- i have a dataset which shows significantly different structure depending on the weighting (robust value) used when i image it. should i self-cal using maps with a natural weighting that show the diffuse emission or maps with a more uniform weighting which emphasize the point sources? i'm inclined to use a robust value around 0 because i feel like the point sources are more reliable self-cal targets (and contribute more flux) even though what i really care about is the diffuse stuff and the maps i make for the analysis will use a robust > 0.
- do i always need to self-cal using clean components from the entire primary beam even if the source i care about is only in the inner 15%? since it does seem true that "the best self-cal model is the one that contains the most flux" i think i am obligated to image the entire primary beam during self-calibration.
Saturday, June 30, 2007
self-calibration subtleties : 2 questions
Subscribe to:
Post Comments (Atom)
If you have a question about AIPS that CANNOT be answered by reading the
2 comments:
1) Self-cal is notoriously bad with diffuse emission. I'd go with the the weighting that emphasizes the point sources more. However, just a note that self-cal can mess up your diffuse flux if you're not careful (see point 2...)
2) It's best if you use the entire primary beam, for the simple reason that there is flux in point sources outside your inner 15%. It's important that you know where your flux is coming from - if you don't use the whole primary beam you can wind up including flux from point sources outside your 15% into your diffuse source at the center.
NOTE: This ALSO applies if you have a very very bright pointsource OUTSIDE the first null as well, and if you are trying to push down close to the confusion limit. If you still wind up with weird numbers and/or a pattern which suggests there's something bright you haven't accounted for, it might be worth checking to see if there's a very bright source feeding flux in from one of the sidelobes.
Post a Comment