Below is an email I sent him tonight:
> print, n_elements(where(stripe82targets.like_ratio_core gt 0.543214))/45.93
> 20.03
I get the same number as you here: 20.0305> 20.03
But I am confused as to why you are calling this number to total
fibers. Wouldn't the fibers be the match between stripe82targets gt
threshold and all those in the BOSS_Quasars_3PCplus_v1.1_
(I call this stripe82data)
Because what you calculate above is all potential targets that pass
the threshold, but a large fraction of them were not observed when you
look at the spherematch between stripe82targets and stripe82data:
; Total objects targeted
spherematch, stripe82targets.ra, stripe82targets.dec, stripe82data.ra,
stripe82data.dec, 2./3600, i1, i2, d12totaltarget = n_elements(where(
threshold))/thisArea
print, totaltarget
11.5174
Which means that of the 20 objects per square degree we expected to
target, only 12.7 were actually targeted... this makes since when we
are using a spec completeness of 0.5... and should go up as we
increase go down in the table correct?
I agree that the area = 45.93 and the threshold = 0.543214
However when I do the below, I get:
> 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
10.2330> stripe82quasars.dec, 2./3600, i1, i2, d12
> print, n_elements(where(
> 0.543214))/45.93
> 10.47
Not sure where the 0.74 difference is coming from....
When I look at the total targets number of objects per square degree
that passed the likelihood threshold but were targeted, this was:
; Total targeted
spherematch, stripe82targets.ra, stripe82targets.dec, stripe82data.ra,
stripe82data.dec, 2./3600, i1, i2, d12totaltarget = n_elements(where(
threshold))/thisArea
print, totaltarget
11.5174
Which means that we have only 1.284 targets per square degree that
were targeted by likelihood and not quasars correct?
The number that are lower redshift quasars is:
lowqsocut = where(stripe82data.z_conf_
stripe82data.Z_PERSON GE 0.5 and stripe82data.Z_PERSON LT 2.15)
stripe82lowquasars = stripe82data[lowqsocut] ; with 0.5 < redshift < 2.15
spherematch, stripe82targets.ra, stripe82targets.dec,
; Low Redshift Quasars targeted per square degree
lqpsd = n_elements(where(
threshold))/thisArea
print, lqpsd
0.326581
In terms of filling out your table I get the following numbers:
Spec Area Total # Like z > 2.15 other not ALL > 2.15 ALL other
Comp fibers targets QSOs QSOs QSOs QSOs QSOs
0.5 45.93 20.03 11.52 10.23 0.33 0.96 23.32 6.79
0.6 37.80 20.45 12.43 11.03 0.37 1.0 24.71 8.25
0.7 26.12 21.11 12.74 10.78 0.53 1.42 24.99 11.73
0.8 14.02 22.40 13.27 10.42 0.85 1.99 23.19 22.26
0.9 2.12 18.35 10.82 8.47 0.94 1.41 20.24 146.86
Below is the code I used to generate the table.
Let me know if you have thoughts.
Jessica
; Read in the targeting file
;erinfile = '/home/jessica/boss/boss-qso-
;targets = mrdfits(erinfile, 2)
adamfile = '/home/jessica/boss/chunk1_
targets = mrdfits(adamfile, 1)
;stripe82cut = where((targets.dec GE -1.25) and (targets.dec LE 1.25)
and ((targets.ra GT 317) or (targets.ra LT 45)))
;stripe82targets = targets[stripe82cut]
stripe82targets = targets
newratio = stripe82targets.LIKE_RATIO_
; Read in the truthtable file
truthfile = '/home/jessica/boss/BOSS_
collateinfo = mrdfits(truthfile, 1)
;High human confidence quasars (with z > 2.15)
confcut = where(stripe82data.z_conf_person GE 2 and
stripe82data.Z_PERSON GE 2.15)
stripe82quasars = stripe82data[confcut] ; with redshift > 2.15confcut = where(stripe82data.z_conf_
stripe82data.Z_PERSON GE 2.15)
lowqsocut = where(stripe82data.z_conf_
stripe82data.Z_PERSON GE 0.5 and stripe82data.Z_PERSON LT 2.15)
stripe82lowquasars = stripe82data[lowqsocut] ; with 0.5 < redshift < 2.15
compthresh = 0.9
wphot = where(stripe82targets.completeness ge compthresh)
wspec = where(stripe82quasars.completeness ge compthresh)
wdata = where(stripe82data.completeness ge compthresh)
;plot, stripe82targets.ra, stripe82targets.dec, psym=3
;oplot, stripe82targets[wphot].ra, stripe82targets[wphot].dec, psym=3,
color=FSC_COLOR('blue')
;oplot, stripe82quasars[wspec].ra, stripe82quasars[wspec].dec, psym=7,
color=FSC_COLOR('red')
areas = mrdfits('/home/jessica/boss/area_wcomp.fits',1)
thisArea = areas(where(areas.comp_thresh eq compthresh)).areawspec = where(stripe82quasars.
wdata = where(stripe82data.
;plot, stripe82targets.ra, stripe82targets.dec, psym=3
;oplot, stripe82targets[wphot].ra, stripe82targets[wphot].dec, psym=3,
color=FSC_COLOR('blue')
;oplot, stripe82quasars[wspec].ra, stripe82quasars[wspec].dec, psym=7,
color=FSC_COLOR('red')
areas = mrdfits('/home/jessica/boss/
stripe82targets = stripe82targets[wphot]
stripe82quasars = stripe82quasars[wspec]
stripe82data = stripe82data[wdata]
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 thresholdtpsd = round(20.0*area)
sortRatio = targets[reverse(sort(targets.
threshold = sortRatio[tpsd-1]
print, compthresh
;Area
print, thisArea
;Likelihood Targets per square degree
thistpsd = n_elements(where(
threshold))/thisArea
print, thistpsd
; Total objects targeted
spherematch, stripe82targets.ra, stripe82targets.dec, stripe82data.ra,
stripe82data.dec, 2./3600, i1, i2, d12totaltarget = n_elements(where(
threshold))/thisArea
print, totaltarget
spherematch, stripe82targets.ra, stripe82targets.dec,
stripe82quasars.ra, stripe82quasars.dec, 2./3600, i1, i2, d12
qpsd = n_elements(where(
threshold))/thisArea
print, qpsd
spherematch, stripe82targets.ra, stripe82targets.dec,
; Likelihood Low Redshift Quasars targeted per square degree
lqpsd = n_elements(where(
threshold))/thisArea
print, lqpsd
;Likelihood targeted not qsos
print, totaltarget - qpsd - lqpsd
;Total z > 2.15 qsos
totalqsos = n_elements(stripe82quasars)/
print, totalqsos
;Total 0.5 < z < 2.15 qsos
totallowqsos = n_elements(stripe82lowquasars)
print, totallowqsos
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