CHEP 2016 Conference, San Francisco, October 8-14, 2016 Report of Contributions https://indico.cern.ch/e/505613 CHEP 2016 Conf … / Report of Contributions Experiment Management System f … Contribution ID: 0 Type: Poster Experiment Management System for the SND Detector Tuesday, 11 October 2016 16:30 (15 minutes) We present a new experiment management system for the SND detector at the VEPP-2000 col- lider (Novosibirsk). Substantially, it includes as important part operator access to experimental databases (configuration, conditions and metadata). The system is designed in client-server architecture. A user interacts with it via web-interface. The server side includes several logical layers: user interface templates, template variables description and initialization, implementation details like database interaction. The templates are believed to have a simple enough structure to be used not only IT professionals but also by physicists. Experiment configuration, conditions and metadata are stored in a database being managedby DBMS MySQL, ones being composed as records having hierarchical structure. To implement the server side NodeJS, a modern JavaScript framework, has been chosen. A new template engine is designed. The important feature of our engine is asynchronous computations hiding. The engine provides heterogeneous synchronous-style expressions (including synchronous or asynchronous values or functions calls). This helps template creators to focus on values toget but not on callbacks to handle. A part of the system is put into production. It includes templates dealing with showing and edit- ing first level trigger configuration and equipment configuration and also showing experiment metadata and experiment conditions data index. Secondary Keyword (Optional) Monitoring Primary Keyword (Mandatory) Databases Tertiary Keyword (Optional) Primary author: Mr PUGACHEV, Konstantin (Budker Institute of Nuclear Physics (RU)) Co-author: KOROL, Aleksandr (Budker Institute of Nuclear Physics (RU)) Presenter: Mr PUGACHEV, Konstantin (Budker Institute of Nuclear Physics (RU)) Session Classification: Posters A / Break Track Classification: Track 2: Offline Computing October 6, 2021 Page 1 CHEP 2016 Conf … / Report of Contributions Reconstruction software of the sili … Contribution ID: 2 Type: Oral Reconstruction software of the silicon tracker of DAMPE mission Thursday, 13 October 2016 11:00 (15 minutes) DAMPE is a powerful space telescope launched in December 2015, able to detect electrons and pho- tons in a wide range of energy (5 GeV to 10 TeV) and with unprecedented energy resolution. Silicon tracker is a crucial component of detector, able to determine the direction of detected particles and trace the origin of incoming gamma rays. This contribution covers the reconstruction software of the tracker, comprising the geometry convertor, track reconstruction and detector alignment al- gorithms. The convertor is an in-house, standalone system that converts the CAD drawings ofthe detector and implements the detector geometry in the GDML (Geometry Description Markup Lan- guage) format. Next, the particle track finding algorithm is described. Since the DAMPE tracker identifies independently the particle trajectory in two orthogonal projections, there is an inherent ambiguity in combining the two measurements. Therefore, the 3D track reconstruction becomes a computationally intensive task and the number of possible combinations increases quadratically with the number of particle tracks. To alleviate the problem, a special technique is developed, which reconstructs track fragments independently in two projections and combine the final result using a 3D Kalman fit of pre-selected points. Finally, the detector alignment algorithm allowsto align the detector geometry based on real data with precision better than the resolution of tracker. The algorithm optimises a set of around four thousand parameters (offsets and rotations ofdetect- ing elements) in an iterative procedure, based on the minimisation of the global likelihood fit of reconstructed tracks. Since the algorithm is agnostic of the detector premises, it could be used for similar optimisation problems with minor modifications by other projects. This contribution will give an insight into the developed algorithms and the results obtained during the first years of operational experience on ground and on orbit. Tertiary Keyword (Optional) Collaborative tools Secondary Keyword (Optional) Algorithms Primary Keyword (Mandatory) Reconstruction Primary author: Dr TYKHONOV, Andrii (Universite de Geneve (CH)) Co-author: Prof. WU, Xin (Universite de Geneve (CH)) Presenter: Dr TYKHONOV, Andrii (Universite de Geneve (CH)) Session Classification: Track 2: Offline Computing October 6, 2021 