spectrophotometric parallax; optimization fail

Today was spectrophotometric-parallax day. I did writing in the paper, I presented the method at MPIA Milky Way Group Meeting, and Eilers (MPIA) and I refactored slightly the model. In the presentation I gave, we got lots of feedback about how to present the method, which I tried to record carefully in the to-do list at the top of our LaTeX document. We also realized that without much change, we could move the model from a model for magnitude to a direct model for the parallax, bypassing any physical idea of how the star indicates its parallax (which is through its brightness and its log-g, to leading order). So our model is now truly data-driven. We also realized that we could make changes to how we represent the spectral pixels that might make the parameters more well-behaved.

All these things are great things! But when we made the relevant code changes, everything borked. The reason appears simple: It is because the model has a bad pathology: While it has a very good, sensible, non-trivial optimum, it has an enormous family of degenerate trivial optima in which the exponential underflows, the predicted parallaxes are all zero, and the derivatives all vanish. And at 7400 free parameters, this degenerate set of minima has a huge space (huge entropy) to find and eat our optimizer. So by the end of the day, Eilers and I realized we have to get much more clever about initializing the optimizer.

Question of the day: Does the method need a name, like The Cygnet? Or is it okay to just call it “linear spectrophotometric parallax”?


  1. Cutsie names are popular these days. When Brian and I organized the HZT, we did not have a name. I think we realized in 1997 or so that we really do need a name to call ourselves and we chose a really boring one. Now it seems as if the name choice is the first thing in any science collaboration.

  2. and ... I look forward to a specphot parallax technique!