diff --git a/NanoGardener/python/framework/samples/samplesCrossSections2016.py b/NanoGardener/python/framework/samples/samplesCrossSections2016.py index 3bc9cd07c..2751f5064 100644 --- a/NanoGardener/python/framework/samples/samplesCrossSections2016.py +++ b/NanoGardener/python/framework/samples/samplesCrossSections2016.py @@ -314,48 +314,48 @@ samples['GluGluHToWWTo2L2Nu_M125_CUETUp'] .extend( ['xsec=0.9913', 'kfact=1.000', 'ref=CF'] ) # 43.92*0.215*0.108*0.108*9 Higgs LHC value samples['GluGluHToWWTo2L2Nu_M125_CUETDown'].extend( ['xsec=0.9913', 'kfact=1.000', 'ref=CF'] ) # 43.92*0.215*0.108*0.108*9 Higgs LHC value -samples['GluGluHToWWToLNuQQ_M115'] .extend( ['xsec=1.0018', 'kfact=1.000', 'ref=Y'] ) # 53.10*0.0859*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M120'] .extend( ['xsec=1.5143', 'kfact=1.000', 'ref=Y'] ) # 48.90*0.141*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M124'] .extend( ['xsec=2.0065', 'kfact=1.000', 'ref=Y'] ) # 45.91*0.199*0.1086*3*0.6741 ### Interpolated Xsec -samples['GluGluHToWWToLNuQQ_M125'] .extend( ['xsec=2.1343', 'kfact=1.000', 'ref=Y'] ) # 45.20*0.215*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M126'] .extend( ['xsec=2.2571', 'kfact=1.000', 'ref=Y'] ) # 44.49*0.231*0.1086*3*0.6741 ### Interpolated Xsec -samples['GluGluHToWWToLNuQQ_M130'] .extend( ['xsec=2.7816', 'kfact=1.000', 'ref=Y'] ) # 41.80*0.303*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M135'] .extend( ['xsec=3.4085', 'kfact=1.000', 'ref=Y'] ) # 38.80*0.400*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M140'] .extend( ['xsec=3.9611', 'kfact=1.000', 'ref=Y'] ) # 36.00*0.501*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M145'] .extend( ['xsec=4.4144', 'kfact=1.000', 'ref=Y'] ) # 33.50*0.600*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M150'] .extend( ['xsec=4.7829', 'kfact=1.000', 'ref=Y'] ) # 31.29*0.696*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M155'] .extend( ['xsec=5.1070', 'kfact=1.000', 'ref=Y'] ) # 29.25*0.795*0.1086*3*0.6741 ### Interpolated Xsec -samples['GluGluHToWWToLNuQQ_M160'] .extend( ['xsec=5.5480', 'kfact=1.000', 'ref=Y'] ) # 27.37*0.908*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M165'] .extend( ['xsec=5.4101', 'kfact=1.000', 'ref=Y'] ) # 25.66*0.960*0.1086*3*0.6741 ### Interpolated Xsec -samples['GluGluHToWWToLNuQQ_M170'] .extend( ['xsec=5.1002', 'kfact=1.000', 'ref=Y'] ) # 24.09*0.964*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M175'] .extend( ['xsec=4.7655', 'kfact=1.000', 'ref=Y'] ) # 22.65*0.958*0.1086*3*0.6741 ### Interpolated Xsec -samples['GluGluHToWWToLNuQQ_M180'] .extend( ['xsec=4.3639', 'kfact=1.000', 'ref=Y'] ) # 21.32*0.932*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M190'] .extend( ['xsec=3.2729', 'kfact=1.000', 'ref=Y'] ) # 18.96*0.786*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M200'] .extend( ['xsec=2.7568', 'kfact=1.000', 'ref=Y'] ) # 16.94*0.741*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M210'] .extend( ['xsec=2.4136', 'kfact=1.000', 'ref=Y'] ) # 15.20*0.723*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M230'] .extend( ['xsec=1.9234', 'kfact=1.000', 'ref=Y'] ) # 12.37*0.708*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M250'] .extend( ['xsec=1.5703', 'kfact=1.000', 'ref=Y'] ) # 10.20*0.701*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M270'] .extend( ['xsec=1.3027', 'kfact=1.000', 'ref=Y'] ) # 8.510*0.697*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M300'] .extend( ['xsec=1.0015', 'kfact=1.000', 'ref=Y'] ) # 6.590*0.692*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M350'] .extend( ['xsec=0.6651', 'kfact=1.000', 'ref=Y'] ) # 4.480*0.676*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M400'] .extend( ['xsec=0.4039', 'kfact=1.000', 'ref=Y'] ) # 3.160*0.582*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M450'] .extend( ['xsec=0.2783', 'kfact=1.000', 'ref=Y'] ) # 2.300*0.551*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M500'] .extend( ['xsec=0.2049', 'kfact=1.000', 'ref=Y'] ) # 1.709*0.546*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M550'] .extend( ['xsec=0.1567', 'kfact=1.000', 'ref=Y'] ) # 1.297*0.550*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M600'] .extend( ['xsec=0.1227', 'kfact=1.000', 'ref=Y'] ) # 1.001*0.558*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M650'] .extend( ['xsec=0.0977', 'kfact=1.000', 'ref=Y'] ) # 0.7834*0.568*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M700'] .extend( ['xsec=0.0786', 'kfact=1.000', 'ref=Y'] ) # 0.6206*0.577*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M750'] .extend( ['xsec=0.0639', 'kfact=1.000', 'ref=Y'] ) # 0.4969*0.586*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M750_NWA'] .extend( ['xsec=0.0589', 'kfact=1.000', 'ref=KF'] ) # 0.460*0.586*0.1086*3*0.6741 - shall we keep the same BR for NWA? -samples['GluGluHToWWToLNuQQ_M800'] .extend( ['xsec=0.0524', 'kfact=1.000', 'ref=Y'] ) # 0.4015*0.594*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M900'] .extend( ['xsec=0.0359', 'kfact=1.000', 'ref=Y'] ) # 0.2685*0.609*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M1000'] .extend( ['xsec=0.0252', 'kfact=1.000', 'ref=Y'] ) # 0.1845*0.621*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M1500'] .extend( ['xsec=0.00503', 'kfact=1.000', 'ref=Y'] ) # 0.0369*0.621*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M2000'] .extend( ['xsec=0.00131', 'kfact=1.000', 'ref=Y'] ) # 