Reproduction package for the paper: "Detection of ultra-fast radio bursts from FRB 20121102A"

# Reproduction package for the paper "Detection of ultra-fast radio bursts from FRB 20121102A"
Authors: Mark P. Snelders, K. Nimmo, J.W.T. Hessels, Z. Bensellam, L.P. Zwaan, P. Chawla, O.S. Ould-Boukattine, F. Kirsten, J.T. Faber and V. Gajjar.
arXiv link: https://arxiv.org/abs/2307.02303
DOI published article: Nature Astronomy, 19 October 2023, https://doi.org/10.1038/s41550-023-02101-x

This work has been made possible by an NWO Vici grant (Principal investigator, J.W.T.H.). 

## Raw Data

- The data are 100% publicly available and are explained in great detail in the following post: http://seti.berkeley.edu:8000/frb-data/
- The data are available from the Breakthrough Initiatives Open Data Portal with target name FRB121102: https://breakthroughinitiatives.org/opendatasearch

In this paper we have re-processed and re-analysed data from the Green Bank Telescope that made use of the Breakthrough Listen digital backend. I will call this the GBT BL data. Below you can find links to multiple papers, GitHub repositories and blogposts that explain the GBT BL data.

- My paper describing the search and analysis of the ultra-fast radio bursts:
   * https://ui.adsabs.harvard.edu/abs/2023arXiv230702303S/abstract
   * https://www.nature.com/articles/s41550-023-02101-x
- First detection of the bursts at 8 GHz: https://ui.adsabs.harvard.edu/abs/2018ApJ...863....2G/abstract
- More bursts from the same dataset with machine learning detections: https://ui.adsabs.harvard.edu/abs/2018ApJ...866..149Z/abstract
- Explaining the Breakthrough Listen project: https://ui.adsabs.harvard.edu/abs/2017AcAau.139...98W/abstract
- Explaining the GBT breakthrough listen recorder: https://ui.adsabs.harvard.edu/abs/2018PASP..130d4502M/abstract
- Explaining the data formats: https://ui.adsabs.harvard.edu/abs/2019PASP..131l4505L/abstract
- Python 2 code to work with the baseband data: https://github.com/greghell/extractor (NOTE THAT IT IS PYTHON 2!!) (I recommend using Python 2.7 if you make use of that repo)
- Structure of the baseband data: https://github.com/UCBerkeleySETI/breakthrough/blob/master/doc/RAW-File-Format.md
- More information: https://github.com/UCBerkeleySETI/breakthrough/blob/master/GBT/waterfall.md
- A version of dspsr, called bl-dspsr, that can work with the GBT BL baseband data: https://github.com/UCBerkeleySETI/bl-dspsr

## Software

- The data was processed on multiple machines with various operating systems, which include, but are not limited to, macOS, Ubuntu and centOS.
- All the used software is open source, see the section above for more information, and also see the 'software' section in the paper.

## Figures and Tables

The files in this Zenodo package should be self-explanatory. E.g. `table_1.tar` contains all the scripts/notebooks/files needed to make table_1, and also contains table 1 itself. 
The file: 'general_info.tar' is basically a txt file with the same info as provided here and it contains an offline version of the Breakthrough Listen blogpost that that explains the raw data. 
The file: `helper_functions.tar` is a tarball that contains a Python file with a collection of helper functions that are used in the Jupyter notebooks (and the figures are made in the Jupyter notebooks). It also contains some files that are needed to e.g. remove the instrumental delay from the data.
 
## End-to-End analysis scripts
The Python code/Jupyter notebooks in the tarfiles are end-to-end. 

## Intermediate data products  

The file `data_and_data_info.tar` contains two intermediate data products (both several gigabytes in size) and a txt file explaining the files and how they were made. Due to the Zenodo file size limitations I cannot upload everything. Please contact me at snelders@astron.nl or m.p.snelders@uva.nl or via ORCID to request any other files.