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Journal articles on the topic 'Atlas data'

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Published: 10 December 2022
Last updated: 29 January 2023

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1

Jones, RWL, DM South, and KS Cranmer. "ATLAS Data Preservation." Journal of Physics: Conference Series 664, no. 3 (December 23, 2015): 032017. http://dx.doi.org/10.1088/1742-6596/664/3/032017.

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2

Barisits, Martin, Mikhail Borodin, Alessandro Di Girolamo, Johannes Elmsheuser, Dmitry Golubkov, Alexei Klimentov, Mario Lassnig, Tadashi Maeno, Rodney Walker, and Xin Zhao. "ATLAS Data Carousel." EPJ Web of Conferences 245 (2020): 04035. http://dx.doi.org/10.1051/epjconf/202024504035.

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The ATLAS experiment at CERN’s LHC stores detector and simulation data in raw and derived data formats across more than 150 Grid sites world-wide, currently in total about 200PB on disk and 250PB on tape. Data have different access characteristics due to various computational workflows, and can be accessed from different media, such as remote I/O, disk cache on hard disk drives or SSDs. Also, larger data centers provide the majority of offline storage capability via tape systems. For the HighLuminosity LHC (HL-LHC), the estimated data storage requirements are several factors bigger than the present forecast of available resources, based on a flat budget assumption. On the computing side, ATLAS Distributed Computing was very successful in the last years with high performance and high throughput computing integration and in using opportunistic computing resources for the Monte Carlo simulation. On the other hand, equivalent opportunistic storage does not exist. ATLAS started the Data Carousel project to increase the usage of less expensive storage, i.e. tapes or even commercial storage, so it is not limited to tape technologies exclusively. Data Carousel orchestrates data processing between workload management, data management, and storage services with the bulk data resident on offline storage. The processing is executed by staging and promptly processing a sliding window of inputs onto faster buffer storage, such that only a small percentage of input data are available at any one time. With this project, we aim to demonstrate that this is the natural way to dramatically reduce our storage cost. The first phase of the project was started in the fall of 2018 and was related to I/O tests of the sites archiving systems. Phase II now requires a tight integration of the workload and data management systems. Additionally, the Data Carousel studies the feasibility to run multiple computing workflows from tape. The project is progressing very well and the results presented in this document will be used before the LHC Run 3.
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3

Vaniachine, A. "Data Challenges in ATLAS computing." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 502, no. 2-3 (April 2003): 446–49. http://dx.doi.org/10.1016/s0168-9002(03)00465-0.

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4

Cuenca Almenar, C., A. Corso-Radu, H. Hadavand, Y. Ilchenko, S. Kolos, K. Slagle, and A. Taffard. "ATLAS Online Data Quality Monitoring." Nuclear Physics B - Proceedings Supplements 215, no. 1 (June 2011): 304–6. http://dx.doi.org/10.1016/j.nuclphysbps.2011.04.038.

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5

Adelman, J., M. Baak, N. Boelaert, M. D'Onofrio, J. A. Frost, C. Guyot, M. Hauschild, et al. "ATLAS offline data quality monitoring." Journal of Physics: Conference Series 219, no. 4 (April 1, 2010): 042018. http://dx.doi.org/10.1088/1742-6596/219/4/042018.

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6

Ould-Saada, Farid. "ATLAS Open Data – Development of a simple-but-real HEP data analysis framework." EPJ Web of Conferences 245 (2020): 08023. http://dx.doi.org/10.1051/epjconf/202024508023.