0.00960*0.621*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M2500'] .extend( ['xsec=0.000394', 'kfact=1.000', 'ref=Y'] ) # 0.00289*0.621*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M3000'] .extend( ['xsec=0.000128', 'kfact=1.000', 'ref=Y'] ) # 0.00094*0.621*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M4000'] .extend( ['xsec=0.0000115', 'kfact=1.000', 'ref=Y'] ) # 0.000114*0.621*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M5000'] .extend( ['xsec=0.0000019', 'kfact=1.000', 'ref=Y'] ) # 0.000014*0.621*0.1086*3*0.6741 +samples['GluGluHToWWToLNuQQ_M115'] .extend( ['xsec=2.00352', 'kfact=1.000', 'ref=Y'] ) # 53.10*0.0859*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M120'] .extend( ['xsec=3.02854', 'kfact=1.000', 'ref=Y'] ) # 48.90*0.141*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M124'] .extend( ['xsec=4.01297', 'kfact=1.000', 'ref=Y'] ) # 45.91*0.199*0.1086*3*0.6741*2 ### Interpolated Xsec +samples['GluGluHToWWToLNuQQ_M125'] .extend( ['xsec=4.26857', 'kfact=1.000', 'ref=Y'] ) # 45.20*0.215*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M126'] .extend( ['xsec=4.51419', 'kfact=1.000', 'ref=Y'] ) # 44.49*0.231*0.1086*3*0.6741*2 ### Interpolated Xsec +samples['GluGluHToWWToLNuQQ_M130'] .extend( ['xsec=5.56320', 'kfact=1.000', 'ref=Y'] ) # 41.80*0.303*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M135'] .extend( ['xsec=6.81706', 'kfact=1.000', 'ref=Y'] ) # 38.80*0.400*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M140'] .extend( ['xsec=7.92220', 'kfact=1.000', 'ref=Y'] ) # 36.00*0.501*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M145'] .extend( ['xsec=8.82880', 'kfact=1.000', 'ref=Y'] ) # 33.50*0.600*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M150'] .extend( ['xsec=9.56578', 'kfact=1.000', 'ref=Y'] ) # 31.29*0.696*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M155'] .extend( ['xsec=10.21406', 'kfact=1.000', 'ref=Y'] ) # 29.25*0.795*0.1086*3*0.6741*2 ### Interpolated Xsec +samples['GluGluHToWWToLNuQQ_M160'] .extend( ['xsec=10.91606', 'kfact=1.000', 'ref=Y'] ) # 27.37*0.908*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M165'] .extend( ['xsec=10.82015', 'kfact=1.000', 'ref=Y'] ) # 25.66*0.960*0.1086*3*0.6741*2 ### Interpolated Xsec +samples['GluGluHToWWToLNuQQ_M170'] .extend( ['xsec=10.20045', 'kfact=1.000', 'ref=Y'] ) # 24.09*0.964*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M175'] .extend( ['xsec=9.53101', 'kfact=1.000', 'ref=Y'] ) # 22.65*0.958*0.1086*3*0.6741*2 ### Interpolated Xsec +samples['GluGluHToWWToLNuQQ_M180'] .extend( ['xsec=8.72787', 'kfact=1.000', 'ref=Y'] ) # 21.32*0.932*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M190'] .extend( ['xsec=6.54585', 'kfact=1.000', 'ref=Y'] ) # 18.96*0.786*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M200'] .extend( ['xsec=5.51362', 'kfact=1.000', 'ref=Y'] ) # 16.94*0.741*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M210'] .extend( ['xsec=4.82711', 'kfact=1.000', 'ref=Y'] ) # 15.20*0.723*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M230'] .extend( ['xsec=3.84688', 'kfact=1.000', 'ref=Y'] ) # 12.37*0.708*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M250'] .extend( ['xsec=3.14068', 'kfact=1.000', 'ref=Y'] ) # 10.20*0.701*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M270'] .extend( ['xsec=2.60536', 'kfact=1.000', 'ref=Y'] ) # 8.510*0.697*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M300'] .extend( ['xsec=2.00307', 'kfact=1.000', 'ref=Y'] ) # 6.590*0.692*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M350'] .extend( ['xsec=1.33024', 'kfact=1.000', 'ref=Y'] ) # 4.480*0.676*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M400'] .extend( ['xsec=0.80782', 'kfact=1.000', 'ref=Y'] ) # 3.160*0.582*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M450'] .extend( ['xsec=0.55665', 'kfact=1.000', 'ref=Y'] ) # 2.300*0.551*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M500'] .extend( ['xsec=0.40986', 'kfact=1.000', 'ref=Y'] ) # 1.709*0.546*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M550'] .extend( ['xsec=0.31333', 'kfact=1.000', 'ref=Y'] ) # 1.297*0.550*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M600'] .extend( ['xsec=0.24534', 'kfact=1.000', 'ref=Y'] ) # 1.001*0.558*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M650'] .extend( ['xsec=0.19545', 'kfact=1.000', 'ref=Y'] ) # 0.7834*0.568*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M700'] .extend( ['xsec=0.15729', 'kfact=1.000', 'ref=Y'] ) # 0.6206*0.577*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M750'] .extend( ['xsec=0.12790', 'kfact=1.000', 'ref=Y'] ) # 0.4969*0.586*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M750_NWA'] .extend( ['xsec=0.11840', 'kfact=1.000', 'ref=KF'] ) # 0.460*0.586*0.1086*3*0.6741*2 - shall we keep the same BR for NWA? +samples['GluGluHToWWToLNuQQ_M800'] .extend( ['xsec=0.10476', 'kfact=1.000', 'ref=Y'] ) # 0.4015*0.594*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M900'] .extend( ['xsec=0.07182', 'kfact=1.000', 'ref=Y'] ) # 0.2685*0.609*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M1000'] .extend( ['xsec=0.05033', 'kfact=1.000', 'ref=Y'] ) # 0.1845*0.621*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M1500'] .extend( ['xsec=0.01007', 'kfact=1.000', 'ref=Y'] ) # 0.0369*0.621*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M2000'] .extend( ['xsec=0.00262', 'kfact=1.000', 'ref=Y'] ) # 0.00960*0.621*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M2500'] .extend( ['xsec=0.00078831', 