DeepPulsarNet

This Python 3 program allows searching for pulsars in filterbank files using neural networks.

Installation

• Install a for your system fitting version of PyTorch (Tested on 1.6.0)
• Run pip install -r requirements.txt

Basic Workflow

• Grab pulsar survey data. One example set of the Parkes Multibeam Survey which already has been sufficiently downsampled can be found here*
• Create a training and noise set using create_training_set.ipynb and prepare_noise_set.ipynb included in ./deeppulsarnet/notebooks
• Train a neural network using train_pulsar_net.py
  • Example command: python train_pulsar_net.py --path simset_training_set_1_noise.csv --path_noise noiseset_noise_sample.csv --name test_model --length 100000
• Make a prediction for a set of observations using make_prediction_for_set.ipynb

The parameters of the network can currently be changed by modifying the .json config files which are given with the --class_configs and -- model_config options wich use configs included in the ./deeppulsarnet/model_configs folder. Single parameters can be changed with the --model_parameter option.

*Original data: Lyne, A; Manchester, R; Camilo, F; Bell, J; Sheppard, D; D'Amico, N; Kaspi, V (2012): Parkes observations for project P268 semester 1997AUGT. v3. CSIRO. Data Collection. https://doi.org/10.4225/08/583746ac2c4de

Tutorial

• When working with the dockerfile the data loader will most likely run into memory issues which can be fixed by adding --shm-size 8G to your docker run command.
• cd tutorial
• python 0_create_pmps_dataset.py
• python 1_create_simulations.py
• python 2_create_targets.py
• bash 3_train_network.sh
• The parameters for the training or the parameters of the simulation set can be changed to increase performance.
• bash 4_test_network.sh
• If the Pulsar Prediction value is above 0.5 the network thinks that there is a real pulsar in the data. Half of the test samples contains known pulsars.