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The ATLAS Collaboration at the Large Hadron Collider is releasing a new set of recorded and simulated data samples at a centre-of-mass energy of 13 TeV collected in pp collisions at the LHC. This new dataset was designed after an in-depth review of the usage of the previous release of samples at 8 TeV. That review showed that capacity-building is one of the most important and abundant uses of public ATLAS samples. To fulfil the requirements of the community and at the same time attract new users and use cases, we developed real analysis software based on ROOT in two of the most popular programming languages: C++ and Python. These so-called analysis frameworks are complex enough to reproduce with reasonable accuracy the results -figures and final yields- of published ATLAS Collaboration physics papers, but still light enough to be run on commodity hardware. With the computers that university students and regular classrooms typically have, students can explore LHC data with similar techniques to those used by current ATLAS analysers. We present the development path and the final result of these analysis frameworks, their products and how they are distributed to final users inside and outside the ATLAS community.
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7

ŞENKUL, Çetin. "DATA LOGGER VERİLERİNE GÖRE GÖKNAR ORMANLARININ SICAKLIK VE NEMLİLİK İSTEKLERİ." ATLAS JOURNAL 4, no. 12 (January 1, 2018): 944–52. http://dx.doi.org/10.31568/atlas.157.

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8

Maelfait, Hannelore, and Kathy Belpaeme. "The Belgian Coastal Atlas: moving from the classic static atlas to an interactive data-driven atlas." Journal of Coastal Conservation 14, no. 1 (October 27, 2009): 13–19. http://dx.doi.org/10.1007/s11852-009-0076-5.

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9

Borodin, Mikhail, Alessandro Di Girolamo, Edward Karavakis, Alexei Klimentov, Tatiana Korchuganova, Mario Lassnig, Tadashi Maeno, Siarhei Padolski, and Xin Zhao. "The ATLAS Data Carousel Project Status." EPJ Web of Conferences 251 (2021): 02006. http://dx.doi.org/10.1051/epjconf/202125102006.

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The High Luminosity upgrade to the LHC, which aims for a tenfold increase in the luminosity of proton-proton collisions at an energy of 14 TeV, is expected to start operation in 2028/29 and will deliver an unprecedented volume of scientific data at the multi-exabyte scale. This amount of data has to be stored, and the corresponding storage system must ensure fast and reliable data delivery for processing by scientific groups distributed all over the world. The present LHC computing and data management model will not be able to provide the required infrastructure growth, even taking into account the expected hardware technology evolution. To address this challenge, the Data Carousel R&D project was launched by the ATLAS experiment in the fall of 2018. State-of-the-art data and workflow management technologies are under active development, and their current status is presented here.
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10

Carroll, C., R. F. Noss, and Bruce A. Stein. "US conservation atlas needs biodiversity data." Science 376, no. 6589 (April 8, 2022): 144–45. http://dx.doi.org/10.1126/science.abo0526.

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11

Laycock, PJ, MA Chelstowska, TC Donszelmann, J. Guenther, A. Nairz, R. Nikolaidou, E. Shabalina, J. Strandberg, A. Taffard, and S. Wang. "ATLAS data preparation in run 2." Journal of Physics: Conference Series 898 (October 2017): 042050. http://dx.doi.org/10.1088/1742-6596/898/4/042050.

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12

DÍAZ, BASTIÁN, and ALFONSO R. ZERWEKH. "AXIGLUON PHENOMENOLOGY USING ATLAS DIJET DATA." International Journal of Modern Physics A 28, no. 26 (October 20, 2013): 1350133. http://dx.doi.org/10.1142/s0217751x13501339.

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In recent years, there has been a renewed interest on axigluons as part of a possible extension of strong interactions at high energies. In this paper, we use recent ATLAS measurements of the dijet spectrum in order to set limits on the axigluon mass and coupling to quarks. We pay special attention to the methodology used to extract the resonant contribution from theoretical simulations. Finally, we present some predictions for the next LHC run at [Formula: see text].
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13

Wilson, M. G. "Assessment of data quality in ATLAS." Journal of Physics: Conference Series 119, no. 4 (July 1, 2008): 042034. http://dx.doi.org/10.1088/1742-6596/119/4/042034.

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14

Farrell, Steve. "ATLAS Offline Data Quality System Upgrade." Journal of Physics: Conference Series 396, no. 5 (December 13, 2012): 052032. http://dx.doi.org/10.1088/1742-6596/396/5/052032.