'kfact=1.000', 'ref=Y'] ) # 0.00289*0.621*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M3000'] .extend( ['xsec=0.00025640', 'kfact=1.000', 'ref=Y'] ) # 0.00094*0.621*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M4000'] .extend( ['xsec=0.00003110', 'kfact=1.000', 'ref=Y'] ) # 0.000114*0.621*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M5000'] .extend( ['xsec=0.00000382', 'kfact=1.000', 'ref=Y'] ) # 0.000014*0.621*0.1086*3*0.6741*2 samples['GluGluHToZZTo4L_M125'] .extend( ['xsec=0.0118', 'kfact=1.000', 'ref=CF'] ) # 43.92*0.0264*0.033658*0.033658*9 samples['GluGluHToTauTau_M120'] .extend( ['xsec=2.2676', 'kfact=1.000', 'ref=KF'] ) # 32.21*0.0704 @@ -509,46 +509,46 @@ samples['VBFHToWWTo2L2Nu_M125_CUETUp'] .extend( ['xsec=0.0846', 'kfact=1.000', 'ref=EF'] ) # 3.75*0.215*0.108*0.108*9 YR value samples['VBFHToWWTo2L2Nu_M125_CUETDown'] .extend( ['xsec=0.0846', 'kfact=1.000', 'ref=EF'] ) # 3.75*0.215*0.108*0.108*9 YR value -samples['VBFHToWWToLNuQQ_M115'] .extend( ['xsec=0.0803', 'kfact=1.000', 'ref=Y'] ) # 4.255*0.0859*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M120'] .extend( ['xsec=0.1265', 'kfact=1.000', 'ref=Y'] ) # 4.086*0.141*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M124'] .extend( ['xsec=0.1729', 'kfact=1.000', 'ref=Y'] ) # 3.956*0.199*0.1086*3*0.6741 ### Interpolated Xsec -samples['VBFHToWWToLNuQQ_M125'] .extend( ['xsec=0.1853', 'kfact=1.000', 'ref=Y'] ) # 3.925*0.215*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M126'] .extend( ['xsec=0.1976', 'kfact=1.000', 'ref=Y'] ) # 3.894*0.231*0.1086*3*0.6741 ### Interpolated Xsec -samples['VBFHToWWToLNuQQ_M130'] .extend( ['xsec=0.2511', 'kfact=1.000', 'ref=Y'] ) # 3.773*0.303*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M135'] .extend( ['xsec=0.3188', 'kfact=1.000', 'ref=Y'] ) # 3.629*0.400*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M140'] .extend( ['xsec=0.3842', 'kfact=1.000', 'ref=Y'] ) # 3.492*0.501*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M145'] .extend( ['xsec=0.4430', 'kfact=1.000', 'ref=Y'] ) # 3.362*0.600*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M150'] .extend( ['xsec=0.4951', 'kfact=1.000', 'ref=Y'] ) # 3.239*0.696*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M155'] .extend( ['xsec=0.5451', 'kfact=1.000', 'ref=Y'] ) # 3.122*0.795*0.1086*3*0.6741 ### Interpolated Xsec -samples['VBFHToWWToLNuQQ_M160'] .extend( ['xsec=0.6002', 'kfact=1.000', 'ref=Y'] ) # 3.010*0.908*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M165'] .extend( ['xsec=0.6123', 'kfact=1.000', 'ref=Y'] ) # 2.904*0.960*0.1086*3*0.6741 ### Interpolated Xsec -samples['VBFHToWWToLNuQQ_M170'] .extend( ['xsec=0.5932', 'kfact=1.000', 'ref=Y'] ) # 2.802*0.964*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M175'] .extend( ['xsec=0.5691', 'kfact=1.000', 'ref=Y'] ) # 2.705*0.958*0.1086*3*0.6741 ### Interpolated Xsec -samples['VBFHToWWToLNuQQ_M180'] .extend( ['xsec=0.5346', 'kfact=1.000', 'ref=Y'] ) # 2.612*0.932*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M190'] .extend( ['xsec=0.4212', 'kfact=1.000', 'ref=Y'] ) # 2.440*0.786*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M200'] .extend( ['xsec=0.3714', 'kfact=1.000', 'ref=Y'] ) # 2.282*0.741*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M210'] .extend( ['xsec=0.3395', 'kfact=1.000', 'ref=Y'] ) # 2.138*0.723*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M230'] .extend( ['xsec=0.2929', 'kfact=1.000', 'ref=Y'] ) # 1.884*0.708*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M250'] .extend( ['xsec=0.2570', 'kfact=1.000', 'ref=Y'] ) # 1.669*0.701*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M270'] .extend( ['xsec=0.2273', 'kfact=1.000', 'ref=Y'] ) # 1.485*0.697*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M300'] .extend( ['xsec=0.1909', 'kfact=1.000', 'ref=Y'] ) # 1.256*0.692*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M350'] .extend( ['xsec=0.1435', 'kfact=1.000', 'ref=Y'] ) # 0.9666*0.676*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M400'] .extend( ['xsec=0.0969', 'kfact=1.000', 'ref=Y'] ) # 0.7580*0.582*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M450'] .extend( ['xsec=0.0731', 'kfact=1.000', 'ref=Y'] ) # 0.6038*0.551*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M500'] .extend( ['xsec=0.0584', 'kfact=1.000', 'ref=Y'] ) # 0.4872*0.546*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M550'] .extend( ['xsec=0.0480', 'kfact=1.000', 'ref=Y'] ) # 0.3975*0.550*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M600'] .extend( ['xsec=0.0401', 'kfact=1.000', 'ref=Y'] ) # 0.3274*0.558*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M650'] .extend( ['xsec=0.0339', 'kfact=1.000', 'ref=Y'] ) # 0.2719*0.568*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M700'] .extend( ['xsec=0.0288', 'kfact=1.000', 'ref=Y'] ) # 0.2275*0.577*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M750'] .extend( ['xsec=0.0261', 'kfact=1.000', 'ref=Y'] ) # 0.1915*0.621*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M750_NWA'] .extend( ['xsec=0.0252', 'kfact=1.000', 'ref=KF'] ) # 0.186*0.621*0.1086*3*0.6741 - shall we keep the same BR for NWA? -samples['VBFHToWWToLNuQQ_M800'] .extend( ['xsec=0.0212', 'kfact=1.000', 'ref=Y'] ) # 0.1622*0.594*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M900'] .extend( ['xsec=0.0158', 'kfact=1.000', 'ref=Y'] ) # 0.1180*0.609*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M1000'] .extend( ['xsec=0.0119', 