We lifted 2 functions out of this paper's own repositories and ran 0 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| svalleco/3Dgan | canonical | 0 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| bit_flip | Not yet run | svalleco/3Dgan/caffe/caffe_train.py code served (permissive licence) · get_code("2470738ec7b29274") |
| ecal_sum | Not yet run | svalleco/3Dgan/keras/EcalEnergyGan.py code served (permissive licence) · get_code("5694195b2ed75e18") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We present the 3DGAN for the simulation of a future high granularity calorimeter output as three-dimensional images. We prove the efficacy of Generative Adversarial Networks (GANs) for generating scientific data while retaining a high level of accuracy for diverse metrics across a large range of input variables. We demonstrate a successful application of the transfer learning concept: we train the network to simulate showers for electrons from a reduced range of primary energies, we then train further for a five times larger range (the model could not train for the larger range directly). The same concept is extended to generate showers for other particles (photons and neutral pions) depositing most of their energies in electromagnetic interactions. In addition, the generation of charged pion showers is also explored, a more accurate effort would require additional data from other detectors not included in the scope of the current work. Our further contribution is a demonstration of using GAN-generated data for a practical application. We train a third-party network using GAN-generated data and prove that the response is similar to a network trained with data from the Monte Carlo simulation. The showers generated by GAN present accuracy within $10\%$ of Monte Carlo for a diverse range of physics features, with three orders of magnitude speedup. The speedup for both the training and inference can be further enhanced by distributed training.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2109.07388")
get_code_for_paper("2109.07388")
have("2109.07388")
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