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15

Kost, Corrie, Steven McDonald, Bryan Caron, and Wade Hong. "ATLAS Canada lightpath data transfer trial." Future Generation Computer Systems 19, no. 6 (August 2003): 1051–62. http://dx.doi.org/10.1016/s0167-739x(03)00082-7.

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16

Costanzo, D. "SUSY searches in early ATLAS data." Journal of Physics: Conference Series 110, no. 7 (May 1, 2008): 072007. http://dx.doi.org/10.1088/1742-6596/110/7/072007.

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17

殷, 新博. "The Research on Construc-tion of Power Grid Marketing Data Atlas Based on Data Atlas Technology." Advances in Energy and Power Engineering 07, no. 04 (2019): 55–62. http://dx.doi.org/10.12677/aepe.2019.74007.

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18

Barisits, Martin, Fernando Barreiro, Thomas Beermann, Karan Bhatia, Kaushik De, Arnaud Dubreuil, Johannes Elmsheuser, et al. "The Data Ocean Project." EPJ Web of Conferences 214 (2019): 04020. http://dx.doi.org/10.1051/epjconf/201921404020.

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Transparent use of commercial cloud resources for scientific experiments is a hard problem. In this article, we describe the first steps of the Data Ocean R&D collaboration between the high-energy physics experiment ATLAS together with Google Cloud Platform, to allow seamless use of Google Compute Engine and Google Cloud Storage for physics analysis. We start by describing the three preliminary use cases that were identified at the beginning of the project. The following sections then detail the work done in the data management system Rucio and the workflow management systems PanDA and Harvester to interface Google Cloud Platform with the ATLAS distributed computing environment, and show the results of the integration tests. Afterwards, we describe the setup and results from a full ATLAS user analysis that was executed natively on Google Cloud Platform, and give estimates on projected costs. We close with a summary and and outlook on future work.
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19

Słomska-Przech, Katarzyna, Aniela Rząsa, and Tomasz Panecki. "Atlas Fontium – examples of the Historical Atlas of Poland 2.0 data use." Abstracts of the ICA 5 (September 14, 2022): 1–2. http://dx.doi.org/10.5194/ica-abs-5-13-2022.

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20

Barisits, M., C. Serfon, V. Garonne, M. Lassnig, T. Beermann, and T. Javurek. "Automatic rebalancing of data in ATLAS distributed data management." Journal of Physics: Conference Series 898 (October 2017): 062006. http://dx.doi.org/10.1088/1742-6596/898/6/062006.

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21

Vukotic, I., R. W. Gardner, and L. A. Bryant. "Big Data Tools as Applied to ATLAS Event Data." Journal of Physics: Conference Series 898 (October 2017): 072003. http://dx.doi.org/10.1088/1742-6596/898/7/072003.

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22

Abdallah, J. M., U. Blumenschein, T. Davidek, A. Dotti, L. Fiorini, C. Maidantchik, C. S. Sanchez, and A. Solodkov. "ATLAS tile calorimeter data quality assessment with commissioning data." Journal of Physics: Conference Series 119, no. 3 (July 1, 2008): 032001. http://dx.doi.org/10.1088/1742-6596/119/3/032001.

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23

Denneau, Larry. "ATLAS: Big Data in a Small Package?" Proceedings of the International Astronomical Union 10, S318 (August 2015): 299–305. http://dx.doi.org/10.1017/s174392131500722x.