'kfact=1.000', 'ref=Y'] ) # 0.08732*0.621*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M1500'] .extend( ['xsec=0.00312', 'kfact=1.000', 'ref=Y'] ) # 0.02288*0.621*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M2000'] .extend( ['xsec=0.000962', 'kfact=1.000', 'ref=Y'] ) # 0.007052*0.621*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M2500'] .extend( ['xsec=0.000322', 'kfact=1.000', 'ref=Y'] ) # 0.002360*0.621*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M3000'] .extend( ['xsec=0.000113', 'kfact=1.000', 'ref=Y'] ) # 0.0008253*0.621*0.1086*3*0.6741 +samples['VBFHToWWToLNuQQ_M115'] .extend( ['xsec=0.1606', 'kfact=1.000', 'ref=Y'] ) # 4.255*0.0859*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M120'] .extend( ['xsec=0.2530', 'kfact=1.000', 'ref=Y'] ) # 4.086*0.141*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M124'] .extend( ['xsec=0.3458', 'kfact=1.000', 'ref=Y'] ) # 3.956*0.199*0.1086*3*0.6741*2 ### Interpolated Xsec +samples['VBFHToWWToLNuQQ_M125'] .extend( ['xsec=0.3706', 'kfact=1.000', 'ref=Y'] ) # 3.925*0.215*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M126'] .extend( ['xsec=0.3952', 'kfact=1.000', 'ref=Y'] ) # 3.894*0.231*0.1086*3*0.6741*2 ### Interpolated Xsec +samples['VBFHToWWToLNuQQ_M130'] .extend( ['xsec=0.5022', 'kfact=1.000', 'ref=Y'] ) # 3.773*0.303*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M135'] .extend( ['xsec=0.6376', 'kfact=1.000', 'ref=Y'] ) # 3.629*0.400*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M140'] .extend( ['xsec=0.7684', 'kfact=1.000', 'ref=Y'] ) # 3.492*0.501*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M145'] .extend( ['xsec=0.8860', 'kfact=1.000', 'ref=Y'] ) # 3.362*0.600*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M150'] .extend( ['xsec=0.9902', 'kfact=1.000', 'ref=Y'] ) # 3.239*0.696*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M155'] .extend( ['xsec=1.0902', 'kfact=1.000', 'ref=Y'] ) # 3.122*0.795*0.1086*3*0.6741*2 ### Interpolated Xsec +samples['VBFHToWWToLNuQQ_M160'] .extend( ['xsec=1.2004', 'kfact=1.000', 'ref=Y'] ) # 3.010*0.908*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M165'] .extend( ['xsec=1.2246', 'kfact=1.000', 'ref=Y'] ) # 2.904*0.960*0.1086*3*0.6741*2 ### Interpolated Xsec +samples['VBFHToWWToLNuQQ_M170'] .extend( ['xsec=1.1864', 'kfact=1.000', 'ref=Y'] ) # 2.802*0.964*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M175'] .extend( ['xsec=1.1382', 'kfact=1.000', 'ref=Y'] ) # 2.705*0.958*0.1086*3*0.6741*2 ### Interpolated Xsec +samples['VBFHToWWToLNuQQ_M180'] .extend( ['xsec=1.0692', 'kfact=1.000', 'ref=Y'] ) # 2.612*0.932*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M190'] .extend( ['xsec=0.8424', 'kfact=1.000', 'ref=Y'] ) # 2.440*0.786*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M200'] .extend( ['xsec=0.7428', 'kfact=1.000', 'ref=Y'] ) # 2.282*0.741*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M210'] .extend( ['xsec=0.6790', 'kfact=1.000', 'ref=Y'] ) # 2.138*0.723*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M230'] .extend( ['xsec=0.5858', 'kfact=1.000', 'ref=Y'] ) # 1.884*0.708*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M250'] .extend( ['xsec=0.5140', 'kfact=1.000', 'ref=Y'] ) # 1.669*0.701*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M270'] .extend( ['xsec=0.4546', 'kfact=1.000', 'ref=Y'] ) # 1.485*0.697*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M300'] .extend( ['xsec=0.3818', 'kfact=1.000', 'ref=Y'] ) # 1.256*0.692*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M350'] .extend( ['xsec=0.2870', 'kfact=1.000', 'ref=Y'] ) # 0.9666*0.676*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M400'] .extend( ['xsec=0.1938', 'kfact=1.000', 'ref=Y'] ) # 0.7580*0.582*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M450'] .extend( ['xsec=0.1462', 'kfact=1.000', 'ref=Y'] ) # 0.6038*0.551*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M500'] .extend( ['xsec=0.1168', 'kfact=1.000', 'ref=Y'] ) # 0.4872*0.546*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M550'] .extend( ['xsec=0.0960', 'kfact=1.000', 'ref=Y'] ) # 0.3975*0.550*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M600'] .extend( ['xsec=0.0802', 'kfact=1.000', 'ref=Y'] ) # 0.3274*0.558*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M650'] .extend( ['xsec=0.0678', 'kfact=1.000', 'ref=Y'] ) # 0.2719*0.568*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M700'] .extend( ['xsec=0.0576', 'kfact=1.000', 'ref=Y'] ) # 0.2275*0.577*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M750'] .extend( ['xsec=0.0522', 'kfact=1.000', 'ref=Y'] ) # 0.1915*0.621*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M750_NWA'] .extend( ['xsec=0.0504', 'kfact=1.000', 'ref=KF'] ) # 0.186*0.621*0.1086*3*0.6741*2 - shall we keep the same BR for NWA? +samples['VBFHToWWToLNuQQ_M800'] .extend( ['xsec=0.0424', 'kfact=1.000', 'ref=Y'] ) # 0.1622*0.594*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M900'] .extend( ['xsec=0.0316', 'kfact=1.000', 'ref=Y'] ) # 0.1180*0.609*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M1000'] .extend( ['xsec=0.0238', 'kfact=1.000', 'ref=Y'] ) # 0.08732*0.621*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M1500'] .extend( ['xsec=0.00624', 'kfact=1.000', 'ref=Y'] ) # 0.02288*0.621*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M2000'] .extend( ['xsec=0.001924', 'kfact=1.000', 'ref=Y'] ) # 0.007052*0.621*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M2500'] .extend( ['xsec=0.000644', 