Tolerances

npbins=50#the number of bins you want reconstructed
xcx, H
[[ 3.13171429e+06 2.89819398e+07 2.11350953e+07 ..., 6.51756443e+03
7.38601449e+03 7.16207338e+03]
[ 2.89819398e+07 2.71293688e+08 1.97689784e+08 ..., 3.36558204e+04
3.34155054e+04 3.21086078e+04]
[ 2.11350953e+07 1.97689784e+08 1.44896458e+08 ..., 2.64562703e+04
2.65681467e+04 2.55453536e+04]
...,
[ 6.51756443e+03 3.36558204e+04 2.64562703e+04 ..., 9.09323446e+05
1.98770981e+06 1.96953234e+06]
[ 7.38601449e+03 3.34155054e+04 2.65681467e+04 ..., 1.98770981e+06
1.03059328e+07 1.07986867e+07]
[ 7.16207338e+03 3.21086078e+04 2.55453536e+04 ..., 1.96953234e+06
1.07986867e+07 1.13617169e+07]] [[ 1. -2. 1. ..., 0. 0. 0.]
[-2. 5. -4. ..., 0. 0. 0.]
[ 1. -4. 6. ..., 0. 0. 0.]
...,
[ 0. 0. 0. ..., 6. -4. 1.]
[ 0. 0. 0. ..., -4. 5. -2.]
[ 0. 0. 0. ..., 1. -2. 1.]]

tol, oldtol, lam -4038.31477464 10000.0 22684922.5783
tol, oldtol, lam -1367.88941631 -4038.31477464 2268492.25783
tol, oldtol, lam -542.927820224 -1367.88941631 226849.225783
tol, oldtol, lam -760.905611638 -542.927820224 22684.9225783


npbins=40#the number of bins you want reconstructed
xcx, H [[ 3.13171429e+06 1.13794627e+08 6.02282157e+06 ..., 7.00883428e+03
6.75329978e+03 7.16207338e+03]
[ 1.13794627e+08 4.37870434e+09 2.16479904e+08 ..., 1.24665584e+05
1.18107780e+05 1.12744551e+05]
[ 6.02282157e+06 2.16479904e+08 1.20523129e+07 ..., 1.24121771e+04
1.18595239e+04 1.20725784e+04]
...,
[ 7.00883428e+03 1.24665584e+05 1.24121771e+04 ..., 2.01580179e+06
1.11912080e+06 1.32029857e+06]
[ 6.75329978e+03 1.18107780e+05 1.18595239e+04 ..., 1.11912080e+06
3.18799388e+06 5.78308988e+06]
[ 7.16207338e+03 1.12744551e+05 1.20725784e+04 ..., 1.32029857e+06
5.78308988e+06 1.13617169e+07]] [[ 1. -2. 1. ..., 0. 0. 0.]
[-2. 5. -4. ..., 0. 0. 0.]
[ 1. -4. 6. ..., 0. 0. 0.]
...,
[ 0. 0. 0. ..., 6. -4. 1.]
[ 0. 0. 0. ..., -4. 5. -2.]
[ 0. 0. 0. ..., 1. -2. 1.]]
tol, oldtol, lam -237.17436153 10000.0 4926448.15935
tol, oldtol, lam -996.686553071 -237.17436153 492644.815935


npbins=35#the number of bins you want reconstructed
xcx, H [[ 3.13171429e+06 1.33946843e+09 3.53599262e+06 ..., 8.05323215e+03
6.56050622e+03 7.16207338e+03]
[ 1.33946843e+09 1.87610982e+12 1.44012285e+09 ..., 1.45454452e+06
1.36393326e+06 1.28352738e+06]
[ 3.53599262e+06 1.44012285e+09 5.86162193e+06 ..., 1.22180250e+04
1.02988012e+04 1.07830348e+04]
...,
[ 8.05323215e+03 1.45454452e+06 1.22180250e+04 ..., 8.69706441e+06
1.41021650e+06 1.20796253e+06]
[ 6.56050622e+03 1.36393326e+06 1.02988012e+04 ..., 1.41021650e+06
1.79603850e+06 4.10569524e+06]
[ 7.16207338e+03 1.28352738e+06 1.07830348e+04 ..., 1.20796253e+06
4.10569524e+06 1.13617169e+07]] [[ 1. -2. 1. ..., 0. 0. 0.]
[-2. 5. -4. ..., 0. 0. 0.]
[ 1. -4. 6. ..., 0. 0. 0.]
...,
[ 0. 0. 0. ..., 6. -4. 1.]
[ 0. 0. 0. ..., -4. 5. -2.]
[ 0. 0. 0. ..., 1. -2. 1.]]
tol, oldtol, lam -652.405158717 10000.0 961480512.441
tol, oldtol, lam -2959.24584582 -652.405158717 96148051.2441


