Radio Frequency Interference Mitigation based on the ArPLS and SumThreshold Method

Desciption of the RFI mitigation method (ArPLS-ST)

The proposed RFI mitigation method utilizes Asymmetrically Reweighted Penalized Least Squares algorithm (ArPLS) and SumThreshold (ST), referred to as ArPLS-ST. ArPLS is used to estimate the baseline of the time-frequency image. After baseline removal, the pixel intensity in the time-frequency image should be almost constant in the interference-free regions. Meanwhile, the RFI regions still remain much higher intensity. Then, SumThreshold is performred on the time-integral curve/Spectral Energy Distribution (SED) curve and time-frequency image to detect band RFI and blob RFI (a short and small-bandwidth interference typically covering nearly 100 microseconds and bandwidth of less than one MHz).

Running Code and Data

1. Download

• Code
• Baseline estimation: baseline.py includes ArPLS and Gaussian filter;
• RFI flagging: mitigation.py implements SumThreshold.
• save the link file: Sample data

2. Requirements

• Python2 or Python3
• numpy
• scipy
• matplotlib
• numba
• fitsio

3. Usage

In this code, you can run our methods on fits file. The main program detect the RFI in the time-frequency image utilizing ArPLS-ST and SumThrehold [1], respectively.

python main.py

The program will output three images as follow:

4. Citation

If you find ArPLS-ST useful in your research, please citing:

Zeng et al. 2021, MNRAS Vol.500, p.2969,
with bib entry here .

5. Reference

[1] SEEK