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AbstractFor even small astronomy projects, the petabyte scale is now upon us. The Asteroid Terrestrial-impact Last Alert System (Tonry 2011) will survey the entire visible sky from Hawaii multiple times per night to search for near-Earth asteroids on impact trajectories. While the ATLAS optical system is modest by modern astronomical standards — two 0.5 m F/2.0 telescopes — each night the ATLAS system will measure nearly 109 astronomical sources to a photometric accuracy of <5%, totaling 1012 individual observations over its initial 3-year mission. This ever-growing dataset must be searched in real-time for moving objects and transients then archived for further analysis, and alerts for newly discovered near-Earth asteroids (NEAs) disseminated within tens of minutes from detection. ATLAS's all-sky coverage ensures it will discover many ‘rifle shot’ near-misses moving rapidly on the sky as they shoot past the Earth, so the system will need software to automatically detect highly-trailed sources and discriminate them from the thousands of low-Earth orbit (LEO) and geosynchronous orbit (GEO) satellites ATLAS will see each night. Additional interrogation will identify interesting phenomena from millions of transient sources per night beyond the solar system. The data processing and storage requirements for ATLAS demand a ‘big data’ approach typical of commercial internet enterprises. We describe our experience in deploying a nimble, scalable and reliable data processing infrastructure, and suggest ATLAS as steppingstone to data processing capability needed as we enter the era of LSST.
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24

Huang, J. P., Y. Q. Xing, and L. Qin. "REVIEW OF NOISE FILTERING ALGORITHM FOR PHOTON DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 7, 2020): 105–10. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-105-2020.

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Abstract. As a continuation of Ice, Cloud, and Land Elevation Satellite-1 (ICESat-1)/Geoscience Laser Altimeter System (GLAS), the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) , which is equipped with the Advanced Topographic Laser Altimeter (ATLAS) system, was successfully launched in 2018. Since ICESat-1/GLAS has facilitated scientific results in the field of forest structure parameter estimation, how to use the ICESat-2/ATLAS photon cloud data to estimate forest structure parameters has become a hotspot in the field of spaceborne photon data application. However, due to the weak photon characteristics of the ICESat-2/ATLAS system, the system is extremely susceptible to noise, which poses a challenge for its subsequent accurate estimation of forest structural parameters. Aiming to filter out the noise photons, the paper introduces the advantages of the spaceborne lidar system ICESat-2/ATLAS than ICESat-1/GLAS. The paper summarizes the research of the simulated photon-counting lidar (PCL) noise filtering algorithm and noise filtering on spaceborne.
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25

Bouchami, J., F. Dallaire, A. Gutiérrez, J. Idarraga, V. Král, C. Leroy, S. Picard, et al. "Estimate of the neutron fields in ATLAS based on ATLAS-MPX detectors data." Journal of Instrumentation 6, no. 01 (January 11, 2011): C01042. http://dx.doi.org/10.1088/1748-0221/6/01/c01042.

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26

Vandelli, Wainer, and the Atlas Tdaq Collaboration. "ATLAS DataFlow Infrastructure: Recent results from ATLAS cosmic and first-beam data-taking." Journal of Physics: Conference Series 219, no. 2 (April 1, 2010): 022047. http://dx.doi.org/10.1088/1742-6596/219/2/022047.

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27

Lecoq, Marie-Elise, Anne-Sophie Archambeau, Fabien Cavière, Kourouma Koura, Sophie Pamerlon, and Jean Ganglo. "GBIF Benin's Data Portal." Biodiversity Information Science and Standards 2 (May 22, 2018): e25890. http://dx.doi.org/10.3897/biss.2.25890.

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GBIF Benin, hosted at the University of Abomey-Calavi, has published more than 338,000 occurrence records in 87 datasets and checklists. It has been a Global Biodiversity Information Facility (GBIF) node since 2004 and is a leader in several projects from the Biodiversity Information for Development (BID) programme. GBIF facilitates collaboration between nodes at different levels through its Capacity Enhancement Support Programme (CESP) [https://www.gbif.org/programme/82219/capacity-enhancement-support-programme]. One of the actions included in the CESP guidelines is called ‘Mentoring activities’. Its main goal is the transfer of knowledge between partners such as information, technologies, experience, and best practices. Sharing architecture and development is the key solution to solve some technical challenges or impediments (hosting, staff turnover, etc.) that GBIF nodes could face. The Atlas of Living Australia (ALA) team developed a functionality called ‘data hub’. It gives the possibility to create a standalone website with a dedicated occurrence search engine that seeks among a range of data (e.g. specific genus, geographic area). In 2017, GBIF Benin and GBIF France wanted to strengthen their partnership and started a CESP project. One of the core objectives of this project is the creation of the Atlas of Living Benin using ALA modules. GBIF France developers, with the help of the GBIF Benin team, are in the process of configuring a data hub that will give access to Beninese data only, while at the same time Atlas of Living France will give access to French data only. Both data portals will use the same back end, therefore the same databases. Benin is the first African GBIF node to implement this kind of infrastructure. On this poster, we will present the Atlas of Living Benin specific architecture and how we have managed to distinguish data coming from Benin and coming from France.
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28

