Jet Momentum Resolution for the CMS Experiment and Distributed Data Caching Strategies

Accurately measured jets are mandatory for precision measurements of the Standard Model of particle physics as well as for searches for new physics. The increased instantaneous luminosity and center-of-mass energy at LHC Run 2 pose challenges for pileup mitigation and the measurement of jet characteristics. This thesis concentrates on using Z + jets events to calibrate the energy scale of jets recorded by the CMS detector in 2018. Furthermore, it proposes a new procedure for determining the jet momentum resolution using Z + jets events. This procedure is expected to allow cross-checking complementary measurement approaches and increasing the accuracy of the jet momentum resolution at the CMS experiment. Data-intensive end-user analyses in High Energy Physics such as the presented calibration of jets put enormous challenges on the computing infrastructure since requiring high data throughput. Besides the particle physics analysis, this thesis also focuses on accelerating data processing within a distributed computing infrastructure via a coordinated distributed caching approach. Coordinated placement of critical data within distributed caches and matching workflows to the most suitable host in terms of cached data allows for optimizing processing efficiency. Improving the processing of data-intensive workflows aims at shortening turnaround cycles and thus deriving physics results, e.g. the jet calibration results, faster

Dynamic Resource Extension for Data Intensive Computing with Specialized Software Environments on HPC Systems

Modern High Energy Physics (HEP) requires large-scale processing of extensive amounts of scientific data. The needed computing resources are currently provided statically by HEP specific computing centers. To increase the number of available resources, for example to cover peak loads, the HEP computing development team at KIT concentrates on the dynamic integration of additional computing resources into the HEP infrastructure. Therefore, we developed ROCED, a tool to dynamically request and integrate computing resources including resources at HPC centers and commercial cloud providers. Since these resources usually do not support HEP software natively, we rely on virtualization and container technologies, which allows us to run HEP workflows on these so called opportunistic resources. Additionally, we study the efficient processing of huge amounts of data on a distributed infrastructure, where the data is usually stored at HEP specific data centers and is accessed remotely over WAN. To optimize the overall data throughput and to increase the CPU efficiency, we are currently developing an automated caching system for frequently used data that is transparently integrated into the distributed HEP computing infrastructure

Space, Time, and Interaction: A Taxonomy of Corner Cases in Trajectory Datasets for Automated Driving

Trajectory data analysis is an essential component for highly automated driving. Complex models developed with these data predict other road users\u27 movement and behavior patterns. Based on these predictions - and additional contextual information such as the course of the road, (traffic) rules, and interaction with other road users - the highly automated vehicle (HAV) must be able to reliably and safely perform the task assigned to it, e.g., moving from point A to B. Ideally, the HAV moves safely through its environment, just as we would expect a human driver to do. However, if unusual trajectories occur, so-called trajectory corner cases, a human driver can usually cope well, but an HAV can quickly get into trouble. In the definition of trajectory corner cases, which we provide in this work, we will consider the relevance of unusual trajectories with respect to the task at hand. Based on this, we will also present a taxonomy of different trajectory corner cases. The categorization of corner cases into the taxonomy will be shown with examples and is done by cause and required data sources. To illustrate the complexity between the machine learning (ML) model and the corner case cause, we present a general processing chain underlying the taxonomy

Space, Time, and Interaction: A Taxonomy of Corner Cases in Trajectory Datasets for Automated Driving

Trajectory data analysis is an essential component for highly automated driving. Complex models developed with these data predict other road users' movement and behavior patterns. Based on these predictions — and additional contextual information such as the course of the road, (traffic) rules, and interaction with other road users — the highly automated vehicle (HAV) must be able to reliably and safely perform the task assigned to it, e.g., moving from point A to B. Ideally, the HAV moves safely through its environment, just as we would expect a human driver to do. However, if unusual trajectories occur, so-called trajectory corner cases, a human driver can usually cope well, but an HAV can quickly get into trouble. In the definition of trajectory corner cases, which we provide in this work, we will consider the relevance of unusual trajectories with respect to the task at hand. Based on this, we will also present a taxonomy of different trajectory corner cases. The categorization of corner cases into the taxonomy will be shown with examples and is done by cause and required data sources. To illustrate the complexity between the machine learning (ML) model and the corner case cause, we present a general processing chain underlying the taxonomy

Long-term impact of myocardial inflammation on quantitative myocardial perfusion-a descriptive PET/MR myocarditis study

