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-------------- TriForce Particle ID System --------------
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Triforce performs particle ID and energy regression, and produces GAN images
using calorimeter data.
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------------------------ Contact ------------------------
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Matt Zhang - set.stun@gmail.com
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---------------------- How To Use -----------------------
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Create an options file in the Options/ folder. default_options.py is a
template. In the file, set training options and initialize the tools you want
to use.
On L25 of triforce.py, choose which options file you want to load.
** PLEASE DO NOT PUSH YOUR LOCAL OPTIONS FILES TO GIT. **
Then, simply do "python3 triforce.py <args>" to run your script, passing
whatever arguments are specified in your options file. Run with Python 3.
E.g. with the default settings you can do "python3 triforce.py Output_001 0.01
0 0.5 256 4".
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--------------- How To Create a New Tool ----------------
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There are four tools in this package:
Classification - for particle classification (electron/gamma/pion)
Regression - for determination of particle energy
GAN - for generating new particle shower images
Analysis - for data analysis and plotting
If you want to alter a tool (e.g. use a different neural net architecture for
regression), open the tool folder and create a new file with the
configuration you want. Then initialize it in your options file. You can use
the default tools as templates.
Critically, you need to implement a net with an __init__() and forward()
function, and you need to set a loss function and an optimizer.
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------------------- Bugs and Features -------------------
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If you find a bug, or want a feature added, please email Matt and add your
request to the TODO file.
mialiu149/Triforce_CaloML
Machine learning tool for identification of physics objects in particle detector calorimeters
Python