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Multi-Input FRBID - MeerKAT Fast Radio Burst Intelligent Distinguisher using Deep Learning

Identification of Fast Radio Burst/Single Pulses (FRB/SP) and Radio Frequency Interference (RFI) using Deep Convolutional Neural Network for MeerKAT facility. The code uses two inputs: the DM-Time and Frequency-Time images/arrays. Each image acts as an input to a CNN. At the end both DM-T CNN and Freq-T CNN are fused and pass through dense layers as shown in the figure below.

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Installation

Follow the instructions in Installation.txt to install all dependencies.

Training and Prediction

To train the model from scratch, either use FRBID - DEMO - MULTIINPUT.ipynb or train.py. Note that there are several parameters that need to be changed if one want different configuration, else run the code as follows:

    python train.py
    or run all cells in FRBID - DEMO - MULTIINPUT.ipynb

To make prediction on new candidates that do not have a label, use either FRBID - prediction-phase.ipynb or predict.py. Note that a directory containing all h5 candidate files should be available and some parameters need to be specified, for e.g the model_name, the directory to save the csv file containing the prediction, the directory of the h5 files and the threshold probability.

Run prediction on new candidate files as follows:

    python predict.py -d ./data/test_set/ -r ./data/results_csv/ -p 0.5              

or run prediction on default settings as follows:

    python predict.py
    or run all cells in FRBID - prediction-phase.ipynb