'kfact=1.000', 'ref=Y'] ) # 0.002360*0.621*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M3000'] .extend( ['xsec=0.000226', 'kfact=1.000', 'ref=Y'] ) # 0.0008253*0.621*0.1086*3*0.6741*2 samples['VBFHToTauTau_M120'] .extend( ['xsec=0.275264', 'kfact=1.000', 'ref=EF'] ) # 3.91*0.0704 samples['VBFHToTauTau_M125'] .extend( ['xsec=0.237000', 'kfact=1.000', 'ref=EF'] ) # 3.75*0.0632 diff --git a/NanoGardener/python/framework/samples/samplesCrossSections2017.py b/NanoGardener/python/framework/samples/samplesCrossSections2017.py index fb978368d..739fdb1e4 100644 --- a/NanoGardener/python/framework/samples/samplesCrossSections2017.py +++ b/NanoGardener/python/framework/samples/samplesCrossSections2017.py @@ -349,48 +349,48 @@ samples['GluGluHToWWTo2L2Nu_M125_CUETUp'] .extend( ['xsec=0.9913', 'kfact=1.000', 'ref=CF'] ) # 43.92*0.215*0.108*0.108*9 Higgs LHC value samples['GluGluHToWWTo2L2Nu_M125_CUETDown'].extend( ['xsec=0.9913', 'kfact=1.000', 'ref=CF'] ) # 43.92*0.215*0.108*0.108*9 Higgs LHC value -samples['GluGluHToWWToLNuQQ_M115'] .extend( ['xsec=1.0018', 'kfact=1.000', 'ref=Y'] ) # 53.10*0.0859*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M120'] .extend( ['xsec=1.5143', 'kfact=1.000', 'ref=Y'] ) # 48.90*0.141*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M124'] .extend( ['xsec=2.0065', 'kfact=1.000', 'ref=Y'] ) # 45.91*0.199*0.1086*3*0.6741 ### Interpolated Xsec -samples['GluGluHToWWToLNuQQ_M125'] .extend( ['xsec=2.1343', 'kfact=1.000', 'ref=Y'] ) # 45.20*0.215*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M126'] .extend( ['xsec=2.2571', 'kfact=1.000', 'ref=Y'] ) # 44.49*0.231*0.1086*3*0.6741 ### Interpolated Xsec -samples['GluGluHToWWToLNuQQ_M130'] .extend( ['xsec=2.7816', 'kfact=1.000', 'ref=Y'] ) # 41.80*0.303*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M135'] .extend( ['xsec=3.4085', 'kfact=1.000', 'ref=Y'] ) # 38.80*0.400*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M140'] .extend( ['xsec=3.9611', 'kfact=1.000', 'ref=Y'] ) # 36.00*0.501*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M145'] .extend( ['xsec=4.4144', 'kfact=1.000', 'ref=Y'] ) # 33.50*0.600*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M150'] .extend( ['xsec=4.7829', 'kfact=1.000', 'ref=Y'] ) # 31.29*0.696*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M155'] .extend( ['xsec=5.1070', 'kfact=1.000', 'ref=Y'] ) # 29.25*0.795*0.1086*3*0.6741 ### Interpolated Xsec -samples['GluGluHToWWToLNuQQ_M160'] .extend( ['xsec=5.5480', 'kfact=1.000', 'ref=Y'] ) # 27.37*0.908*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M165'] .extend( ['xsec=5.4101', 'kfact=1.000', 'ref=Y'] ) # 25.66*0.960*0.1086*3*0.6741 ### Interpolated Xsec -samples['GluGluHToWWToLNuQQ_M170'] .extend( ['xsec=5.1002', 'kfact=1.000', 'ref=Y'] ) # 24.09*0.964*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M175'] .extend( ['xsec=4.7655', 'kfact=1.000', 'ref=Y'] ) # 22.65*0.958*0.1086*3*0.6741 ### Interpolated Xsec -samples['GluGluHToWWToLNuQQ_M180'] .extend( ['xsec=4.3639', 'kfact=1.000', 'ref=Y'] ) # 21.32*0.932*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M190'] .extend( ['xsec=3.2729', 'kfact=1.000', 'ref=Y'] ) # 18.96*0.786*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M200'] .extend( ['xsec=2.7568', 'kfact=1.000', 'ref=Y'] ) # 16.94*0.741*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M210'] .extend( ['xsec=2.4136', 'kfact=1.000', 'ref=Y'] ) # 15.20*0.723*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M230'] .extend( ['xsec=1.9234', 'kfact=1.000', 'ref=Y'] ) # 12.37*0.708*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M250'] .extend( ['xsec=1.5703', 'kfact=1.000', 'ref=Y'] ) # 10.20*0.701*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M270'] .extend( ['xsec=1.3027', 'kfact=1.000', 'ref=Y'] ) # 8.510*0.697*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M300'] .extend( ['xsec=1.0015', 'kfact=1.000', 'ref=Y'] ) # 6.590*0.692*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M350'] .extend( ['xsec=0.6651', 'kfact=1.000', 'ref=Y'] ) # 4.480*0.676*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M400'] .extend( ['xsec=0.4039', 'kfact=1.000', 'ref=Y'] ) # 3.160*0.582*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M450'] .extend( ['xsec=0.2783', 'kfact=1.000', 'ref=Y'] ) # 2.300*0.551*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M500'] .extend( ['xsec=0.2049', 'kfact=1.000', 'ref=Y'] ) # 1.709*0.546*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M550'] .extend( ['xsec=0.1567', 'kfact=1.000', 'ref=Y'] ) # 1.297*0.550*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M600'] .extend( ['xsec=0.1227', 'kfact=1.000', 'ref=Y'] ) # 1.001*0.558*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M650'] .extend( ['xsec=0.0977', 'kfact=1.000', 'ref=Y'] ) # 0.7834*0.568*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M700'] .extend( ['xsec=0.0786', 'kfact=1.000', 'ref=Y'] ) # 0.6206*0.577*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M750'] .extend( ['xsec=0.0639', 'kfact=1.000', 'ref=Y'] ) # 0.4969*0.586*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M750_NWA'] .extend( ['xsec=0.0589', 'kfact=1.000', 'ref=Y'] ) # 0.460*0.586*0.1086*3*0.6741 - shall we keep the same BR for NWA? -samples['GluGluHToWWToLNuQQ_M800'] .extend( ['xsec=0.0524', 'kfact=1.000', 'ref=Y'] ) # 0.4015*0.594*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M900'] .extend( ['xsec=0.0359', 'kfact=1.000', 'ref=Y'] ) # 