An FRB detection code written for my undergraduate research project. This Python code is written based on the Burst Emission Automatic Roger (BEAR) written in C++
(Men et al., 2019). An explanation of the data analysis process is available in the same article as well as in my thesis.
- This repository is only intended as an archive, no further development will be done. This code was finalized in June 2021.
- PoLaR BEAR was written for me to learn and understand FRB detection techniques. Therefore, it is mostly inefficient.
- PoLaR BEAR was written as a functional code rather than object oriented.
- Inputs and analysis parameters are not taken using arguments, the code has to be modified to adjust them.
- New filterbank header items (which are added after June 2021) which are not available in the header dictionary will cause the code to fail. Add the new attributes to get it to work again.
- Fake FRB filterbank data can be generated with using FRBFakeRandom.py.
The data used here is the FRB010124 (source: Parkes Archival FRB Data).
Source: Unknown
Time (MJD): 51934.019976851850515
Downsampling coefficient: 20
Start: 0.00000000
End: 0.20000000
Threshold: 41.99597534
Zapped frequencies:
1435.00 - 1440.00 MHz
1495.00 - 1505.00 MHz
Candidate DM (cm-3 pc) Width (s) Time (s) S SNR
----------- -------------- ----------- ---------- ------- -------
1 790 0.0075 28.8025 507.762 22.5336
2 1 0.0025 22.535 95.7433 9.78485
3 671 0.1225 28.855 71.634 8.46369
4 981 0.1225 28.735 46.0027 6.78253
- Men, Y. P., Luo, R., Chen, M. Z., Hao, L. F., Lee, K. J., Li, J., Li, Z. X., Liu, Z. Y., Pei, X., Wen, Z. G., Wu, J. J., Xu, Y. H., Xu, R. X., Yuan, J. P., & Zhang, C. F. (2019). Piggyback search for fast radio bursts using Nanshan 26 m and Kunming 40 m radio telescopes – I. Observing and data analysis systems, discovery of a mysterious peryton. In Monthly Notices of the Royal Astronomical Society (Vol. 488, Issue 3, pp. 3957–3971). Oxford University Press (OUP). https://doi.org/10.1093/mnras/stz1931