npbins=36#the number of bins you want reconstructed
xcx, H [[ 3.13171429e+06 5.03718197e+08 3.90751789e+06 ..., 7.71436959e+03
6.59215753e+03 7.16207338e+03]
[ 5.03718197e+08 1.33784332e+11 6.04891778e+08 ..., 5.46799638e+05
5.13404665e+05 4.84612444e+05]
[ 3.90751789e+06 6.04891778e+08 6.29937408e+06 ..., 1.18851615e+04
1.03957814e+04 1.08440179e+04]
...,
[ 7.71436959e+03 5.46799638e+05 1.18851615e+04 ..., 5.76527335e+06
1.26268711e+06 1.20442277e+06]
[ 6.59215753e+03 5.13404665e+05 1.03957814e+04 ..., 1.26268711e+06
2.00914237e+06 4.40622791e+06]
[ 7.16207338e+03 4.84612444e+05 1.08440179e+04 ..., 1.20442277e+06
4.40622791e+06 1.13617169e+07]] [[ 1. -2. 1. ..., 0. 0. 0.]
[-2. 5. -4. ..., 0. 0. 0.]
[ 1. -4. 6. ..., 0. 0. 0.]
...,
[ 0. 0. 0. ..., 6. -4. 1.]
[ 0. 0. 0. ..., -4. 5. -2.]
[ 0. 0. 0. ..., 1. -2. 1.]]
tol, oldtol, lam -434.056005416 10000.0 71452978.5684
tol, oldtol, lam -2218.02619076 -434.056005416 7145297.85684


npbins=34#the number of bins you want reconstructed
xcx, H [[ 3.13171429e+06 2.62056248e+09 3.21588187e+06 ..., 8.54242599e+03
6.53264064e+03 7.16207338e+03]
[ 2.62056248e+09 1.33106149e+13 2.50852716e+09 ..., 2.85486067e+06
2.67213149e+06 2.50887821e+06]
[ 3.21588187e+06 2.50852716e+09 5.78004279e+06 ..., 1.28553645e+04
1.03261725e+04 1.08347746e+04]
...,
[ 8.54242599e+03 2.85486067e+06 1.28553645e+04 ..., 1.45571578e+07
1.65903627e+06 1.23935750e+06]
[ 6.53264064e+03 2.67213149e+06 1.03261725e+04 ..., 1.65903627e+06
1.60956952e+06 3.82114514e+06]
[ 7.16207338e+03 2.50887821e+06 1.08347746e+04 ..., 1.23935750e+06
3.82114514e+06 1.13617169e+07]] [[ 1. -2. 1. ..., 0. 0. 0.]
[-2. 5. -4. ..., 0. 0. 0.]
[ 1. -4. 6. ..., 0. 0. 0.]
...,
[ 0. 0. 0. ..., 6. -4. 1.]
[ 0. 0. 0. ..., -4. 5. -2.]
[ 0. 0. 0. ..., 1. -2. 1.]]
tol, oldtol, lam -614.370185483 10000.0 7034364708.26
tol, oldtol, lam -1362.06063658 -614.370185483 703436470.826


npbins=33#the number of bins you want reconstructed
xcx, H [[ 3.13171429e+06 5.48188740e+08 2.94434886e+06 ..., 9.31103948e+03
6.50882364e+03 7.16207338e+03]
[ 5.48188740e+08 1.83350631e+11 4.62028859e+08 ..., 6.03807155e+05
5.61921563e+05 5.27430466e+05]
[ 2.94434886e+06 4.62028859e+08 6.10701296e+06 ..., 1.39729860e+04
1.05004753e+04 1.10192759e+04]
...,
[ 9.31103948e+03 6.03807155e+05 1.39729860e+04 ..., 2.84921305e+07
2.09765693e+06 1.32126317e+06]
[ 6.50882364e+03 5.61921563e+05 1.05004753e+04 ..., 2.09765693e+06
1.44747312e+06 3.55192372e+06]
[ 7.16207338e+03 5.27430466e+05 1.10192759e+04 ..., 1.32126317e+06
3.55192372e+06 1.13617169e+07]] [[ 1. -2. 1. ..., 0. 0. 0.]
[-2. 5. -4. ..., 0. 0. 0.]
[ 1. -4. 6. ..., 0. 0. 0.]
...,
[ 0. 0. 0. ..., 6. -4. 1.]
[ 0. 0. 0. ..., -4. 5. -2.]
[ 0. 0. 0. ..., 1. -2. 1.]]
tol, oldtol, lam -526.405870495 10000.0 105792018.303
tol, oldtol, lam -2835.34579103 -526.405870495 10579201.8303


