Commit 584ff2f2 authored by JGlombitza's avatar JGlombitza
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### A Deep Learning-based Reconstruction of Cosmic Ray-induced Air Showers
##### _Martin Erdmann, Jonas Glombitza, David Walz_
 
| | Website |
......@@ -7,9 +8,10 @@
| ArXiv | https://arxiv.org/pdf/1708.00647.pdf |
| Elsevier | ........ |
Requirements: Keras, Tensorflow, seaborn, numpy and matplotlib
For training a Deep Neural Network (DNN) on air shower reconstruction, there are 3 steps to do:
Requirements: Keras, Tensorflow, seaborn, numpy and matplotlib
- ##### Simulate data
Use the script `./sim.py` to simulate showers. (simulates ~100.000 proton showers in 10 packages = 10.000 airshowers per package)
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Start some script in ```./training/```
After training, the script evaluates the trained model and plots the result. (Default path: `./training/` change path with: log_dir)
Some usefull parameters:
```
Some usefull parameters
max_steps=15 - iteration steps (runtime)
lr = 0.001 - learning rate
log_dir="." - path to plots
nbatch = 132 - size of each mini batch
```
There are 5 DNN's for the 3 reconstruction tasks:
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