0.2685*0.609*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M1000'] .extend( ['xsec=0.0252', 'kfact=1.000', 'ref=Y'] ) # 0.1845*0.621*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M1500'] .extend( ['xsec=0.00503', 'kfact=1.000', 'ref=Y'] ) # 0.0369*0.621*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M2000'] .extend( ['xsec=0.00131', 'kfact=1.000', 'ref=Y'] ) # 0.00960*0.621*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M2500'] .extend( ['xsec=0.000394', 'kfact=1.000', 'ref=Y'] ) # 0.00289*0.621*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M3000'] .extend( ['xsec=0.000128', 'kfact=1.000', 'ref=Y'] ) # 0.00094*0.621*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M4000'] .extend( ['xsec=0.0000115', 'kfact=1.000', 'ref=Y'] ) # 0.000114*0.621*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M5000'] .extend( ['xsec=0.0000019', 'kfact=1.000', 'ref=Y'] ) # 0.000014*0.621*0.1086*3*0.6741 +samples['GluGluHToWWToLNuQQ_M115'] .extend( ['xsec=2.00352', 'kfact=1.000', 'ref=Y'] ) # 53.10*0.0859*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M120'] .extend( ['xsec=3.02854', 'kfact=1.000', 'ref=Y'] ) # 48.90*0.141*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M124'] .extend( ['xsec=4.01297', 'kfact=1.000', 'ref=Y'] ) # 45.91*0.199*0.1086*3*0.6741*2 ### Interpolated Xsec +samples['GluGluHToWWToLNuQQ_M125'] .extend( ['xsec=4.26857', 'kfact=1.000', 'ref=Y'] ) # 45.20*0.215*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M126'] .extend( ['xsec=4.51419', 'kfact=1.000', 'ref=Y'] ) # 44.49*0.231*0.1086*3*0.6741*2 ### Interpolated Xsec +samples['GluGluHToWWToLNuQQ_M130'] .extend( ['xsec=5.56320', 'kfact=1.000', 'ref=Y'] ) # 41.80*0.303*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M135'] .extend( ['xsec=6.81706', 'kfact=1.000', 'ref=Y'] ) # 38.80*0.400*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M140'] .extend( ['xsec=7.92220', 'kfact=1.000', 'ref=Y'] ) # 36.00*0.501*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M145'] .extend( ['xsec=8.82880', 'kfact=1.000', 'ref=Y'] ) # 33.50*0.600*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M150'] .extend( ['xsec=9.56578', 'kfact=1.000', 'ref=Y'] ) # 31.29*0.696*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M155'] .extend( ['xsec=10.21406', 'kfact=1.000', 'ref=Y'] ) # 29.25*0.795*0.1086*3*0.6741*2 ### Interpolated Xsec +samples['GluGluHToWWToLNuQQ_M160'] .extend( ['xsec=10.91606', 'kfact=1.000', 'ref=Y'] ) # 27.37*0.908*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M165'] .extend( ['xsec=10.82015', 'kfact=1.000', 'ref=Y'] ) # 25.66*0.960*0.1086*3*0.6741*2 ### Interpolated Xsec +samples['GluGluHToWWToLNuQQ_M170'] .extend( ['xsec=10.20045', 'kfact=1.000', 'ref=Y'] ) # 24.09*0.964*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M175'] .extend( ['xsec=9.53101', 'kfact=1.000', 'ref=Y'] ) # 22.65*0.958*0.1086*3*0.6741*2 ### Interpolated Xsec +samples['GluGluHToWWToLNuQQ_M180'] .extend( ['xsec=8.72787', 'kfact=1.000', 'ref=Y'] ) # 21.32*0.932*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M190'] .extend( ['xsec=6.54585', 'kfact=1.000', 'ref=Y'] ) # 18.96*0.786*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M200'] .extend( ['xsec=5.51362', 'kfact=1.000', 'ref=Y'] ) # 16.94*0.741*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M210'] .extend( ['xsec=4.82711', 'kfact=1.000', 'ref=Y'] ) # 15.20*0.723*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M230'] .extend( ['xsec=3.84688', 'kfact=1.000', 'ref=Y'] ) # 12.37*0.708*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M250'] .extend( ['xsec=3.14068', 'kfact=1.000', 'ref=Y'] ) # 10.20*0.701*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M270'] .extend( ['xsec=2.60536', 'kfact=1.000', 'ref=Y'] ) # 8.510*0.697*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M300'] .extend( ['xsec=2.00307', 'kfact=1.000', 'ref=Y'] ) # 6.590*0.692*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M350'] .extend( ['xsec=1.33024', 'kfact=1.000', 'ref=Y'] ) # 4.480*0.676*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M400'] .extend( ['xsec=0.80782', 'kfact=1.000', 'ref=Y'] ) # 3.160*0.582*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M450'] .extend( ['xsec=0.55665', 'kfact=1.000', 'ref=Y'] ) # 2.300*0.551*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M500'] .extend( ['xsec=0.40986', 'kfact=1.000', 'ref=Y'] ) # 1.709*0.546*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M550'] .extend( ['xsec=0.31333', 'kfact=1.000', 'ref=Y'] ) # 1.297*0.550*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M600'] .extend( ['xsec=0.24534', 'kfact=1.000', 'ref=Y'] ) # 1.001*0.558*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M650'] .extend( ['xsec=0.19545', 'kfact=1.000', 'ref=Y'] ) # 0.7834*0.568*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M700'] .extend( ['xsec=0.15729', 'kfact=1.000', 'ref=Y'] ) # 0.6206*0.577*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M750'] .extend( ['xsec=0.12790', 'kfact=1.000', 'ref=Y'] ) # 0.4969*0.586*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M750_NWA'] .extend( ['xsec=0.11840', 'kfact=1.000', 'ref=KF'] ) # 0.460*0.586*0.1086*3*0.6741*2 - shall we keep the same BR for NWA? +samples['GluGluHToWWToLNuQQ_M800'] .extend( ['xsec=0.10476', 'kfact=1.000', 'ref=Y'] ) # 0.4015*0.594*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M900'] .extend( ['xsec=0.07182', 'kfact=1.000', 'ref=Y'] ) # 0.2685*0.609*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M1000'] .extend( ['xsec=0.05033', 