localization, however, require external tracking devices, making them inconvenient for clinical use. In addition, the probe pose is typically measured with respect to a fixed coordinate system independent of the patient’s anatomy, making it difficult to correlate ultrasound studies across time. This dissertation concerns the development and evaluation of a novel self-contained ultrasound probe tracking system, which navigates the probe in patient space using camera pose estimation relative to the anatomical context. As the probe moves in patient space, a video camera on the probe is used to automatically identify natural skin features and subdermal cues, and match them with a pre-acquiring high-resolution 3D surface map that serves as an atlas of the anatomy. We have addressed the problem of distinguishing rotation from translation by including an inertial navigation system (INS) to accurately measure rotation. Experiments on both a phantom containing an image of human skin (palm) as well as actual human upper extremity (fingers, palm, and wrist) validate the effectiveness of our approach. We have also developed a real-time 3D interactive visualization system that superimposes the ultrasound data within the anatomical context of the exterior of the patient, to permit accurate anatomic localization of ultrasound data. The combination of the proposed tracking approach and the visualization system may have broad implications for ultrasound imaging, permitting the compilation of volumetric ultrasound data as the 2D probe is moved, as well as comparison of real-time ultrasound scans registered with previous scans from the same anatomical location. In a broader sense, tools that self-locate by viewing the patient’s exterior could have broad beneficial impact on clinical medicine