npbins=32#the number of bins you want reconstructed
xcx, H [[ 3.13171429e+06 2.59951324e+08 2.72061542e+06 ..., 1.06978498e+04
6.48939550e+03 7.16207338e+03]
[ 2.59951324e+08 2.95912187e+10 1.93902954e+08 ..., 2.92261737e+05
2.68751485e+05 2.52339658e+05]
[ 2.72061542e+06 1.93902954e+08 6.97199118e+06 ..., 1.60082530e+04
1.08591768e+04 1.13700601e+04]
...,
[ 1.06978498e+04 2.92261737e+05 1.60082530e+04 ..., 7.24477450e+07
2.96193837e+06 1.51332458e+06]
[ 6.48939550e+03 2.68751485e+05 1.08591768e+04 ..., 2.96193837e+06
1.30789463e+06 3.29742742e+06]
[ 7.16207338e+03 2.52339658e+05 1.13700601e+04 ..., 1.51332458e+06
3.29742742e+06 1.13617169e+07]] [[ 1. -2. 1. ..., 0. 0. 0.]
[-2. 5. -4. ..., 0. 0. 0.]
[ 1. -4. 6. ..., 0. 0. 0.]
...,
[ 0. 0. 0. ..., 6. -4. 1.]
[ 0. 0. 0. ..., -4. 5. -2.]
[ 0. 0. 0. ..., 1. -2. 1.]]
tol, oldtol, lam -265.001162206 10000.0 22440685.2993
tol, oldtol, lam -1773.65119352 -265.001162206 2244068.52993


