MDC2024: Difference between revisions

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==Aims of MDC2024==
==Aims of MDC2024==
There are a few aims of MDC2024, these are separated into infrastrucutre and analysis-preparation:
For the infrastructure changes:
* Move toward a fcl-less schema;
* Implement move away from SAM;
For analysis-preparations:
* Build and test analysis strategies;
* Build and test data-driven methods for estimating background and systematic uncertainties;
* Evolve TrkAna, and make it an integral part of Production as we move towards more realistic simulations.

Revision as of 15:42, 5 February 2024

Introduction

Previous MDC (Mock Data Challenges) have had limited purposes. MDC2020 for example was an end-to-end production update that implemented an updated geometry, detector simulations, persistent schema, and simulation workflows compared to its predecessor, MDC2018. The goal is to provide a reasonably a complete and accurate model of what Mu2e will record during commissioning and the first running period ('Run 1', add reference), including OnSpill, OffSpill, and Extracted Position samples. The primary intended use cases of these samples are:

  • Detector calibration and alignment, including cross-system (ie CRV to Calo) calibrations
  • Detector commissioning
  • Trigger algorithm testing and development
  • TDAQ to Offline data transfer workflows
  • Offline reconstruction algorithm development
  • Science extraction framework and algorithm development

It was also the first time Mu2e had utilized POMs and allowed us to develop large amounts of infrastructure to improve job efficiency.

MDC2024 builds on MDC2020 infrastructure but is focused now on analysis preparation and will aid the development of our analysis tools and frameworks.

Aims of MDC2024

There are a few aims of MDC2024, these are separated into infrastrucutre and analysis-preparation:

For the infrastructure changes:

  • Move toward a fcl-less schema;
  • Implement move away from SAM;


For analysis-preparations:

  • Build and test analysis strategies;
  • Build and test data-driven methods for estimating background and systematic uncertainties;
  • Evolve TrkAna, and make it an integral part of Production as we move towards more realistic simulations.