ReadMe

README.md

-# airshower
-authors: Jonas Glombitza, David Walz
-
-git:   https://git.rwth-aachen.de/DavidWalz/airshower.git
-arXiv: https://arxiv.org/pdf/1708.00647.pdf  (A Deep Learning-based Reconstruction of Cosmic Ray-induced Air Showers)
+### A Deep Learning-based Reconstruction of Cosmic Ray-induced Air Showers
+##### _Martin Erdmann, Jonas Glombitza, David Walz_
+ 
+|  | Website |
+| ------ | ------ |
+| ArXiv | https://arxiv.org/pdf/1708.00647.pdf |
+| Elsevier | ........ |
+| Github | https://git.rwth-aachen.de/DavidWalz/airshower.git |
 
 For training a Deep Neural Network (DNN) on air shower reconstruction, there are 3 steps to do:
 
-Needed: Keras, Tensorflow, seaborn, numpy and matplotlib
+Requirements: Keras, Tensorflow, seaborn, numpy and matplotlib
 
-1. Simulate data
-    Use the script ./sim.py to simulate showers. (simulates ~100.000 proton showers in 10 packages = 10.000 airshowers per package)
+  - ##### Simulate data
+    Use the script `./sim.py` to simulate showers. (simulates ~100.000 proton showers in 10 packages = 10.000 airshowers per package)
     100.000 showers - 60.000 for training - 40.000 for evaluation
-    The data will be saved in ./RawData/showers*
-    
-2. Preprocess the simulated data
-    Use the script PreProcessing.py
-    The script loads all showers in the RAM and preprocess the data (around 12 Gbyte RAM needed!)
-    The preprocessed data will be stored in ./Data_preprocessed/
-
-3. Train DNN with preprocessed data
-    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)
+    The data will be saved in `./RawData/showers*`
     
+  - ##### Preprocess the simulated data
+    Use the script ```PreProcessing.py```
+    The script loads all showers in the RAM and preprocess the data.
+    The preprocessed data will be stored in `./Data_preprocessed/`
+
+  - ##### Train DNN with preprocessed data
+    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:
     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:
+
+##### Angular Reconstruction
+  - Only time input
+  - All inputs
 
-    There are 5 DNN's for the 3 reconstruction tasks:
-    Angular Reconstruction
-        - Only time input
-        - All inputs
-    Energy Reconstruction
-        - Only total signal input
-        - All inputs
-    Xmax Reconstruction
-        - All inputs
+##### Energy Reconstruction
+  - Only total signal input
+  - All inputs
+##### Xmax Reconstruction
+  - All inputs
 
-    For more network architecture details see paper:    https://arxiv.org/pdf/1708.00647.pdf
+For more details on the network architecture see:    https://arxiv.org/pdf/1708.00647.pdf