npbins=31#the number of bins you want reconstructed
xcx, H [[ 3.13171429e+06 1.50572173e+08 2.54674458e+06 2.46264677e+06
6.90992506e+05 4.68739039e+05 3.76782530e+05 1.81289080e+05
4.19032295e+05 9.67148400e+04 2.32968258e+05 6.19984119e+04
7.02150257e+04 4.55107424e+04 3.90251724e+04 3.90615663e+04
2.62059093e+04 5.31007628e+04 1.94317638e+04 3.97831212e+04
1.54393213e+04 1.73062231e+04 1.30986279e+04 1.19074885e+04
1.22975960e+04 9.22011511e+03 1.68570630e+04 7.56847304e+03
1.39009931e+04 6.47479552e+03 7.16207338e+03]
[ 1.50572173e+08 8.59569834e+09 9.99821208e+07 3.20296137e+07
1.33915938e+07 7.74794952e+06 5.07742813e+06 3.42666976e+06
2.89497255e+06 1.92894075e+06 1.72018995e+06 1.23813556e+06
1.04709334e+06 8.64366178e+05 7.37399264e+05 6.42348972e+05
5.50951781e+05 5.18394670e+05 4.28268501e+05 4.07197253e+05
3.42998454e+05 3.13391237e+05 2.81466989e+05 2.56508678e+05
2.36270285e+05 2.14791028e+05 2.07035080e+05 1.82787524e+05
1.76936890e+05 1.57614181e+05 1.48164288e+05]
[ 2.54674458e+06 9.99821208e+07 8.64653660e+06 2.68181175e+07
3.15989336e+06 1.63100514e+06 9.92598821e+05 4.66822719e+05
8.16071111e+05 2.16569905e+05 4.08781750e+05 1.27124463e+05
1.29644063e+05 8.68324101e+04 7.29504605e+04 6.87718533e+04
4.88350666e+04 8.08678405e+04 3.58087094e+04 5.96818598e+04
2.79474103e+04 2.91512826e+04 2.30663742e+04 2.08526491e+04
2.06140234e+04 1.63796036e+04 2.48186499e+04 1.34795449e+04
2.04312046e+04 1.14642267e+04 1.19427967e+04]
[ 2.46264677e+06 3.20296137e+07 2.68181175e+07 1.06608697e+08
1.00395697e+07 4.73123880e+06 2.35440535e+06 1.14354793e+06
1.45280216e+06 5.01789231e+05 6.92025810e+05 2.83914723e+05
2.59395054e+05 1.86353533e+05 1.54929835e+05 1.38348341e+05
1.06216226e+05 1.35804117e+05 7.84341221e+04 1.00690933e+05
6.09484083e+04 5.90139962e+04 4.94405448e+04 4.46197596e+04
4.22889381e+04 3.58719201e+04 4.34970135e+04 2.97915526e+04
3.61046592e+04 2.53328791e+04 2.48927365e+04]
[ 6.90992506e+05 1.33915938e+07 3.15989336e+06 1.00395697e+07
1.72047484e+07 1.54295293e+07 3.90653713e+06 1.44414227e+06
1.89249751e+06 4.79155653e+05 7.68389146e+05 2.38311612e+05
2.25310157e+05 1.46857198e+05 1.19404219e+05 1.07581880e+05
7.63734059e+04 1.15946866e+05 5.40229606e+04 8.30317223e+04
4.09149155e+04 4.13588484e+04 3.28545636e+04 2.94242413e+04
2.84882889e+04 2.28781134e+04 3.24668675e+04 1.86168004e+04
2.64562272e+04 1.56491436e+04 1.59611776e+04]
[ 4.68739039e+05 7.74794952e+06 1.63100514e+06 4.73123880e+06
1.54295293e+07 1.44085517e+07 6.73393034e+06 2.26102402e+06
2.98919486e+06 6.01994598e+05 1.02993481e+06 2.74939747e+05
2.62320430e+05 1.62550330e+05 1.30534833e+05 1.16984545e+05
8.06636768e+04 1.27721818e+05 5.58718526e+04 8.96969396e+04
4.17595635e+04 4.26106921e+04 3.32836536e+04 2.97002613e+04
2.88397402e+04 2.28162814e+04 3.34781506e+04 1.84210186e+04
2.70732099e+04 1.54056524e+04 1.58333156e+04]
[ 3.76782530e+05 5.07742813e+06 9.92598821e+05 2.35440535e+06
3.90653713e+06 6.73393034e+06 5.61496285e+07 1.38595018e+07
7.65257738e+06 1.57055613e+06 1.88824069e+06 5.63668547e+05
4.72260574e+05 2.93825138e+05 2.27047476e+05 1.92099464e+05
1.35936646e+05 1.83144089e+05 9.15872947e+04 1.26047429e+05
6.67238329e+04 6.47604601e+04 5.17643611e+04 4.58139713e+04
4.32250494e+04 3.52014420e+04 4.58154179e+04 2.82711405e+04
3.69074514e+04 2.34425529e+04 2.32892533e+04]
[ 1.81289080e+05 3.42666976e+06 4.66822719e+05 1.14354793e+06
1.44414227e+06 2.26102402e+06 1.38595018e+07 4.49490668e+06
1.81226525e+07 1.55399046e+06 2.58440364e+06 4.60662572e+05
4.25204813e+05 2.25414325e+05 1.71848731e+05 1.48400631e+05
9.42169070e+04 1.62295299e+05 6.05175495e+04 1.07525167e+05
4.30738753e+04 4.49359400e+04 3.33486658e+04 2.93687752e+04
2.87040059e+04 2.16634867e+04 3.50350435e+04 1.70142258e+04
2.76997545e+04 1.39701208e+04 1.47202297e+04]
[ 4.19032295e+05 2.89497255e+06 8.16071111e+05 1.45280216e+06
1.89249751e+06 2.98919486e+06 7.65257738e+06 1.81226525e+07
1.13722832e+09 2.53435918e+07 1.07220004e+07 2.96815038e+06
1.87930746e+06 1.09536260e+06 7.69582177e+05 5.85274086e+05
4.18888884e+05 4.36290258e+05 2.64246186e+05 2.85694342e+05
1.82822485e+05 1.64741412e+05 1.35248530e+05 1.17835883e+05
1.06516325e+05 8.99064140e+04 9.75135360e+04 7.13159860e+04
7.78682761e+04 5.82331523e+04 5.51651220e+04]
[ 9.67148400e+04 1.92894075e+06 2.16569905e+05 5.01789231e+05
4.79155653e+05 6.01994598e+05 1.57055613e+06 1.55399046e+06
2.53435918e+07 2.69616112e+06 2.08182884e+07 1.31322905e+06
1.04558058e+06 4.30141629e+05 3.00227103e+05 2.37245855e+05
1.39178156e+05 2.40364726e+05 8.09152616e+04 1.47556990e+05
5.39851193e+04 5.61556154e+04 4.00455972e+04 3.46973819e+04
3.36290601e+04 2.46211455e+04 4.12565921e+04 1.87987624e+04
3.18757723e+04 1.51186043e+04 1.60503008e+04]
[ 2.32968258e+05 1.72018995e+06 4.08781750e+05 6.92025810e+05
7.68389146e+05 1.02993481e+06 1.88824069e+06 2.58440364e+06
1.07220004e+07 2.08182884e+07 9.09419201e+08 1.42689320e+07
5.45538347e+06 2.23579990e+06 1.34070765e+06 9.07998349e+05
5.90386188e+05 6.10300058e+05 3.31471161e+05 3.64265834e+05
2.13191840e+05 1.89505534e+05 1.50244812e+05 1.28444050e+05
1.14742546e+05 9.44625697e+04 1.05026034e+05 7.29253880e+04
8.18183684e+04 5.83332081e+04 5.52573248e+04]
[ 6.19984119e+04 1.23813556e+06 1.27124463e+05 2.83914723e+05
2.38311612e+05 2.74939747e+05 5.63668547e+05 4.60662572e+05
2.96815038e+06 1.31322905e+06 1.42689320e+07 3.03298399e+06
7.68552042e+06 1.31970810e+06 7.54238605e+05 4.88171121e+05
2.56662504e+05 4.15277156e+05 1.27999679e+05 2.27296760e+05
7.78450463e+04 7.90344695e+04 5.42967150e+04 4.60501095e+04
4.37010614e+04 3.13338959e+04 5.22343299e+04 2.31809938e+04
3.93080885e+04 1.81932946e+04 1.92044302e+04]
[ 7.02150257e+04 1.04709334e+06 1.29644063e+05 2.59395054e+05
2.25310157e+05 2.62320430e+05 4.72260574e+05 4.25204813e+05
1.87930746e+06 1.04558058e+06 5.45538347e+06 7.68552042e+06
2.83672773e+07 3.37212435e+06 1.78626139e+06 9.60090837e+05
4.88936918e+05 6.59949180e+05 2.23786067e+05 3.36015632e+05
1.29251491e+05 1.22795814e+05 8.64072061e+04 7.23195050e+04
6.61307335e+04 4.91843128e+04 7.11023981e+04 3.61857524e+04
5.30726971e+04 2.81307933e+04 2.83422595e+04]
[ 4.55107424e+04 8.64366178e+05 8.68324101e+04 1.86353533e+05
1.46857198e+05 1.62550330e+05 2.93825138e+05 2.25414325e+05
1.09536260e+06 4.30141629e+05 2.23579990e+06 1.31970810e+06
3.37212435e+06 5.95867527e+06 5.62920574e+06 1.65107429e+06
6.79812260e+05 9.14135010e+05 2.51365667e+05 4.08378836e+05
1.31600222e+05 1.26925453e+05 8.36565842e+04 6.88349463e+04
6.30258223e+04 4.45270898e+04 7.10886558e+04 3.17142837e+04
5.18003494e+04 2.41557388e+04 2.50252630e+04]
[ 3.90251724e+04 7.37399264e+05 7.29504605e+04 1.54929835e+05
1.19404219e+05 1.30534833e+05 2.27047476e+05 1.71848731e+05
7.69582177e+05 3.00227103e+05 1.34070765e+06 7.54238605e+05
1.78626139e+06 5.62920574e+06 5.54249723e+06 2.86165212e+06
1.07563980e+06 1.45283346e+06 3.27853825e+05 5.55786698e+05
1.58821315e+05 1.53242537e+05 9.68657215e+04 7.86956525e+04
7.13868745e+04 4.93205082e+04 8.04446199e+04 3.44127399e+04
5.75376409e+04 2.58454036e+04 2.68776529e+04]
[ 3.90615663e+04 6.42348972e+05 6.87718533e+04 1.38348341e+05
1.07581880e+05 1.16984545e+05 1.92099464e+05 1.48400631e+05
5.85274086e+05 2.37245855e+05 9.07998349e+05 4.88171121e+05
9.60090837e+05 1.65107429e+06 2.86165212e+06 2.27164446e+07
5.90247498e+06 3.55053865e+06 7.60005829e+05 9.72277025e+05
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tol, oldtol, lam -240.699533341 10000.0 7249448.02874
tol, oldtol, lam -1258.89616312 -240.699533341 724944.802874