Hartmann, Nikolai, Johannes Elmsheuser, and Günter Duckeck. "Columnar data analysis with ATLAS analysis formats." EPJ Web of Conferences 251 (2021): 03001. http://dx.doi.org/10.1051/epjconf/202125103001.

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Future analysis of ATLAS data will involve new small-sized analysis formats to cope with the increased storage needs. The smallest of these, named DAOD_PHYSLITE, has calibrations already applied to allow fast downstream analysis and avoid the need for further analysis-specific intermediate formats. This allows for application of the “columnar analysis” paradigm where operations are applied on a per-array instead of a per-event basis. We will present methods to read the data into memory, using Uproot, and also discuss I/O aspects of columnar data and alternatives to the ROOT data format. Furthermore, we will show a representation of the event data model using the Awkward Array package and present proof of concept for a simple analysis application.
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29

Gardner, Robert, Simone Campana, Guenter Duckeck, Johannes Elmsheuser, Andrew Hanushevsky, Friedrich G. Hönig, Jan Iven, Federica Legger, Ilija Vukotic, and Wei Yang. "Data federation strategies for ATLAS using XRootD." Journal of Physics: Conference Series 513, no. 4 (June 11, 2014): 042049. http://dx.doi.org/10.1088/1742-6596/513/4/042049.

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30

Lassnig, M., V. Garonne, G. A. Stewart, M. Barisits, T. Beermann, R. Vigne, C. Serfon, L. Goossens, A. Nairz, and A. Molfetas. "The ATLAS data management software engineering process." Journal of Physics: Conference Series 513, no. 5 (June 11, 2014): 052017. http://dx.doi.org/10.1088/1742-6596/513/5/052017.

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31

Robertson, M. P., G. S. Cumming, and B. F. N. Erasmus. "Getting the most out of atlas data." Diversity and Distributions 16, no. 3 (April 13, 2010): 363–75. http://dx.doi.org/10.1111/j.1472-4642.2010.00639.x.

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32

Niamir, Aidin, Andrew K. Skidmore, Albertus G. Toxopeus, Antonio R. Muñoz, and Raimundo Real. "Finessing atlas data for species distribution models." Diversity and Distributions 17, no. 6 (June 7, 2011): 1173–85. http://dx.doi.org/10.1111/j.1472-4642.2011.00793.x.

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33

Rzehorz, G. F., G. Kawamura, O. Keeble, and A. Quadt. "Data intensive ATLAS workflows in the Cloud." Journal of Physics: Conference Series 898 (October 2017): 062008. http://dx.doi.org/10.1088/1742-6596/898/6/062008.

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34

Sánchez, J., A. Fernández Casaní, and S. González de la Hoz. "Distributed Data Collection for the ATLAS EventIndex." Journal of Physics: Conference Series 664, no. 4 (December 23, 2015): 042046. http://dx.doi.org/10.1088/1742-6596/664/4/042046.

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35

Ricardo, R., B. Miguel, G. Benjamin, G. Vincent, L. Mario, S. Pedro, and C. David. "Monitoring the atlas distributed data management system." Journal of Physics: Conference Series 119, no. 7 (July 1, 2008): 072027. http://dx.doi.org/10.1088/1742-6596/119/7/072027.

