1 / 11

Fringe correction in the PREREPIX package

This document details the implementation of a comprehensive fringe correction and preprocessing pipeline within the PREREPIX package applied to CFH12K data. Key steps include the generation of master frames such as BIAS and FLATS, and the application of robust fitting methods for scaling factors. Challenges addressed include masking issues and the necessity for quality assessments of the fringe correction results. The empirical methods demonstrated effectiveness even in non-optimal conditions, although further evaluation of residuals is required for quality assurance.

christmas
Télécharger la présentation

Fringe correction in the PREREPIX package

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Fringe correction in the PREREPIX package M. Radovich (OAC, Naples) Y. Mellier, E. Bertin, G. Missonnier (IAP)

  2. Prereduction • Implementation: PERL + PDL [+ pTK] • Status of the pipeline: all the prereduction steps are implemented • Main problems: fringe correction and masking • To be done: quality assessment (e.g. fringe correction); the generation of masks should be improved

  3. The Preprocessing steps Generation of master frames: • BIAS • DARK • DOME & TWILIGHT FLATS, • FRINGE • SUPERFLAT Science frames: preprocessing + generation of • Weight maps • Previews

  4. Fringe correction • The method: for each science frame we assume that • A smoothed background is subtracted from each image, the so obtained frames are combined to build a master fringe frame • Objects cannot be masked since “detections” may include fringes • For each science frame, the scaling factor is computed using a robust fitting method • >5 frames and large dither patterns are required !! • Is the fringe pattern stable during one night/run ? PCA analysis could be used. • To be done: evaluate fringe residuals

  5. Rescaling of the master fringe frame: counts in the master fringe and the image frames are displayed. The red line shows the results of the robust fit

  6. Example of fringe subtraction with and without rescaling K=0.83 K=1.0 Raw image (section)

  7. Fringe frame built from a run with: narrow dither sequence, only two separate fields Raw image (first field) Corrected image, first field Corrected image, second field

  8. Superflat • One master frame for each run, obtained from the combination of fringe corrected science frames. • To minimize the impact of bright stars, before combination science frames are “smoothed” using SExtractor

  9. Speed Example: processing of 300 files for each step

  10. Conclusions • The empirical method for fringe subtraction (combination of frames + computation of the scaling factor by a robust fit) works for CFH12K data even in non optimal conditions (narrow dither, few different pointings, one fringe frame per run ) • The algorithm is still slow • Need to evaluate residuals (how much the correction is good ?)

More Related