New Threshold Results

I've calculated the (new) thresholds using the method outlined in this post.

Below are the lratio new thresholds for the old version of the likelihood (v1) and the likelihood with ALL the Quasars as a function of targets per square degree (TPSD):

TPSD ----- v1 threshold - all quasar threshold
10.0000 -----0.749085 --------- 0.770306
20.0000 -----0.490932 --------- 0.571854
30.0000 -----0.347334 --------- 0.425242
40.0000 -----0.253020 --------- 0.329808
50.0000 -----0.198630 --------- 0.264506
60.0000 -----0.160074 --------- 0.214092
70.0000 -----0.128912 --------- 0.182231
80.0000 -----0.107913 --------- 0.152691

Here's a plot
Cyan is the ALL the Quasars
Green is likelihood v1

Using these thresholds here are the number of quasars targeted:
TPSD ---- #QSOs (v1) --- #QSOs (ALL Quasars)
10.0000 ---443.000 -------- 406.000
20.0000 ---656.000 -------- 582.000
30.0000 ---781.000 -------- 722.000
40.0000 ---897.000 -------- 830.000
50.0000 ---965.000 -------- 892.000
60.0000 ---1035.00 -------- 955.000
70.0000 ---1089.00 -------- 1000.00
80.0000 ---1123.00 -------- 1037.00

Here's a plot
Cyan is the ALL the Quasars
Green is likelihood v1

Looks like ALL the quasars doesn't do better after all with the non-Milky Way thresholds. Boo.
The log file to run this code is here: ../logs100520log.pro

New (non Milky Way) Thresholds

Following Adam's suggestions I've made a new file to use for setting the thresholds (that is in a different RA region of the sky). It is objects in Erin's median seeing file (../boss/boss-qso-stripe82median.fits) that are in the following ra/dec ranges:

(-1 < dec ≤ 1) and
(0 < ra ≤ 1) or (10 < ra ≤ 11) or (20 < ra ≤ 21) or
(30 < ra ≤ 1) or (40 < ra ≤ 41) or (359 ≤ ra < 360)

The area of this file is 2 (degrees in dec) × 6 (degrees in ra) = 12 deg².
The number of targets in this file 27,794 (much less than before)

Ra/Dec of Threshold Targets

The log file for this run is here: ../logs/100519log.pro
The run directory is here: ../likelihood/likeAllNew2
Results to follow....

