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Related Concept Videos

High-Resolution Mass Spectrometry (HRMS)01:15

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The resolution of a mass spectrometer depends on the efficiency of separating ions with different ion masses. The mass of an atom is approximated to the sum of the masses of protons and neutrons inside, considering the masses of protons and neutrons as equal. However, the masses of the proton (1.6726 × 10−24 g) and neutron (1.6749 × 10−24 g) are not truly equal. There is a minor error in the expression of atomic masses relative to the simplest atom of hydrogen. For...
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Updated: May 3, 2026

Quantification of Hydrogen Concentrations in Surface and Interface Layers and Bulk Materials through Depth Profiling with Nuclear Reaction Analysis
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The LHCb Stripping Project: Sustainable Legacy Data Processing for High-Energy Physics.

Nathan Allen Grieser1, Eduardo Rodrigues2, Niladri Sahoo3

  • 1University of Cincinnati, Cincinnati, USA.

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This summary is machine-generated.

The LHCb Stripping project refines collision data for analysis, optimizing processing for legacy and live datasets. This ensures continued access to valuable physics data for the LHCb collaboration.

Keywords:
Data processing and offline analysisHigh-energy physicsLHCb experiment

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Area of Science:

  • High Energy Physics
  • Particle Physics Data Analysis

Background:

  • The LHCb experiment generates vast amounts of collision data requiring sophisticated processing.
  • Efficient data refinement is crucial for offline analysis and scientific discovery.

Purpose of the Study:

  • To provide a comprehensive overview of the LHCb Stripping project's framework and operations.
  • To detail advancements in data processing infrastructure and campaign management.
  • To discuss challenges and future directions for legacy and live data handling.

Main Methods:

  • Utilizing a Python-configurable architecture for the Stripping framework.
  • Integrating the framework with LHCb computing systems for large-scale campaign management.
  • Implementing GitLab-based workflows, continuous integration, and automation for efficient processing.

Main Results:

  • Successful re-analysis of Runs 1 and 2 legacy data.
  • Optimization of infrastructure for both legacy and live data processing.
  • Advancements in organizational and computational processing techniques.

Conclusions:

  • The Stripping project is vital for managing LHCb data, ensuring access to valuable physics datasets.
  • Continuous optimization and adoption of modern workflows are key to efficient data processing.
  • Lessons learned will guide future roadmaps for sustained data access and analysis.