'kfact=1.000', 'ref=Y'] ) # 0.1845*0.621*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M1500'] .extend( ['xsec=0.01007', 'kfact=1.000', 'ref=Y'] ) # 0.0369*0.621*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M2000'] .extend( ['xsec=0.00262', 'kfact=1.000', 'ref=Y'] ) # 0.00960*0.621*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M2500'] .extend( ['xsec=0.00078831', 'kfact=1.000', 'ref=Y'] ) # 0.00289*0.621*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M3000'] .extend( ['xsec=0.00025640', 'kfact=1.000', 'ref=Y'] ) # 0.00094*0.621*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M4000'] .extend( ['xsec=0.00003110', 'kfact=1.000', 'ref=Y'] ) # 0.000114*0.621*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M5000'] .extend( ['xsec=0.00000382', 'kfact=1.000', 'ref=Y'] ) # 0.000014*0.621*0.1086*3*0.6741*2 samples['GluGluHToZZTo4L_M125'] .extend( ['xsec=0.0118', 'kfact=1.000', 'ref=CF'] ) # 43.92*0.0264*0.033658*0.033658*9 samples['GluGluHToTauTau_M120'] .extend( ['xsec=2.2676', 'kfact=1.000', 'ref=KF'] ) # 32.21*0.0704 @@ -553,46 +553,46 @@ samples['VBFHToWWTo2L2Nu_M125_CUETUp'] .extend( ['xsec=0.0846', 'kfact=1.000', 'ref=EF'] ) # 3.75*0.215*0.108*0.108*9 YR value samples['VBFHToWWTo2L2Nu_M125_CUETDown'] .extend( ['xsec=0.0846', 'kfact=1.000', 'ref=EF'] ) # 3.75*0.215*0.108*0.108*9 YR value -samples['VBFHToWWToLNuQQ_M115'] .extend( ['xsec=0.0803', 'kfact=1.000', 'ref=Y'] ) # 4.255*0.0859*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M120'] .extend( ['xsec=0.1265', 'kfact=1.000', 'ref=Y'] ) # 4.086*0.141*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M124'] .extend( ['xsec=0.1729', 'kfact=1.000', 'ref=Y'] ) # 3.956*0.199*0.1086*3*0.6741 ### Interpolated Xsec -samples['VBFHToWWToLNuQQ_M125'] .extend( ['xsec=0.1853', 'kfact=1.000', 'ref=Y'] ) # 3.925*0.215*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M126'] .extend( ['xsec=0.1976', 'kfact=1.000', 'ref=Y'] ) # 3.894*0.231*0.1086*3*0.6741 ### Interpolated Xsec -samples['VBFHToWWToLNuQQ_M130'] .extend( ['xsec=0.2511', 'kfact=1.000', 'ref=Y'] ) # 3.773*0.303*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M135'] .extend( ['xsec=0.3188', 'kfact=1.000', 'ref=Y'] ) # 3.629*0.400*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M140'] .extend( ['xsec=0.3842', 'kfact=1.000', 'ref=Y'] ) # 3.492*0.501*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M145'] .extend( ['xsec=0.4430', 'kfact=1.000', 'ref=Y'] ) # 3.362*0.600*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M150'] .extend( ['xsec=0.4951', 'kfact=1.000', 'ref=Y'] ) # 3.239*0.696*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M155'] .extend( ['xsec=0.5451', 'kfact=1.000', 'ref=Y'] ) # 3.122*0.795*0.1086*3*0.6741 ### Interpolated Xsec -samples['VBFHToWWToLNuQQ_M160'] .extend( ['xsec=0.6002', 'kfact=1.000', 'ref=Y'] ) # 3.010*0.908*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M165'] .extend( ['xsec=0.6123', 'kfact=1.000', 'ref=Y'] ) # 2.904*0.960*0.1086*3*0.6741 ### Interpolated Xsec -samples['VBFHToWWToLNuQQ_M170'] .extend( ['xsec=0.5932', 'kfact=1.000', 'ref=Y'] ) # 2.802*0.964*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M175'] .extend( ['xsec=0.5691', 'kfact=1.000', 'ref=Y'] ) # 2.705*0.958*0.1086*3*0.6741 ### Interpolated Xsec -samples['VBFHToWWToLNuQQ_M180'] .extend( ['xsec=0.5346', 'kfact=1.000', 'ref=Y'] ) # 2.612*0.932*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M190'] .extend( ['xsec=0.4212', 'kfact=1.000', 'ref=Y'] ) # 2.440*0.786*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M200'] .extend( ['xsec=0.3714', 'kfact=1.000', 'ref=Y'] ) # 2.282*0.741*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M210'] .extend( ['xsec=0.3395', 'kfact=1.000', 'ref=Y'] ) # 2.138*0.723*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M230'] .extend( ['xsec=0.2929', 'kfact=1.000', 'ref=Y'] ) # 1.884*0.708*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M250'] .extend( ['xsec=0.2570', 'kfact=1.000', 'ref=Y'] ) # 1.669*0.701*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M270'] .extend( ['xsec=0.2273', 'kfact=1.000', 'ref=Y'] ) # 1.485*0.697*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M300'] .extend( ['xsec=0.1909', 'kfact=1.000', 'ref=Y'] ) # 1.256*0.692*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M350'] .extend( ['xsec=0.1435', 'kfact=1.000', 'ref=Y'] ) # 0.9666*0.676*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M400'] .extend( ['xsec=0.0969', 'kfact=1.000', 'ref=Y'] ) # 0.7580*0.582*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M450'] .extend( ['xsec=0.0731', 'kfact=1.000', 'ref=Y'] ) # 0.6038*0.551*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M500'] .extend( ['xsec=0.0584', 'kfact=1.000', 'ref=Y'] ) # 0.4872*0.546*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M550'] .extend( ['xsec=0.0480', 'kfact=1.000', 'ref=Y'] ) # 0.3975*0.550*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M600'] .extend( ['xsec=0.0401', 'kfact=1.000', 'ref=Y'] ) # 0.3274*0.558*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M650'] .extend( ['xsec=0.0339', 'kfact=1.000', 'ref=Y'] ) # 0.2719*0.568*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M700'] .extend( ['xsec=0.0288', 'kfact=1.000', 'ref=Y'] ) # 0.2275*0.577*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M750'] .extend( ['xsec=0.0261', 'kfact=1.000', 'ref=Y'] ) # 0.1915*0.621*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M750_NWA'] .extend( ['xsec=0.0252', 'kfact=1.000', 