Adam's Thoughts...

Adam Myers got back to me about the differences in our thresholds. He thinks that my problem is that I am setting the thresholds with objects that are in the stellar locus, so this is making my thresholds higher than they should be (from 5/19 email re: Testing Likelihood):

~~~~~~

....my guess is that your thresholds are so high because the area between RA = 320 and 321 degrees has one of the largest gradients in stellar density of anywhere on the sky (see the attached quick-plot).


If you compare you threshold in this range to the other RA strips you should find that it is grossly deviant

e.g., my numbers are likelihood v2:

0 < RA < 1....threshold at 20 fibers per sq. deg. = 0.468797
320 < RA < 321....threshold at 20 fibers per sq. deg. = 0.999961

In fact, 320 < RA < 321 dominates the counts to the extent that you don't even really *see* the other strips in RA in your test. Taking the first 240 objects over your 12 degrees of strips (i.e. targeting at 20 per sq. deg.) I find that 90% (215 out of 240) of them come from the 320 < RA < 321 strip. So, that's your problem there. This is why I set thresholds over large representative areas, preferably outside of stripe 82. Although, I think you'd be safe to continue doing what you're doing if you stick to, say strips in the range 350 < RA < 60.

~~~~~~

So it looks like I need to try again, setting the thresholds with a different sky region (away from Milky Way). Results to follow.

ALL the Quasars Results

I've calculated the thresholds using the method outlined in this post.

Below are the lratio thresholds for the old version of the likelihood (v1) and the likelihood with ALL the Quasars as a function of targets per square degree (TPSD):

TPSD ----- v1 threshold - all quasar threshold
10.0000 ----- 0.996883 ----- 0.984691
20.0000 ----- 0.868663 ----- 0.795943
30.0000 ----- 0.674674 ----- 0.629976
40.0000 ----- 0.496851 ----- 0.522806
50.0000 ----- 0.403819 ----- 0.441149
60.0000 ----- 0.318171 ----- 0.363089
70.0000 ----- 0.262071 ----- 0.310237
80.0000 ----- 0.215056 ----- 0.268664

Here's a plot
Cyan is the ALL the Quasars
Green is likelihood v1

Using these thresholds here are the number of quasars targeted:
TPSD ---- #QSOs (v1) --- #QSOs (ALL quasars)
10.0000 -- 132.000 ----- 155.000
20.0000 -- 343.000 ----- 374.000
30.0000 -- 484.000 ----- 527.000
40.0000 -- 651.000 ----- 638.000
50.0000 -- 731.000 ----- 713.000
60.0000 -- 815.000 ----- 793.000
70.0000 -- 888.000 ----- 853.000
80.0000 -- 944.000 ----- 889.000

Here's a plot
Cyan is the ALL the Quasars
Green is likelihood v1

Next, I'll look at the difference between these QSO populations, but first I want Adam/Erin to confirm that I am doing things correctly.

The code to do this is in the following log file: ../logs/100517log.pro

ALL the Quasars

Recently Dinosaur Comics has been emphasizing ALL of everything. ALL the secrets, ALL the balls, ALL the burgers. So today we are going to creating the QSO catalog with ALL the Quasars.

Before we had a brightness limit and some other cuts, but Hennawi wanted me to see what it looked like with all of them.

I did this by modifying: qso_fake_jess.pro with the following call to qso_photosamp:
qsos = qso_photosamp(Z_MIN = Z_MIN, Z_MAX = Z_MAX, NOCUTS = NOCUTS, ILIM = 23.0)

Here is what these QSOs look like in color-color space:

And re-running hiz_kde_numerator_jess.pro:
.run hiz_kde_numerator_jess.pro

To create a new QSO Catalog:
../likelihood/qsocatalog/QSOCatalog-Wed-May-12-14:03:41-2010.fits

Here is what the QSO Catalog looks like in color-color space:

Change likelihood_compute to read in this catalog:
; Read in the QSO file
file2 = '/home/jessica/repository/ccpzcalib/Jessica/likelihood/qsocatalog/QSOCatalog-Wed-May-12-14:03:41-2010.fits'

This run is in the directory:
../likelihood/likev1all

The log file is here:
../logs/100512log.pro

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On a side note, I looked at Google Analytics for this blog today. It turns out that over it's lifetime 355 Absolute Unique vistors have viewed my blog! These visitors are from 10 countries and 28 different states. Pretty cool! Yet very few people leave comments on my blog. Everyone reading
this, leave a comment so I know you are out there!

