I downloaded the latest BOSS data and randoms from Will Percival as Martin White suggested in my email exchange with him yesterday.

There are in the following location on the wiki:

https://trac.sdss3.org/wiki/BOSS/clustering/cats

I put them here on riemann:

/clusterfs/riemann/raid001/jessica/boss/

galaxy-wjp-main008-LOWZ-020211-cut.txt

random-wjp-main008-LOWZ-020211-cut-small.txt

galaxy-wjp-merge-CMASS-020211-cut.txt

random-wjp-merge-CMASS-020211-cut-small.txt

The format of these files is:

ra / degrees

dec / degrees

redshift

galaxy weight

sector completeness

close pair flag

THING_ID

MASK POLY ID

ID for galaxy in spAll file (not in std format)

I read them in and below are plots. As you can see, the distribution of the randoms matches the data. So there is something wrong with what I am doing. At this point though, I'm not going to waste more time trying to fix my masks/randoms. I'm going to use these catalogs and re-run the correlation functions to see if this fixes things.

I'm also going to ask Shirley or Eric to run a correlation function on the same data to check that we get the same answer.

The code to make the above plots is in the following log file:

../logs/110302log/pro

thisfile = '/clusterfs/riemann/raid001/jessica/boss/galaxy-wjp-main008-LOWZ-020211-cut.txt'

readcol,thisfile,slra,sldec,slz,x,x,x,x,x,x,format='(F,F,F,F,F,F,F,F,F)'

thisfile = '/clusterfs/riemann/raid001/jessica/boss/galaxy-wjp-merge-CMASS-020211-cut.txt'

readcol,thisfile,sra,sdec,sz,x,x,x,x,x,x,format='(F,F,F,F,F,F,F,F,F)'

sra = [sra,slra]

sdec = [sdec,sldec]

sz = [sz,slz]

thisfile = '/clusterfs/riemann/raid001/jessica/boss/random-wjp-main008-LOWZ-020211-cut-small.txt'

readcol,thisfile,rslra,rsldec,rslz,x,x,x,x,x,x,format='(F,F,F,F,F,F,F,F,F)'

thisfile = '/clusterfs/riemann/raid001/jessica/boss/random-wjp-merge-CMASS-020211-cut-small.txt'

readcol,thisfile,rsra,rsdec,rsz,x,x,x,x,x,x,format='(F,F,F,F,F,F,F,F,F)'

rsra = [rsra,rslra]

rsdec = [rsdec,rsldec]

rsz = [rsz,rslz]

xtit = 'Ra'

ytit = 'Dec'

mtit = 'Ra vs Dec'

window,xsize=700,ysize=600

plot, sra, sdec, ps = 3, xrange = [110,130], yrange=[40,55],XTITLE = xtit, YTITLE =ytit, TITLE = mtit, charsize = 1.5, charthick = 1

oplot, rsra, rsdec, ps=3, color = fsc_color('green')

;Make histogram of the dec distributions

data = sdec

datamin = min(sdec)

datamax = max(sdec)

binsize = (datamax - datamin)/100

xtit = 'Dec Distribution'

ytit = '% in bin'

mtit = 'Histogram of Spectroscopic Dec'

window,xsize=700,ysize=600

hist = HISTOGRAM(data, binsize = binsize, min = datamin, max = datamax)

bins = FINDGEN(N_ELEMENTS(hist))*binsize + datamin

plot, bins, hist*1.0/n_elements(data), PSYM = 10, xrange = [datamin,datamax], yrange=[0,1.0*max(hist)/n_elements(data)],XTITLE = xtit, YTITLE =ytit, TITLE = mtit, charsize = 1.5, charthick = 1

data = rsdec

datamin = min(rsdec)

datamax = max(rsdec)

hist = HISTOGRAM(data, binsize = binsize, min = datamin, max = datamax)

bins = FINDGEN(N_ELEMENTS(hist))*binsize + datamin

oplot, bins, 1.0*hist/n_elements(data), PSYM = 10, color = fsc_color('green')

;Make histogram of the dec distributions

data = sdec

datamin = min(sdec)

datamax = max(sdec)

binsize = (datamax - datamin)/1000

xtit = 'Dec Distribution'

ytit = '# in bin'

mtit = 'Histogram of Spectroscopic Dec'

window,xsize=700,ysize=600

hist = HISTOGRAM(data, binsize = binsize, min = datamin, max = datamax)

bins = FINDGEN(N_ELEMENTS(hist))*binsize + datamin

plot, bins, hist*1.0, PSYM = 10, xrange = [datamin,datamax], yrange=[0,1.0*max(hist)],XTITLE = xtit, YTITLE =ytit, TITLE = mtit, charsize = 1.5, charthick = 1

data = rsdec

datamin = min(rsdec)

datamax = max(rsdec)

hist = HISTOGRAM(data, binsize = binsize, min = datamin, max = datamax)

bins = FINDGEN(N_ELEMENTS(hist))*binsize + datamin

oplot, bins, 1.0*hist, PSYM = 10, color = fsc_color('green')

