Title: PHYSLITE - A new reduced common data format for ATLAS

Journal Article · · EPJ Web of Conferences (Online)
 [1];  [2];  [3];  [4];  [5];  [6];  [7]
  1. Univ. of Washington, Seattle, WA (United States)
  2. Univ. of Oslo (Norway)
  3. Brookhaven National Laboratory (BNL), Upton, NY (United States)
  4. Technical Univ. of Munich (Germany)
  5. Iowa State Univ., Ames, IA (United States)
  6. Argonne National Laboratory (ANL), Argonne, IL (United States)
  7. Univ. of Texas, Arlington, TX (United States)

The High Luminosity LHC (HL-LHC) era brings unprecedented computing challenges that call for novel approaches to reduce the amount of real and Monte Carlo-simulated data that is stored, while continuing to support the rich physics program of the ATLAS experiment. With the beginning of LHC Run 3, ATLAS introduced a new common data format, PHYS, that replaces most of the analysis-specific formats that were used in Run 2, and therefore reduces the disk storage significantly. ATLAS also launched the prototype of another common format, PHYSLITE, that is about a third of the size of PHYS. PHYSLITE will be the main format for ATLAS at the HL-LHC and aims to serve 80% of all physics analyses. To simplify analysis workloads and further reduce disk usage it is designed to largely replace user-defined analysis n-tuples and consequently contains pre-calibrated objects. Various forms of validations are in place to ensure correct functionality for users. Developments continue towards HL-LHC to improve the PHYSLITE format further.

Research Organization:
Brookhaven National Laboratory (BNL), Upton, NY (United States)
Sponsoring Organization:
USDOE Office of Science (SC), High Energy Physics (HEP)
Grant/Contract Number:
SC0012704
OSTI ID:
2429543
Report Number(s):
BNL-225907-2024-JAAM
Journal Information:
EPJ Web of Conferences (Online), Journal Name: EPJ Web of Conferences (Online) Vol. 295; ISSN 2100-014X
Publisher:
EDP SciencesCopyright Statement
Country of Publication:
United States
Language:
English

References (5)

ATLAS Data Carousel journal January 2020
Framework for custom event sample augmentations for ATLAS analysis data journal January 2024
Optimizing ATLAS data storage: The impact of compression algorithms on ATLAS physics analysis data formats journal January 2024
Integration of RNTuple in ATLAS Athena journal January 2024
A new petabyte-scale data derivation framework for ATLAS journal December 2015

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