PURPOSE Whether myocardial inflammation causes long-term sequelae potentially affecting myocardial blood flow (MBF) is unknown. We aimed to assess the effect of myocardial inflammation on quantitative MBF parameters, as assessed by 13N-ammonia positron emission tomography myocardial perfusion imaging (PET-MPI) late after myocarditis. METHODS Fifty patients with a history of myocarditis underwent cardiac magnetic resonance (CMR) imaging at diagnosis and PET/MR imaging at follow-up at least 6 months later. Segmental MBF, myocardial flow reserve (MFR), and 13N-ammonia washout were obtained from PET, and segments with reduced 13N-ammonia retention, resembling scar, were recorded. Based on CMR, segments were classified as remote (n = 469), healed (inflammation at baseline but no late gadolinium enhancement [LGE] at follow-up, n = 118), and scarred (LGE at follow-up, n = 72). Additionally, apparently healed segments but with scar at PET were classified as PET discordant (n = 18). RESULTS Compared to remote segments, healed segments showed higher stress MBF (2.71 mL*min$^{-1}$*g$^{-1}$ [IQR 2.18-3.08] vs. 2.20 mL*min$^{-1}$*g$^{-1}$ [1.75-2.68], p < 0.0001), MFR (3.78 [2.83-4.79] vs. 3.36 [2.60-4.03], p < 0.0001), and washout (rest 0.24/min [0.18-0.31] and stress 0.53/min [0.40-0.67] vs. 0.22/min [0.16-0.27] and 0.46/min [0.32-0.63], p = 0.010 and p = 0.021, respectively). While PET discordant segments did not differ from healed segments regarding MBF and MFR, washout was higher by ~ 30% (p < 0.014). Finally, 10 (20%) patients were diagnosed by PET-MPI as presenting with a myocardial scar but without a corresponding LGE. CONCLUSION In patients with a history of myocarditis, quantitative measurements of myocardial perfusion as obtained from PET-MPI remain altered in areas initially affected by inflammation. CMR = cardiac magnetic resonance; PET = positron emission tomography; LGE = late gadolinium enhancement

Long-term impact of myocardial inflammation on quantitative myocardial perfusion-a descriptive PET/MR myocarditis study.

PURPOSE Whether myocardial inflammation causes long-term sequelae potentially affecting myocardial blood flow (MBF) is unknown. We aimed to assess the effect of myocardial inflammation on quantitative MBF parameters, as assessed by 13N-ammonia positron emission tomography myocardial perfusion imaging (PET-MPI) late after myocarditis. METHODS Fifty patients with a history of myocarditis underwent cardiac magnetic resonance (CMR) imaging at diagnosis and PET/MR imaging at follow-up at least 6 months later. Segmental MBF, myocardial flow reserve (MFR), and 13N-ammonia washout were obtained from PET, and segments with reduced 13N-ammonia retention, resembling scar, were recorded. Based on CMR, segments were classified as remote (n = 469), healed (inflammation at baseline but no late gadolinium enhancement [LGE] at follow-up, n = 118), and scarred (LGE at follow-up, n = 72). Additionally, apparently healed segments but with scar at PET were classified as PET discordant (n = 18). RESULTS Compared to remote segments, healed segments showed higher stress MBF (2.71 mL*min-1*g-1 [IQR 2.18-3.08] vs. 2.20 mL*min-1*g-1 [1.75-2.68], p < 0.0001), MFR (3.78 [2.83-4.79] vs. 3.36 [2.60-4.03], p < 0.0001), and washout (rest 0.24/min [0.18-0.31] and stress 0.53/min [0.40-0.67] vs. 0.22/min [0.16-0.27] and 0.46/min [0.32-0.63], p = 0.010 and p = 0.021, respectively). While PET discordant segments did not differ from healed segments regarding MBF and MFR, washout was higher by ~ 30% (p < 0.014). Finally, 10 (20%) patients were diagnosed by PET-MPI as presenting with a myocardial scar but without a corresponding LGE. CONCLUSION In patients with a history of myocarditis, quantitative measurements of myocardial perfusion as obtained from PET-MPI remain altered in areas initially affected by inflammation. CMR = cardiac magnetic resonance; PET = positron emission tomography; LGE = late gadolinium enhancement

Advancing throughput of HEP analysis work-flows using caching concepts

High throughput and short turnaround cycles are core requirements for efficient processing of data-intense end-user analyses in High Energy Physics (HEP). Together with the tremendously increasing amount of data to be processed, this leads to enormous challenges for HEP storage systems, networks and the data distribution to computing resources for end-user analyses. Bringing data close to the computing resource is a very promising approach to solve throughput limitations and improve the overall performance. However, achieving data locality by placing multiple conventional caches inside a distributed computing infrastructure leads to redundant data placement and inefficient usage of the limited cache volume. The solution is a coordinated placement of critical data on computing resources, which enables matching each process of an analysis work-flow to its most suitable worker node in terms of data locality and, thus, reduces the overall processing time. This coordinated distributed caching concept was realized at KIT by developing the coordination service NaviX that connects an XRootD cache proxy infrastructure with an HTCondor batch system. We give an overview about the coordinated distributed caching concept and experiences collected on prototype system based on NaviX

Proceedings of the 4th bwHPC Symposium

The bwHPC Symposium 2017 took place on October 4th, 2017, Alte Aula, Tübingen. It focused on the presentation of scientific computing projects as well as on the progress and the success stories of the bwHPC realization concept. The event offered a unique opportunity to engage in an active dialogue between scientific users, operators of bwHPC sites, and the bwHPC support team