;Make histogram of the dec distributions

data = sra

datamin = min(sra)

datamax = max(sra)

binsize = (datamax - datamin)/100

xtit = 'Ra Distribution'

ytit = '% in bin'

mtit = 'Histogram of Spectroscopic Ra'

window,xsize=700,ysize=600

hist = HISTOGRAM(data, binsize = binsize, min = datamin, max = datamax)

bins = FINDGEN(N_ELEMENTS(hist))*binsize + datamin

plot, bins, hist*1.0/n_elements(data), PSYM = 10, xrange = [datamin,datamax], yrange=[0,1.0*max(hist)/n_elements(data)],XTITLE = xtit, YTITLE =ytit, TITLE = mtit, charsize = 1.5, charthick = 1

data = rsra

datamin = min(rsra)

datamax = max(rsra)

hist = HISTOGRAM(data, binsize = binsize, min = datamin, max = datamax)

bins = FINDGEN(N_ELEMENTS(hist))*binsize + datamin

oplot, bins, 1.0*hist/n_elements(data), PSYM = 10, color = fsc_color('green')

;Make histogram of the dec distributions

data = sra

datamin = min(sra)

datamax = max(sra)

binsize = (datamax - datamin)/1000

xtit = 'RA Distribution'

ytit = '# in bin'

mtit = 'Histogram of Spectroscopic RA'

window,xsize=700,ysize=600

hist = HISTOGRAM(data, binsize = binsize, min = datamin, max = datamax)

bins = FINDGEN(N_ELEMENTS(hist))*binsize + datamin

plot, bins, hist*1.0, PSYM = 10, xrange = [datamin,datamax], yrange=[0,1.0*max(hist)],XTITLE = xtit, YTITLE =ytit, TITLE = mtit, charsize = 1.5, charthick = 1

data = rsra

datamin = min(rsra)

datamax = max(rsra)

hist = HISTOGRAM(data, binsize = binsize, min = datamin, max = datamax)

bins = FINDGEN(N_ELEMENTS(hist))*binsize + datamin

oplot, bins, 1.0*hist, PSYM = 10, color = fsc_color('green')

;Make histogram of the z distributions

data = sz

datamin = min(sz)

datamax = max(sz)

binsize = (datamax - datamin)/50

xtit = 'Redshift Distribution'

ytit = '% in bin'

mtit = 'Histogram of Spectroscopic z'

window,xsize=700,ysize=600

hist = HISTOGRAM(data, binsize = binsize, min = datamin, max = datamax)

bins = FINDGEN(N_ELEMENTS(hist))*binsize + datamin

plot, bins, hist*1.0/n_elements(data), PSYM = 10, xrange = [datamin,datamax], yrange=[0,1.0*max(hist)/n_elements(data)],XTITLE = xtit, YTITLE =ytit, TITLE = mtit, charsize = 1.5, charthick = 1

data = rsz

datamin = min(rsz)

datamax = max(rsz)

hist = HISTOGRAM(data, binsize = binsize, min = datamin, max = datamax)

bins = FINDGEN(N_ELEMENTS(hist))*binsize + datamin

oplot, bins, 1.0*hist/n_elements(data), PSYM = 10, color = fsc_color('green')

;Plot Data that is inside the mask

window,xsize=700,ysize=600

xobject = sra

yobject = sdec

xtit = 'RA'

ytit = 'Dec'

mtit = 'SDSS Spectroscopic Data + Masks'

plot, xobject, yobject, psym=3, symsize=2, XTITLE = xtit, YTITLE = ytit, TITLE = mtit, charsize = 2, charthick = 1, thick = 2, xthick=2, ythick=2

oplot, xobject, yobject, ps=3, color=fsc_color('white')

oplot, rsra, rsdec, ps=3, color=fsc_color('green')

## Wednesday, March 2, 2011

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Jess, this is great news, I am so happy we now have a matching set of data and randoms!! Awesome :) I think the next steps are (roughly in order of importance):

ReplyDelete1) Compute the 2DCF and see if it still looks weirdly flat, and negative in strange places.

2) Ask Shirley or Eric to compute 2DCF and 3DCF with their code so you can double check what your code is doing. Be aware, if they are computing it in subdivisions so as to get bootstrapped error bars, there can be a difference of a few percent form computing the CF in the whole volume. It shouldn't be this way (the randoms should compensate) but it just is. I've spent weeks obsessing about this, but I don't think you need to.

3) Do some of the tests we talked about on the 3DCF: check sensitivity to bin width (narrowness) and shift the center around by a few percent to see if there are big changes in the measured 3DCF.

4) Run the CFs through the reconstruction code. Do this last, I want to check the CFs to make sure we REALLY REALY believe them before we spend a bunch of time fiddling with the reconstruction parameters. The reconstruction won't work if the CFs are wrong.