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SSN Analysis Pipeline V2.0 for Data Reduction and Candidate Selection

This document outlines the general pipeline for the SSN analysis version 2.0, utilizing csh codes. The process includes checking the upload directory for new data and archiving existing data, identifying reduction IDs, creating projections against template fits, and applying background corrections. Additionally, it involves catalog matching, seeing and blur corrections using IRAF, and selecting candidate figures based on fitting goodness. Future plans address sampling issues and candidate optimization, focusing on bright supernovae with a SNR greater than ~10.

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SSN Analysis Pipeline V2.0 for Data Reduction and Candidate Selection

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  1. Searching SN -- SSN 2004年7月1日星期四

  2. # Here We give a general pipline(s) of SSN. # version 2.0 acoording to *.csh codes. ----------------------------------------------------------- Check upload dir for new data & archive data | | Find reduction ID of new data copy them into workspace | 'cob50' <== 3 (or more) image --> sn01.fit | | make projection with respect to template.fit --> sn.fit template.fit | | | V | catalog & -> 'newcrrb' --->| (optional) mask regions | Background crrected image: clean_crrb.fit |

  3. | (Catalog Matching) LINE 2 ----- LINE 3 (Variables) | LINE 1 (Subtraction) | Find the difference in Seeing and blur one image by iraf for the aim that equivalent-seeing subtraction | template.fit | experimented 'Kernel' & its v.s. -> See'_diff --->|<--- variation over CCD clean_crrb.fit | |<--- Mask known stars with | a definite catalog of stars (DCS) | subIMG = clean_crrb.fit - template.fit(Seeing_corrected) --------------------------------------------------- | Reject the black rings --->|?<--- de-noise (wavelet + 921trans) (by fitting a PSF | or double-gaussian) |

  4. | from LINE 2 --+-- from LINE 3 | | Candidates | Sort by goodness of fitting ->|<- Sort by dsitance to galaxies | | | (Need the DCS) ~10 or less Cadidates | | - generate cadidate figures and webpages and publish them; - inform colleagues for a check by emails.

  5. Future Plan • 空间采样不足。  • 暗弱的SN。  • 靠近星系的候选体  • 亮的SN（SNR > ~10）  • 减少候选体 

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