Welcome to nirHiss, a data reduction routine for the curviest JWST instrument! Here, you'll find all kinds of tricks to extracting high-precision NIRISS light curves (and subsequently transmission spectra)!
For installation, you can grab the latest release from PyPI by doing:
pip install nirhiss
or you can the latest development version by doing:
git clone https://github.com/afeinstein20/nirhiss
cd nirhiss
python setup.py install
ADF_extracted_stellar_spectra
- F277W filter from the median image, then gaussian filtered to smooth any noise
- Removing cosmic rays and interpolating over
- Using the 2D modeled background to get rid of 1/f noise
- Subtracting the median from each column
ADF_extracted_stellar_spectra_method2
- F277W filter from the median image, but only using the high outliers so it captures the 0th order effects and not any noise in the image
- DQ masked and interpolated pixels
- Using the SUBSTRIP256 model background provided on JDox
- Removing cosmic rays and interpolating over
- Removing bad integrations (5 in total)
- No additional background modeling
ADF_extracted_stellar_spectra_method3
- F277W filter from the median image, then gaussian filtered to smooth any noise
- DQ masked and interpolated pixels
- Using the SUBSTRIP256 model background provided on JDOX
- Removing cosmic rays and interpolating over
ADF_extracted_stellar_spectra_method4
- F277W filter from the median image, but only using the high outliers so it captures the 0th order effects and not any noise in the image
- DQ masked and interpolated pixels
- Using the SUBSTRIP256 model background provided on JDox
- Removing cosmic rays and interpolating over
- Minor additional background correction (only looking at pixels <1.8 sigma), interpolating, and smoothing with a Gaussian filter
ADF_extracted_wasp39_full_v5
- F277W filter from the median image, sigma clipped to isolate the 0th order
sources, then gaussian filtered to smooth any noise
- Scaling to 2 different sources and taking the median
- DQ masked and interpolated pixels
- Scaling the SUBSTRIP256 model background provided on JDox
- 1/f noise correction via Néstor's routine (no mask)
- Removing cosmic rays and interpolating over those pixels
- Minor additional background correction, interpolating, taking a median of all the models, and smoothing with a Gaussian filter
- Decreased box mask size for Orders 1 and 2 to 24 pixels each (diameter)