README.md

FETCH

fetch is Fast Extragalactic Transient Candidate Hunter. It has been detailed in the paper Towards deeper neural networks for Fast Radio Burst detection.

This is the tensorflow>=2 version of the fetch, if you are looking for the older tensorflow version click here.

Install

git clone https://github.com/devanshkv/fetch.git
cd fetch
pip install -r requirements.txt
python setup.py install

The installation will put predict.py and train.py in your PYTHONPATH.

Usage

To use fetch, you would first have to create candidates. Use your for this purpose, this notebook explains the whole process. Your also comes with a command line script your_candmaker.py which allows you to use CPU or single/multiple GPUs.

To predict a candidate h5 files living in the directory /data/candidates/ use predict.py for model a as follows:

predict.py --data_dir /data/candidates/ --model a

To fine-tune the model a, with a bunch of candidates, put them in a pandas readable csv, candidate.csv with headers 'h5' and 'label'. Use

train.py --data_csv candidates.csv --model a --output_path ./

This would train the model a and save the training log, and model weights in the output path.

Example

Test filterbank data can be downloaded from here. The folder contains three filterbanks: 28.fil 29.fil 34.fil. Heimdall results for each of the files are as follows:

for 28.fil

16.8128	1602	2.02888	1	127	475.284	22	1601	1604

for 29.fil

18.6647	1602	2.02888	1	127	475.284	16	1601	1604

for 34.fil

13.9271	1602	2.02888	1	127	475.284	12	1602	1604 

The cand.csv would look like the following:

file,snr,stime,width,dm,label,chan_mask_path,num_files
28.fil,16.8128,2.02888,1,475.284,1,,1
29.fil,18.6647,2.02888,1,475.284,1,,1
34.fil,13.9271,2.02888,1,475.284,1,,1

Running your_candmaker.py will create three files:

cand_tstart_58682.620316710374_tcand_2.0288800_dm_475.28400_snr_13.92710.h5
cand_tstart_58682.620316710374_tcand_2.0288800_dm_475.28400_snr_16.81280.h5
cand_tstart_58682.620316710374_tcand_2.0288800_dm_475.28400_snr_18.66470.h5

Running predict.py with model a will give results_a.csv:

,candidate,probability,label
0,cand_tstart_58682.620316710374_tcand_2.0288800_dm_475.28400_snr_18.66470.h5,1.0,1.0
1,cand_tstart_58682.620316710374_tcand_2.0288800_dm_475.28400_snr_16.81280.h5,1.0,1.0
2,cand_tstart_58682.620316710374_tcand_2.0288800_dm_475.28400_snr_13.92710.h5,1.0,1.0

Training Data

The training data is available at astro.phys.wvu.edu/fetch.

Citating this work

---

If you use this work please cite:

@article{Agarwal2020,
  doi = {10.1093/mnras/staa1856},
  url = {https://doi.org/10.1093/mnras/staa1856},
  year = {2020},
  month = jun,
  publisher = {Oxford University Press ({OUP})},
  author = {Devansh Agarwal and Kshitij Aggarwal and Sarah Burke-Spolaor and Duncan R Lorimer and Nathaniel Garver-Daniels},
  title = {{FETCH}: A deep-learning based classifier for fast transient classification},
  journal = {Monthly Notices of the Royal Astronomical Society}
}
@software{agarwal_aggarwal_2020,
  author       = {Devansh Agarwal and
                  Kshitij Aggarwal},
  title        = {{devanshkv/fetch: Software release with the 
                   manuscript}},
  month        = jun,
  year         = 2020,
  publisher    = {Zenodo},
  version      = {0.1.8},
  doi          = {10.5281/zenodo.3905437},
  url          = {https://doi.org/10.5281/zenodo.3905437}
}