Page 2 CHEP 2016 Conf … / Report of Contributions Reconstruction software of the sili … Track Classification: Track 2: Offline Computing October 6, 2021 Page 3 CHEP 2016 Conf … / Report of Contributions HEPData - a repository for high en … Contribution ID: 3 Type: Oral HEPData - a repository for high energy physics data exploration Thursday, 13 October 2016 14:00 (15 minutes) The Durham High Energy Physics Database (HEPData) has been built up over the past fourdecades as a unique open-access repository for scattering data from experimental particle physics. Itis comprised of data points from plots and tables underlying over eight thousand publications, some of which are from the Large Hadron Collider (LHC) at CERN. HEPData has been rewritten from the ground up in the Python programming language andisnow based on the Invenio 3 framework. The software is open source with the current site available at http://hepdata.net with: 1) a more stream-lined submission system; 2) advanced submission reviewing functionalities; 3) powerful full repository search; 4) an interactive data plotting library; 5) an attractive, easy to use interface; and 6) a new data driven visual exploration tool. Here we will report on our efforts to bring findable, accessible, interoperable, and reusable (FAIR) principles to high energy physics. Our presentation will cover the background of HEPData, limitations of the current tool, and why we created the new system using Invenio 3. We will present our system by considering four im- portant aspects of the work: 1) the submission process; 2) making the data discoverable; 3) making data first class citable objects; and 4) making data interoperable and reusable. Tertiary Keyword (Optional) Visualization Primary Keyword (Mandatory) Databases Secondary Keyword (Optional) Preservation of analysis and data Primary author: Dr MAGUIRE, Eamonn James (CERN) Co-authors: Prof. KRAUSS, Frank Martin (University of Durham (GB)); Dr WATT, Graeme (Durham University); STYPKA, Jan Andrzej (AGH University of Science and Technology (PL)); HEINRICH, Lukas Alexander (New York University (US)); Dr WHALLEY, Michael (Durham University); Dr MELE, Salvatore (CERN) Presenter: HEINRICH, Lukas Alexander (New York University (US)) Session Classification: Track 8: Security, Policy and Outreach Track Classification: Track 8: Security, Policy and Outreach October 6, 2021 Page 4 CHEP 2016 Conf … / Report of Contributions Reconstruction of Micropattern D … Contribution ID: 4 Type: Oral Reconstruction of Micropattern Detector Signals using Convolutional Neural Networks Tuesday, 11 October 2016 14:45 (15 minutes) Micropattern gaseous detector (MPGD) technologies, such as GEMs or MicroMegas, are particu- larly suitable for precision tracking and triggering in high rate environments. Given their relatively low production costs, MPGDs are an exemplary candidate for the next generation of particle de- tectors. Having acknowledged these advantages, both the ATLAS and CMS collaborations at the LHC are exploiting these new technologies for their detector upgrade programs in the coming years. When MPGDs are utilized for triggering purposes, the measured signals need to be pre- cisely reconstructed within less than 200 ns, which can be achieved by the usage of FPGAs. In this work, we present for a novel approach to identify reconstructed signals, their timing and the corresponding spatial position on the detector. In particular, we study the effect of noise and dead readout strips on the reconstruction performance. Our approach leverages the potential of convolutional neural networks (CNNs), which have been recently manifesting an outstanding per- formance in a range of modeling tasks. The proposed neural network architecture of our CNNis designed simply enough, so that it can be modeled directly by an FPGA and thus provide precise information on reconstructed signals already in trigger level. Primary Keyword (Mandatory) Artificial intelligence/Machine learning Secondary Keyword (Optional) Reconstruction Tertiary Keyword (Optional) Primary author: Mrs FLEKOVA, Lucie (Technical University of Darmstadt) Co-authors: DUDDER, Andreas Christian (Johannes-Gutenberg-Universitaet Mainz (DE)); SCHOTT, Matthias (Johannes-Gutenberg-Universitaet Mainz (DE)) Presenter: Mrs FLEKOVA, Lucie (Technical University of Darmstadt) Session Classification: Track 1: Online Computing Track Classification: Track 1: Online Computing October 6, 2021 Page 5 CHEP 2016 Conf … / Report of Contributions Federated data storage system pro … Contribution ID: 6 Type: Poster Federated data storage system prototype for LHC experiments and data intensive science Thursday, 13 October 2016 16:30 (15 minutes) Rapid increase of data volume from the experiments running at the Large Hadron