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36

Yamamura, Taiki. "Early Higgs search with the ATLAS data." Journal of Physics: Conference Series 347 (February 14, 2012): 012010. http://dx.doi.org/10.1088/1742-6596/347/1/012010.

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37

Dell'Asta, Lidia. "Electroweak results with the ATLAS 2010 data." Journal of Physics: Conference Series 347 (February 14, 2012): 012023. http://dx.doi.org/10.1088/1742-6596/347/1/012023.

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38

Andrew Stewart, Graeme, Jamie Boyd, João Firmino da Costa, Joseph Tuggle, and Guillaume Unal. "Prompt data reconstruction at the ATLAS experiment." Journal of Physics: Conference Series 396, no. 2 (December 13, 2012): 022049. http://dx.doi.org/10.1088/1742-6596/396/2/022049.

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39

Maeno, T., K. De, and S. Panitkin. "PD2P: PanDA Dynamic Data Placement for ATLAS." Journal of Physics: Conference Series 396, no. 3 (December 13, 2012): 032070. http://dx.doi.org/10.1088/1742-6596/396/3/032070.

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40

Barisits, Martin, Vincent Garonne, Mario Lassnig, and Angelos Molfetas. "Simulating the ATLAS Distributed Data Management System." Journal of Physics: Conference Series 396, no. 5 (December 13, 2012): 052009. http://dx.doi.org/10.1088/1742-6596/396/5/052009.

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41

Soloviev, Igor, Giuseppe Avolio, Andrei Kazymov, and Matei Vasile. "ATLAS Operational Monitoring Data Archival and Visualization." EPJ Web of Conferences 245 (2020): 01020. http://dx.doi.org/10.1051/epjconf/202024501020.

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The Information Service (IS) is an integral part of the Trigger and Data Acquisition (TDAQ) system of the ATLAS experiment at the Large Hadron Collider (LHC) at CERN. The IS allows online publication of operational monitoring data, and it is used by all sub-systems and sub-detectors of the experiment to constantly monitor their hardware and software components including more than 25000 applications running on more than 3000 computers. The Persistent Back-End for the ATLAS Information System (PBEAST) service stores all raw operational monitoring data for the lifetime of the experiment and provides programming and graphical interfaces to access them including Grafana dashboards and notebooks based on the CERN SWAN platform. During the ATLAS data taking sessions (for the full LHC Run 2 period) PBEAST acquired data at an average information update rate of 200 kHz and stored 20 TB of highly compacted and compressed data per year. This paper reports how over six years PBEAST became an essential piece of the experiment operations including details of the challenging requirements, the failures and successes of the various attempted implementations, the new types of monitoring data and the results of the time-series database technology evaluations for the improvements towards LHC Run 3.
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42

Czyczula, Z. "Tau Physics with First Data in ATLAS." Nuclear Physics B - Proceedings Supplements 189 (April 2009): 344–49. http://dx.doi.org/10.1016/j.nuclphysbps.2009.03.056.

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43

Sandell, M., and J. P. Leahy. "Morphological data from the Atlas of DRAGNs." New Astronomy Reviews 46, no. 2-7 (May 2002): 361–64. http://dx.doi.org/10.1016/s1387-6473(01)00209-3.

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44

Lassnig, Mario, Vincent Garonne, Miguel Branco, and Angelos Molfetas. "Dynamic and adaptive data-management in ATLAS." Journal of Physics: Conference Series 219, no. 6 (April 1, 2010): 062054. http://dx.doi.org/10.1088/1742-6596/219/6/062054.

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45

Frost, James. "Commissioning of ATLAS Data Quality Infrastructure with First Collision Data." Journal of Physics: Conference Series 331, no. 3 (December 23, 2011): 032009. http://dx.doi.org/10.1088/1742-6596/331/3/032009.

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46

Rinaldi, Lorenzo, Elizabeth J. Gallas, and Andrea Formica. "Optimizing access to conditions data in ATLAS event data processing." EPJ Web of Conferences 214 (2019): 04051. http://dx.doi.org/10.1051/epjconf/201921404051.

