pyspecdenoise 1.0.2

Perform Fourier de-noising of supernova spectra (Liu et al., 2016). Follows Finneran et al. (2024). See: https://arxiv.org/abs/2411.12574.

pyspecdenoise documentation

Python library for removing noise from supernova spectra using a Fourier method. Implemented by Gabriel Finneran at University College Dublin, Ireland.

Here is an example showing the input and result for a spectrum of SN2004gq (spectrum taken from WISeREP).

A full description of the tool is given here. Further information may be found in Finneran et al. (2024) (see below for details on how to cite this work!).

This algorithm is based on the procedure presented in Liu et al. (2016) (see their Appendix B). This is the first publicly available implementation of this algorithm written in Python.

An IDL version of this code is available from the original developers.

This package can be installed from PyPI using pip:

pip install pyspecdenoise

Full documentation is available here.

Issues can be logged here.

You can also contact me via email.

Basic description

1. Rebin the spectrum on a log-wavelength axis.
2. Resample spectrum into equal-width bins. Uses the smallest dispersion as the bin width.
3. Take the FFT of the flux.
4. Define the range of wavenumbers/velocities for spectral features (see notes); the FFT indices are determined using k_low and k_high.
5. Fit the magnitude (M) spectrum with a power law between k_low and k_high.
6. Compute MEAN(M).
7. k_noise is the point of intersection between the power law fit and MEAN(M).
8. Set M = 0 for k > k_noise.
9. Invert FFT.
10. Resample spectrum to the original linear grid.

Here is an example image showing the procedure used to determine k_noise (using the same spectrum of SN2004gq from WISeREP):

Notes:

• k is related to the velocity of spectral features in the SN spectrum by k = c/v.
• k can be chosen to exclude high and low velocity features that are likely not due to the SN.
• The default values of k are k=300 (3000 km/s) and k=3 (100000 km/s) (Liu et al. 2016).

How to cite this code in your work

If you use pyspecdenoise in your work, please consider citing Finneran et al. (2024) (see below for bibtex).

I would also appreciate it if you could add an acknowledgment such as:

To remove noise from supernova spectra, this work has made use of \texttt{pyspecdenoise},
implemented by Gabriel Finneran and available at: \url{https://github.com/GabrielF98/fouriersmooth}.

@ARTICLE{2024arXiv241112574F,
      author = {{Finneran}, Gabriel and {Martin-Carrillo}, Antonio},
      title = "{Measuring the expansion velocities of broad-line Ic supernovae: An investigation of neglected sources of error in two popular methods}",
      journal = {arXiv e-prints},
      keywords = {Astrophysics - High Energy Astrophysical Phenomena},
      year = 2024,
      month = nov,
      eid = {arXiv:2411.12574},
      pages = {arXiv:2411.12574},
      doi = {10.48550/arXiv.2411.12574},
      archivePrefix = {arXiv},
      eprint = {2411.12574},
      primaryClass = {astro-ph.HE},
      adsurl = {https://ui.adsabs.harvard.edu/abs/2024arXiv241112574F},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}