T9BeamTestAna

Analysis package for the T9 beam test

To compile the code type make in the main directory.

Processing data

The first analysis stage includes processing data files. The output is a root file with anaTree TTRee. It includes, trigger timestamps, pedestals, pedestals standard deviations, signal amplitudes and times.

USAGE: ./bin/waveform_analysis.app -i input_list_file -o output_root_file -c analysis_config.json

-i input_list_file --> ASCII file with run file names (one file per line). It allows users to run with multiple runs. If you put # before the file name, it will be "commented out" (not used by the code)

-o output_root_file --> output root file

-c analysis_config.json --> json configuration file (an example is located in config/config.json

Making plots

Navigate to scripts/ directory.

root -q -b 'MakeDataPlots.C("output_file_from_step1", momentum)'

The script will use int momentum to recognize which cuts it needs to use. The script output is a root file with the same name as the input and "_plots" appended to the end. It contains:

• reference histograms (they have "ref" in the name and don't change when you apply cuts)
• TOF histograms that are used to estimate the fraction of particles.

Fitting: the TOF distribution:

Done by scripts/FitTOF.C, a compilable ROOT macro

• By default, a 3-component fit of 3 Gaussians is fitted to extract the number of e, mu and pi events;
• In the macro, booleans isThreeComponentFit and isProtonFit are set automatically based on the momentum.

Example running: ** root -l "scripts/FitTOF.C("output_260n_plots.root",-260)"

Additional scripting over all momenta

The list of runs for every momentum is stored in python/data_runs.py

• edit as needed, must enter also the total number of spills for the runs. The pipe separator between runs is needed as this is used to grep the proper runs. Again, 'p' and 'n' stand for positive and negative charged particles in given momentum setup.

The runs are split to low (200--280 MeV), high (300--360 MeV) and p (>= 400 MeV) runs.

Create a link name 'data' to a folded holding the TB ROOT files in name format as root_run_000153_0000_clean.root

• ln -s YOURPATHTODATA data

Running the waveform analysis, plotting and fitting for all momenta: done by ./python/run_momenta.py

One has to specify whether really run (0/1), or just print the commands to choose from, or optionally not run but just fit by '0f'. Momentum range low/high/p must be specified, so e.g.:

• python ./python/run_momenta.py 1 low # run everytging: waveforms analyis, make histos, fit the TOF; for low momenta
• python ./python/run_momenta.py 1 high # same as above; for high momenta
• python ./python/run_momenta.py 0f low # do not run the waveform analysis, just fit, higher momenta
• python ./python/run_momenta.py 0m low # do not run the waveform analysis, but run making the histograms and cuts
• python ./python/run_momenta.py 0fm low # do not run the waveform analysis, but run making the histograms and cuts; and fit
• python ./python/run_momenta.py 0 p # just print commands for the proton runs

Output are png's, pdf's; and mainly ascii files with fitted numbers of e, mu, pi; or p; or mu+pi over 300 MeV.

• E.g. ascii_output_220p_plots.txt

Plotting the mu and pi yields scaled to per day rates

The yields scalling is based on interval between spills of 40s; as function of the momentum.

This is done by python/plotFromAscii.py for which one needs to choose the negative or positive beam p/n and the momenta range (low, high, or 'p' for protons).

• python ./python/plotFromAscii.py p low
• python ./python/plotFromAscii.py n low
• python ./python/plotFromAscii.py p high
• python ./python/plotFromAscii.py n high
• python ./python/plotFromAscii.py p p # for protons

One can make a html page for viewing the fit results:

• ./scripts/make_html.sh