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Is anyone else having problem's editing their blogspot blog using Google Chrome? It totally messes up the fonts and when I look at the html it is all funky. Seem strange considering google owns both Chrome and blogsplot! Maybe it is their way of punishing Mac users for the iPhone being better than the Google phone.

Likelihood Threshold Results

I've calculated the thresholds using the method outlined in yesterday's post. Calculating the likelihoods on the 43,134 targets in this 12 deg² region took ~30 minutes (so that is quick).

Below are the thresholds for the old version of the likelihood (v1) in parentheses are the thresholds Adam Myers got:

Targets/deg²---------Threshold
10----------------0.995851
20----------------0.858535 (0.533)
30----------------0.664424
40----------------0.490932 (0.235)
50----------------0.400611
60----------------0.311577
70----------------0.256615
80----------------0.213374

So my thresholds still don't match his.

Here is a plot of deg² vs threshold:

In terms of recovered QSOs with these thresholds:
Targets/deg²---Threshold-----Recovered QSOs (out of 1625)
10.0000 --- 0.995851 ------- 135 (8.3%)
20.0000 --- 0.858535 ------- 346 (21.3%)
30.0000 --- 0.664424 ------- 489 (30.1%)
40.0000 --- 0.490932 ------- 651 (40.1%)
50.0000 --- 0.400611 ------- 727 (44.7%)
60.0000 --- 0.311577 ------- 816 (50.2%)
70.0000 --- 0.256615 ------- 886 (54.5%)
80.0000 --- 0.213374 ------- 942 (58.0%)

I've saved these likelihoods in the directory: ../likelihood/likev1/
The they are also here:
likelihoodv1thresholds.fits
qsolikelihoodv1thresholds.fits
The log file is here: ../logs/100511log.pro

Now to make some magic happen...

Setting Thresholds

I've made a file to use for setting the thresholds. It is objects in Erin's median seeing file (../boss/boss-qso-stripe82median.fits) that are in the following ra/dec ranges:

(-1 < dec ≤ 1) and
(0 < ra ≤ 1) or (20 < ra ≤ 21) or (40 < ra ≤ 41) or
(320 < ra ≤ 321) or (340 < ra ≤ 341) or (359 ≤ ra < 360)

The area of this file is 2 (degrees in dec) × 6 (degrees in ra) = 12 deg².
The number of targets in this file 43,134.

Ra/Dec of Threshold Targets

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I modified the likelihood.script to take as an argument the directory to store the likefile*.fits. So now when you run the likelihood.script you do the following:

./likelihood.script thisrundirectory

This will stores all the likefile*.fits and qsub*.out files in the directory ../likelihood/thisrundirectory

If you don't give the likelihood.script a directory argument it will write in the current directory.

I have found a problem that not all the Stripe-82 quasars in the 3pc catalog are in Erin's median seeing targets catalog. I've pinged him about this. We'll see if he has an idea.

In the mean time, I am determining the thresholds for the old version of the likelihood (v1) on my set of threshold targets above. I'm also re-calculating the likelihoods on the 3pc quasars which I have median seeing for to make sure I get the same numbers again.

The code for this is here: ../logs/100510log.pro
The run directory is here: ../likelihood/likev1

Hennawi Visit Do To List

Joe Hennawi came to visit today and thus we have a huge list of things to do now.

1. I've been setting the threshold incorrectly for testing the likelihoods. I need to do the following. This should actually speed things up a bunch because I'll need to calculate the likelihoods on a lot fewer objects.
-----a) Pick a set of targets (Threshold Set) in a known area from the test objects, say -1.25 < dec < 1.25 and 1 degree in ra spaced across the stripe (-20, -10, 0, 10, 20). The Threshold Set of targets occupies 5*2.5 = 12.5 deg² of area.
-----b) Calculate the (new) likelihoods on the Threshold Set.
-----c) Determine a threshold (TH) of (X/deg²) by picking the objects with the top Y = X*12.5 likelihoods in the Threshold Set. The threshold will be the min(likelihood_ratio) of these Y objects.
-----d) Calculate the (new) likelihoods on all objects in the 3pc catalog that are on Stripe-82 (Truth Table Set).
-----e) We targeted at 80 targets/deg² in this region, so we can get the effective area (A) of the Truth Table Set by dividing the # of objects (Z) / 80: A (deg²) of Truth Table Set = Z/(80 targets/deg²)
-----f) Determine the (new) targeted quasars (TQ) by saying that any quasar in the Truth Table Set with likelihood greater than TH.
-----g) The QSOs /deg² = TQ / A.

2. Joe fixed his luminosity function code, so I can now run it to generate the Hopkins, Richards, Hernquist (2007) luminosity function.

3. Joe thinks we don't need to put a brightness cut on the quasar inputs. Gonna see if that changes things

4. David gave me SDSS DR7 Quasars to use as inputs into the Monte Carlo.

5. I should look at something in the target files called x2_star. This is the chi-squared distance of the objects from the stellar locus, and probably all likelihood targets have a x2_star which is big. We might be able to reduce the likelihood calculations by only calculating likelihoods on objects which pass a certain x2_star threshold.

