This repository provides a simplified particle physics analysis example for the REANA reusable research data analysis plaftorm. The objective is to extract the top-quark mass by measuring the peak position of the energy of b-tagged jets in the laboratory frame as portrayed in the CMS Data Analysis School exercises. More information can be accessed in the CMSDAS example repository here.
Making a research data analysis reproducible basically means to provide "runnable recipes" addressing (1) where is the input data, (2) what software was used to analyse the data, (3) which computing environments were used to run the software and (4) which computational workflow steps were taken to run the analysis. This will permit to instantiate the analysis on the computational cloud and run the analysis to obtain (5) output results.
The analysis takes the following inputs:
-
the list of sample CMS runs from 2015 and 2016 included in the
inputsdirectory:samples_Run2015_25ns.jsonsamples_Run2015_25ns_m169p5.jsonsamples_Run2015_25ns_m175p5.jsonsamples_Run2016_25ns.jsonsamples_Run2016_25ns_m169p5.jsonsamples_Run2016_25ns_m175p5.jsonsamples_Run2016_m169p5.jsonsamples_Run2016_m175p5.json
The analysis will consist of three stages. In the first stage, we shall process
the original and simulated collision data (using analyzeNplot.py) to select
top-pair events that decay in the eμ channel, compare the selection (control distributions
and event yields), and propagate the sources of systematic uncertainties to the
b jet energy peak;. In the second stage, we shall fit the b jet energy peak and
calibrate the peak measured for the set of selection criteria previously defined
to the expected b jet energy peak, from which the top-quark mass can be easily extracted.
In the third stage, we shall compare the results with the standard top-quark mass
measurements performed with 8 TeV data.
In order to be able to rerun the analysis even several years in the future, we need to "encapsulate the current compute environment", for example to freeze the software package versions our analysis is using. We shall achieve this by preparing a Docker container image for our analysis steps.
This analysis example runs within the CMSSW analysis framework that was packaged for Docker in clelange/cmssw.
The analysis workflow is simple and consists of the above-mentioned stages:
We shall use the CWL workflow specification to express the computational workflow:
and its individual steps: