This repository contains code and other materials related to applying Machine Learning techniques to signal feature extraction, for the sPHENIX calorimeters.
At present, we are using the following platforms:
- Keras (which runs on top of TensorFlow) and work in the Python environment
- ONNX: https://onnx.ai/
In the latter case, a Keras model is converted into the ONNX format and used
with a variety of runtimes libraries, such as both C++ and Python versions of
the ONNX runtime, which is characterized by high performance. Work is under
way to develop this into a lightweight, standalone library which is easy
to integrate into sPHENIX and other software.
In addition, Keras inference was tested in service-type deployments based on nginx and gunicorn.
Training for signal feature extraction was performed with two different methods
- Approximated version of the Landau function
- "Template fit" which is using a parametrized average shape of the pulse
The first stage of the study used simulated signals with the shape approximated by the Landau function. Then, the test beam data taken with the EMCAL prototype in 2018 was used as input for the ML process.
Most of ML code is contained in the folder testbeam (for data analysis and model training),
and its subfolder onnx. Dependencies for the ONNX-based code are contained therein, so
it can be built locally.