Duplicates: The Bane of My Existance!

I am about ready to smash my fist through a wall. It turns out there were also duplicates in the quasar file.... here are the numbers/plots from yesterday with the quasar duplicates and target duplicates removed. Here's a cute piece of code to remove them (from Adam):

a = mdrfits('knownQSO+BOSS.fits',1) spherematch, a.ra, a.dec, a.ra, a.dec, 2./3600, m1, m2, maxmatch=0 dups = m1[where(m1 gt m2)] good = [indgen(n_elements(a))*0]+1 good[dups]=0 dupsremoved = a[where(good)] mwrfits,dupsremoved,'knownQSO+BOSS.nodup.fits',/create
And also code from Erin for removing BOSS duplicates:
targetplatefile = './targetallfile.fits'
targets= mrdfits(targetplatefile, 1)
pid = photoid(targets)
targets = targets[rem_dup(pid)]

Below are my new numbers (as compared to Adam's table) with a QSO redshift range 2.2 < z < 3.5:

                  Threshold         # QSOs per deg^2
              20/deg^2  40/deg^2   20/deg^2  40/deg^2
Likelihood v1  0.7623   0.46035      7.14      10.85
Likelihood v2  0.2433   0.12765      8.09      10.64

If I use Myers' thresholds I get the following:

                  Threshold         # QSOs per deg^2
             20/deg^2  40/deg^2   20/deg^2  40/deg^2
Likelihood v1  0.533     0.235       5.93     6.96
Likelihood v2  0.200     0.071       7.30     6.86

Adam get's the following numbers:

                  Threshold         # QSOs per deg^2
             20/deg^2  40/deg^2   20/deg^2  40/deg^2
Likelihood v1  0.533     0.235       8.81    12.23
Likelihood v2  0.200     0.071       8.25    11.18

I suspect that Adam might be calculating the # per deg^2 taking the number of QSOs and dividing by 20/40. This doesn't seem right to me, because I am getting significantly different numbers of target fibers using these thresholds for the v1 vs v2 likelihoods (2848 for v1, 2060 for v2) and so simply dividing by 20/40 favors the old likelihood because we are giving more fibers to that version.

This is why I use:
# QSOs per deg^2 = # targeted QSOs / (total # targets / # per square degree)

There is also a question of changes in the distribution of likelihood ratio for the v1 vs v2. We would expect very different values for likelihood ratio because we are now modeling low/high redshift QSOs and adding them to the denominator, I am not sure if comparing these values tells us much. I think a more significant test is looking at the distribution of the redshift of the QSOs for v1 vs v2. We do seem to be targeting more low QSOs with the v2. This could possibly be corrected by implementing the McDonald score. I have the code to do this. Hennawi seems to think that the Richard's luminosity function doesn't work well at high redshifts. Now that I have removed the duplicates I need to re-test all the different luminosity functions again anyway. I can do this with and without the McDonald score for each luminosity function.

Below are plots (same as yesterday's post) + the likelihood ratio distributions. Again, white is v1 (old) and green is v2 (new):

Myers Likelihood Comparisons

Adam Myers got significantly different results when he tested the new likelihood. After some debugging, we realized that I was duplicating some targets in my targetallfile.fits file which was effecting my results. I've now removed all duplicates and re-ran the Likelihood Test (4) (see log file ../logs/100505log.pro for code). Below are my new numbers (as compared to Adam's table) with a QSO redshift range 2.2 < z < 3.5:

                  Threshold         # QSOs per deg^2
             20/deg^2  40/deg^2   20/deg^2  40/deg^2
Likelihood v1  0.7623   0.46035      6.50      9.77
Likelihood v2  0.2433   0.12765      7.14      9.31

This shows an improvements at 20/deg^2 but not at 40/deg^2.

The puzzling thing is that even when I remove the duplicates I am still getting dramatically different thresholds compared with Myers. If I use Myers' thresholds I get the following:

                  Threshold         # QSOs per deg^2
             20/deg^2  40/deg^2   20/deg^2  40/deg^2
Likelihood v1  0.533     0.235       5.38     3.084
Likelihood v2  0.200     0.071       6.45     3.016

I get the number per square degree by taking the total # of targeted QSOs, and then dividing by the total number of targets and then multiplying by the number of targets per square degree:

# targeted QSOs / (total # targets / # per square degree)

I do this because using Myers' thresholds gives us different number of targets for v1 and v2 so this seems like the best way to directly compare the numbers.

Below are a bunch of plots of the redshift distributions of the quasars for the above thresholds. The white is likelihood v1 (old) and green is likelihood v2 (new). Targeted QSOs are all QSOs targeted by the two methods, unique QSOs are QSOs only targeted by one method or the other: