Efficiency and Completeness (again)

Finally I think I am doing this right. The log file with this code is here:
.../logs/110209log.pro

Below is an email to Adam explaining my problems, and newest findings
~~~~~~~
Adam,
For some reason when I wget the file, and the file is already in my directory, it doesn't overwrite it, but saves it as a different version:

BOSS_Quasars_3PCplus_v1.1_wcomp.fits.6

I didn't realize this was happening until just now, and so I kept re-downloading it, but then using the old version of the file (because BOSS_Quasars_3PCplus_v1.1_wcomp.fits was still the old version). Sorry about that!

I'm now getting the same numbers as you for the following:

print, n_elements(where(stripe82targets.like_ratio_core gt 0.543214))/45.93

20.03

spherematch, stripe82targets.ra, stripe82targets.dec, stripe82quasars.ra, stripe82quasars.dec, 2./3600, i1, i2, d12
print, n_elements(where(stripe82targets[i1].like_ratio_core gt 0.543214))/45.93

10.47

In terms of filling out your table I get the following numbers:

Here are the column headings (i've modified them slightly):
1) Spec Completeness
2) Area (deg^2)
3) Total fibers /deg^2 at like thresh > 0.543214
4) # z > 2.15 quasars confirmed / deg^2
5) # z < 2.15 quasars confirmed (low redshift) / deg^2
6) others that got a fiber (not quasars) / deg^2
7) not confirmed (didn't get fiber) / deg^2
8) Total known z > 2.15 quasars / deg^2
9) Total known z < 2.15 quasars / deg^2

(1) (2) (3) (4) (5) (6) (7) (8) (9)
0.5 45.93 20.03 10.47 0.348 2.24 6.97 24.21 18.05
0.6 37.80 20.45 11.24 0.397 2.27 6.53 25.61 15.77
0.7 26.61 21.12 11.01 0.526 2.56 7.03 25.82 11.56
0.8 14.02 22.40 10.70 0.856 3.21 7.63 24.05 6.065
0.9 2.124 18.36 8.473 0.941 1.41 7.53 20.24 5.178

Note, that as you say, col (4 + 5 + 6 + 7) = col 3

So I understand what you are saying that most of the targets that didn't get a fiber (column 7) are not quasars because they weren't found with the coadded data.

So it seems like you are saying that I should be defining the efficiency as:

col (4) / col (3) = 52% (@ spec completeness 0.5)

And the completeness as:
col (4) / col (8) = 43$ (@ spec completeness 0.5)


These numbers change the following way with spec competeness:

spec comp efficiency completeness
0.6 55% 44%
0.7 50% 43%
0.8 48% 44%
0.9 46% 42%

So which of these should I use? You had mentioned there was another step....
Jessica



Below is the code I used to generate these numbers:
; Read in the targeting file
adamfile = '/home/jessica/boss/chunk1_wcomp.fits'
targets = mrdfits(adamfile, 1)
stripe82targets1 = targets
newratio = stripe82targets1.LIKE_RATIO_CORE

; Read in the truthtable file
truthfile = '/home/jessica/boss/BOSS_Quasars_3PCplus_v1.1_wcomp.fits'
collateinfo1 = mrdfits(truthfile, 1)
stripe82data1 = collateinfo1
;High human confidence quasars (with z > 2.15)
confcut = where(stripe82data1.z_conf_person GE 2 and stripe82data1.Z_PERSON GE 2.15)
stripe82quasars1 = stripe82data1[confcut] ; with redshift > 2.15
lowqsocut = where(stripe82data1.z_conf_person GE 2 and stripe82data1.Z_PERSON GE 0.5 and stripe82data1.Z_PERSON LT 2.15)
stripe82lowquasars1 = stripe82data1[lowqsocut] ; with 0.5 < redshift < 2.15

compthresh = 0.9
wphot = where(stripe82targets1.completeness ge compthresh)
wspec = where(stripe82quasars1.completeness ge compthresh)
wdata = where(stripe82data1.completeness ge compthresh)
wlow = where(stripe82lowquasars1.completeness ge compthresh)

areas = mrdfits('/home/jessica/boss/area_wcomp.fits',1)
areacol = where(areas.comp_thresh ge compthresh)
thisArea = areas[areacol[0]].area

stripe82targets = stripe82targets1[wphot]
stripe82quasars = stripe82quasars1[wspec]
stripe82data = stripe82data1[wdata]
stripe82lowqso = stripe82lowquasars1[wlow]

area = 219.9 ; we know the exact area of the chunk1 file
tpsd = round(20.0*area)
sortRatio = targets[reverse(sort(targets.LIKE_RATIO_CORE))].LIKE_RATIO_CORE
threshold = sortRatio[tpsd-1]

;Comp threshold
print, compthresh

;Area
print, thisArea

;Like Threshold
print, threshold

;Likelihood Targets per square degree
thistpsd = n_elements(where(stripe82targets.like_ratio_core gt threshold))/thisArea
print, thistpsd

spherematch, stripe82targets.ra, stripe82targets.dec, stripe82quasars.ra, stripe82quasars.dec, 2./3600, i1, i2, d12
; Likelihood Quasars targeted per square degree
qpsd = n_elements(where(stripe82targets[i1].like_ratio_core gt threshold))/thisArea
print, qpsd


spherematch, stripe82targets.ra, stripe82targets.dec, stripe82lowqso.ra, stripe82lowqso.dec, 2./3600, i1, i2, d12
; Likelihood Low Redshift Quasars targeted per square degree
lqpsd = n_elements(where(stripe82targets[i1].like_ratio_core gt threshold))/thisArea
print, lqpsd

spherematch, stripe82targets.ra, stripe82targets.dec, stripe82data.ra, stripe82data.dec, 2./3600, i1, i2, d12
; Others targeted per square degree
otpsd = n_elements(where(stripe82targets[i1].like_ratio_core gt threshold))/thisArea
print, otpsd - (qpsd + lqpsd)

;Likelihood fibers not targeted
print, thistpsd-otpsd


;Total z > 2.15 qsos
totalqsos = n_elements(stripe82quasars)/thisArea
print, totalqsos

;Total 0.5 < z < 2.15 qsos
totallowqsos = n_elements(stripe82lowqso)/thisArea
print, totallowqsos

;Efficiency
print, qpsd/thistpsd

;Completeness
print, qpsd/totalqsos

Single Epoch Likelihood

I've been playing with the single epoch likelihoods cross checked with the truth table. The rest of the targeting will be on single epoch measurements (i.e. not stripe 82) so these are a more accurate measurement of how well the likelihood is doing. I've been playing with adjusting various parameters like how we define "variability" in terms of the chi^2 cut, if we should add variable objects from the L_everything catalog to L_QSO.

Below are the raw numbers of quasars targeted (normalized to targeting of ~40/degree^2) as well as the accuracy of each method:

No variability Method
; number quasars
1029
;percent accuracy
0.545986
; total targeted
1881


Variable objects with chi^2 > 1.5 subtracted from L_everything
; number quasars
1047
;percent accuracy
0.556619
; total targeted
1881


Variable objects with chi^2 > 1.5 subtracted from L_everything
Variable objects with chi^2 > 1.5 added to L_QSO
; number quasars
1033
;percent accuracy
0.549176
;total targeted
1881


Variable objects with chi^2 > 1.1 subtracted from L_everything
; number quasars
1053
;percent accuracy
0.559809
; total targeted
1881



Variable objects with chi^2 > 1.2 subtracted from L_everything
; number quasars
1055
;percent accuracy
0.560872
; total targeted
1881


Variable objects with chi^2 > 1.3 subtracted from L_everything
; number quasars
1054
;percent accuracy
0.560340
; total targeted
1881


Variable objects with chi^2 > 1.4 subtracted from L_everything
; number quasars
1051
;percent accuracy
0.558745
; total targeted
1881


Variable objects with chi^2 > 1.2 subtracted from L_everything
Variable objects with chi^2 > 1.6 added to L_QSO
; number quasars
1036
;percent accuracy
0.550771
;total targeted
1881

Variable objects with chi^2 > 1.2 subtracted from L_everything
Variable objects with chi^2 > 1.5 added to L_QSO
; number quasars
1035
;percent accuracy
0.550239
;total targeted
1881

Variable objects with chi^2 > 1.2 subtracted from L_everything
Variable objects with chi^2 > 1.4 added to L_QSO
; number quasars
1034
;percent accuracy
0.549708
;total targeted
1881


Variable objects with chi^2 > 1.2 subtracted from L_everything
Variable objects with chi^2 > 1.3 added to L_QSO
; number quasars
1029
;percent accuracy
0.547049
;total targeted
1881

It seems that the method with the best accuracy (56%) is variable objects with chi^2 > 1.2 subtracted from L_everything.

Here is a plot of the L_everything (non variable) vs L_QSO:

Magenta objects are targeted QSOs.
Cyan objects are targeted stars.
Green objects are not targeted stars.
Red objects are missed qsos.

It looks like we might be able to make the L_ratio a function of L_QSO to perhaps pick up a few more of the red objects at the top right corner.

Another interested plot is looking at likelihood-ratio vs g-band flux:

The color scheme is the same as first plot

We might be able to get more missed QSOs (red) if we target faint (g-flux less than 5) objects with a L_ratio close to the cut. I've also plotted likelihood-ratio vs g-r magnitude:

The color scheme is the same as first plot

Again, we might be able to use the fact that the missed QSOs (red) are clustered.

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Cool Tip of The Day:
To download file directly from web using the command line:
wget http://www.downloadingurl.com/filetodownload.fits

If it is from a password protected page (like the wiki) then do the following:
wget --http-user=myusername --http-password=mypassword http://www.downloadingurl.com/filetodownload.fits