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.
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...
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?
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.
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