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The processing of ATLAS event data requires access to conditions data which are stored in database systems. This data includes, for example alignment, calibration, and configuration information which may be characterized by large volumes, diverse content, and/or information which evolves over time as refinements are made in those conditions. Additional layers of complexity are added by the need to provide this information across the worldwide ATLAS computing grid and the sheer number of simultaneously executing processes on the grid, each demanding a unique set of conditions to proceed. Distributing this data to all the processes that require it in an efficient manner has proven to be an increasing challenge with the growing needs and numbers of event-wise tasks. In this presentation, we briefly describe the systems in which we have collected information about the database content and the use of conditions in event data processing. We then proceed to explain how this information has been used not only to refine reconstruction software and job configuration but also to guide modifications of underlying conditions data configuration and in some cases, rewrites of the data in the database into a more harmonious form for offline usage in the processing of both real and simulated data..
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47

Vamosi, Ralf, Mario Lassnig, and Erich Schikuta. "Data Allocation Service ADAS for the Data Rebalancing of ATLAS." EPJ Web of Conferences 214 (2019): 06012. http://dx.doi.org/10.1051/epjconf/201921406012.

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The distributed data management system Rucio manages all data of the ATLAS collaboration across the grid. Automation, such as data replication and data rebalancing are important to ensure proper operation and execution of the scientific workflow. In this proceedings, a new data allocation grid service based on machine learning is proposed. This learning agent takes subsets of the global datasets and proposes a better allocation based on the imposed cost metric, such as waiting time in the workflow. As a service, it can be modularized and can run independently of the existing rebalancing and replication mechanisms. Furthermore, it collects data from other services and learns better allocation while running in the background. Apart from the user selecting datasets, other data services may consult this meta-heuristic service for improved data placement. Network and storage utilization is also taken into account.
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48

Titov, M., G. Záruba, A. Klimentov, and K. De. "A Probabilistic Analysis of Data Popularity in ATLAS Data Caching." Journal of Physics: Conference Series 396, no. 3 (December 13, 2012): 032106. http://dx.doi.org/10.1088/1742-6596/396/3/032106.

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49

Maier, Thomas, Thomas Beermann, Günter Duckeck, Mario Lassnig, Federica Legger, Matteo Magoni, and Ilija Vukotic. "Performance and impact of dynamic data placement in ATLAS." EPJ Web of Conferences 214 (2019): 04025. http://dx.doi.org/10.1051/epjconf/201921404025.

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For high-throughput computing the efficient use of distributed computing resources relies on an evenly distributed workload, which in turn requires wide availability of input data that is used in physics analysis. In ATLAS, the dynamic data placement agent C3PO was implemented in the ATLAS distributed data management system Rucio which identifies popular data and creates additional, transient replicas to make data more widely and more reliably available. This proceedings presents studies on the performance of C3PO and the impact it has on throughput rates of distributed computing in ATLAS. Furthermore, results of a study on popularity prediction using machine learning techniques are presented.
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50

Abreu, H., E. Amin Mansour, C. Antel, A. Ariga, T. Ariga, F. Bernlochner, T. Boeckh, et al. "The trigger and data acquisition system of the FASER experiment." Journal of Instrumentation 16, no. 12 (December 1, 2021): P12028. http://dx.doi.org/10.1088/1748-0221/16/12/p12028.

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Abstract The FASER experiment is a new small and inexpensive experiment that is placed 480 meters downstream of the ATLAS experiment at the CERN LHC. FASER is designed to capture decays of new long-lived particles, produced outside of the ATLAS detector acceptance. These rare particles can decay in the FASER detector together with about 500–1000 Hz of other particles originating from the ATLAS interaction point. A very high efficiency trigger and data acquisition system is required to ensure that the physics events of interest will be recorded. This paper describes the trigger and data acquisition system of the FASER experiment and presents performance results of the system acquired during initial commissioning.
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