Exploratory repository which attempts to use coffea as an alternative means of processing and analyzing Cathode Strip Chamber (CSC) data.

The repository contains:

• Signal-to-background analysis Cathode Local Charged Tracks (CLCTs) using ROC curves
• Algorithms used to estimate muon momentum via the energy they lose along their flight path

Setup

On an LXPLUS node, first clone the git repository with

git clone https://github.com/williamnash/CSCCoffea.git
cd CSCCoffea

Then setup the virtual environment

source setup.sh

After the initial installation, one must reactivate the virtual environment again before running the code

Run

Analysis is broken up into processors, which hold the logic which operates on each event, and executors, which distributes the computation and ultimately collects it. Once the setup is complete (and with an active virtual environment) one can run a script in the src/ folder via

python template_executor.py

this script will produce an output histogram (png) holding information related to a sample dataset.

Coffea usage

The primary idea is that for loops in python are slow (due to type-checking overhead on each iteration), so using awkward array (based on numpy) affords loops that can iterate ~ 100x faster than they would otherwise.

This is done using combinations of masking, slicing, and broadcasting, which affords a diverse selection of operations that can all be done without a single for loop. This also has the added benefit of both not having to keep track of indices when iterating through arrays, as well as code readability.

For instance a mask can be created with

import numpy as np
a = np.array([1,2,3,4,5,6])
mask = a < 3
print(mask)

Which gives

array([ True,  True, False, False, False, False])

These masks can also be used to select for elements in the array (particularly useful for us)

print(a[mask])

Which gives

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