Thursday, September 24, 2009

Back to the Likelihoods

It's been a while since I have worked on the likelihood QSO selection method. With the next deadline for target selection is coming up, it's time to go "Back to the Likelihoods." Note to self: It more time efficient to keep working on something continuously than to not work on it for weeks and then waste a day trying to remember what I was doing.

Where we left off...
Below is a color-color (ug - gr) plot of the final likelihood selection objects for the commissioning data.

The white data points are a random sampling of 20,000 possible objects to target. The red data points are objects who's likelihood ratio is greater than 0.1, where likelihood ratio is defined as:

where L_QSO and L_everything, as described in "A Likely Result" are defined as:

The green data points are objects are objects who's likelihood ratio is greater than 0.1 and L_everything is greater than 10^-6. This is eliminate classification of "fringe" objects that are not close to any objects (and therefore have a small everything likelihood.

The likelihood was then run on the co-added Stripe 82 data and all of the above green objects were submitted as targets for the commissioning data.

What we need to work out...
  • Why are the likelihoods so small/large? The likelihoods should be a probability, but we have likelihood's spanning from 0-11.
  • Why is our completeness and efficiency on the MMT data so poor?
  • How does the likeliness compare to QSOs based on variability?
  • How well does this method work on single epoch Stripe 82 data versus the co-added images?

1 comment:

  1. Hey Jess, just a quick note on likelihood functions. Though they are typically defined in terms of Bayes' rule (which implies a value between 0 and 1), likelihood functions can be multiplied by an arbitrary constant. All that really matters are likelihood ratios, L1/L2 = (a*L1)/(a*L2), and maximum likelihood estimators, both of which are unaffected by that constant. So I wouldn't worry too much about the range of your likelihood functions.