Pre-Thanksgiving Update

Progress continues to be slow, but measurable, with the broadband spectra project.

During discussions with Geoff, he repeated a fact that I’d really failed to fully appreciate: a key aspect of this project is demonstrating that the results I get are reliable and reproducible.

This wasn’t news to me, but it had always seemed to me to be both obvious and vague. Clearly, you want reproducible and reliable results, but how do you go about showing that? When Geoff mentioned this goal most recently, though, I had the thought that I have an easy way to show reliability that I’ve been completely failing to take advantage of. I haven’t been cross-checking my results at all. All of my measurements are relative to one calibrator or another: measure calibrator A, observe source X, get brightness X(A). Measure calibrator B, observe source Y, get brightness Y(B). But I’ve basically neglected to do the obvious pairing of measuring calibrator B, observing source X, and getting brightness X(B). If systematics are controlled and uncertainties are understood, X(A) and X(B) should agree to within their uncertainties.

Unfortunately, a lot of my data don’t have multiple calibrators available, purely as a side effect of what is visible at any given time. But I certainly have a fair amount of data that can be used for this kind of cross-checking.

I started pursuing this idea and obtained a pretty nice result fairly quickly. I soon realized that the measurements I was getting based on the calibrator 3c48 were fairly discrepant from the other ones. I looked into this and, lo and behold, the model I was using for 3c48 wasn’t accurate, and better values that I obtained were different in almost exactly the way my measurements indicated they needed to be. That was encouraging.

Even better, I remembered that the ATA has a large library of archival long-duration calibrator observations. I can use these data to  test out my processing pipeline and do the kinds of cross- checks mentioned above. I can also assess the stability of my results in general and look for other issues.

Already, I found one further problem: for a long time, I’ve been normalizing my results by dividing my crosscorrelations by my autocorrelations. This has some helpful effects, but looking at the calibrator observations revealed to me that the autocorrelations will drift up and down in ways not seen in the cross-correlations, damaging my amplitude calibration. Stopping this practice seems to improve my results even more, though I haven’t reprocessed everything yet.

I was sick at the end of last week, so work slowed down a bit, but I hope to keep on pushing this. I believe there’s at least one more nontrivial systematic effect to deal with, but even that one may not be significant enough to affect my results. If I can figure out how to compensate for it, though, I think my reduction process will be in extremely good shape.