'ref=KF'] ) # 0.186*0.621*0.1086*3*0.6741 - shall we keep the same BR for NWA? -samples['VBFHToWWToLNuQQ_M800'] .extend( ['xsec=0.0212', 'kfact=1.000', 'ref=Y'] ) # 0.1622*0.594*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M900'] .extend( ['xsec=0.0158', 'kfact=1.000', 'ref=Y'] ) # 0.1180*0.609*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M1000'] .extend( ['xsec=0.0119', 'kfact=1.000', 'ref=Y'] ) # 0.08732*0.621*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M1500'] .extend( ['xsec=0.00312', 'kfact=1.000', 'ref=Y'] ) # 0.02288*0.621*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M2000'] .extend( ['xsec=0.000962', 'kfact=1.000', 'ref=Y'] ) # 0.007052*0.621*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M2500'] .extend( ['xsec=0.000322', 'kfact=1.000', 'ref=Y'] ) # 0.002360*0.621*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M3000'] .extend( ['xsec=0.000113', 'kfact=1.000', 'ref=Y'] ) # 0.0008253*0.621*0.1086*3*0.6741 +samples['VBFHToWWToLNuQQ_M115'] .extend( ['xsec=0.1606', 'kfact=1.000', 'ref=Y'] ) # 4.255*0.0859*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M120'] .extend( ['xsec=0.2530', 'kfact=1.000', 'ref=Y'] ) # 4.086*0.141*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M124'] .extend( ['xsec=0.3458', 'kfact=1.000', 'ref=Y'] ) # 3.956*0.199*0.1086*3*0.6741*2 ### Interpolated Xsec +samples['VBFHToWWToLNuQQ_M125'] .extend( ['xsec=0.3706', 'kfact=1.000', 'ref=Y'] ) # 3.925*0.215*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M126'] .extend( ['xsec=0.3952', 'kfact=1.000', 'ref=Y'] ) # 3.894*0.231*0.1086*3*0.6741*2 ### Interpolated Xsec +samples['VBFHToWWToLNuQQ_M130'] .extend( ['xsec=0.5022', 'kfact=1.000', 'ref=Y'] ) # 3.773*0.303*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M135'] .extend( ['xsec=0.6376', 'kfact=1.000', 'ref=Y'] ) # 3.629*0.400*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M140'] .extend( ['xsec=0.7684', 'kfact=1.000', 'ref=Y'] ) # 3.492*0.501*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M145'] .extend( ['xsec=0.8860', 'kfact=1.000', 'ref=Y'] ) # 3.362*0.600*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M150'] .extend( ['xsec=0.9902', 'kfact=1.000', 'ref=Y'] ) # 3.239*0.696*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M155'] .extend( ['xsec=1.0902', 'kfact=1.000', 'ref=Y'] ) # 3.122*0.795*0.1086*3*0.6741*2 ### Interpolated Xsec +samples['VBFHToWWToLNuQQ_M160'] .extend( ['xsec=1.2004', 'kfact=1.000', 'ref=Y'] ) # 3.010*0.908*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M165'] .extend( ['xsec=1.2246', 'kfact=1.000', 'ref=Y'] ) # 2.904*0.960*0.1086*3*0.6741*2 ### Interpolated Xsec +samples['VBFHToWWToLNuQQ_M170'] .extend( ['xsec=1.1864', 'kfact=1.000', 'ref=Y'] ) # 2.802*0.964*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M175'] .extend( ['xsec=1.1382', 'kfact=1.000', 'ref=Y'] ) # 2.705*0.958*0.1086*3*0.6741*2 ### Interpolated Xsec +samples['VBFHToWWToLNuQQ_M180'] .extend( ['xsec=1.0692', 'kfact=1.000', 'ref=Y'] ) # 2.612*0.932*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M190'] .extend( ['xsec=0.8424', 'kfact=1.000', 'ref=Y'] ) # 2.440*0.786*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M200'] .extend( ['xsec=0.7428', 'kfact=1.000', 'ref=Y'] ) # 2.282*0.741*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M210'] .extend( ['xsec=0.6790', 'kfact=1.000', 'ref=Y'] ) # 2.138*0.723*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M230'] .extend( ['xsec=0.5858', 'kfact=1.000', 'ref=Y'] ) # 1.884*0.708*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M250'] .extend( ['xsec=0.5140', 'kfact=1.000', 'ref=Y'] ) # 1.669*0.701*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M270'] .extend( ['xsec=0.4546', 'kfact=1.000', 'ref=Y'] ) # 1.485*0.697*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M300'] .extend( ['xsec=0.3818', 'kfact=1.000', 'ref=Y'] ) # 1.256*0.692*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M350'] .extend( ['xsec=0.2870', 'kfact=1.000', 'ref=Y'] ) # 0.9666*0.676*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M400'] .extend( ['xsec=0.1938', 'kfact=1.000', 'ref=Y'] ) # 0.7580*0.582*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M450'] .extend( ['xsec=0.1462', 'kfact=1.000', 'ref=Y'] ) # 0.6038*0.551*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M500'] .extend( ['xsec=0.1168', 'kfact=1.000', 'ref=Y'] ) # 0.4872*0.546*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M550'] .extend( ['xsec=0.0960', 'kfact=1.000', 'ref=Y'] ) # 0.3975*0.550*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M600'] .extend( ['xsec=0.0802', 'kfact=1.000', 'ref=Y'] ) # 0.3274*0.558*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M650'] .extend( ['xsec=0.0678', 'kfact=1.000', 'ref=Y'] ) # 0.2719*0.568*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M700'] .extend( ['xsec=0.0576', 'kfact=1.000', 'ref=Y'] ) # 0.2275*0.577*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M750'] .extend( ['xsec=0.0522', 'kfact=1.000', 'ref=Y'] ) # 0.1915*0.621*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M750_NWA'] .extend( ['xsec=0.0504', 'kfact=1.000', 'ref=KF'] ) # 0.186*0.621*0.1086*3*0.6741*2 - shall we keep the same BR for NWA? +samples['VBFHToWWToLNuQQ_M800'] .extend( ['xsec=0.0424', 'kfact=1.000', 'ref=Y'] ) # 0.1622*0.594*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M900'] .extend( ['xsec=0.0316', 'kfact=1.000', 'ref=Y'] ) # 0.1180*0.609*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M1000'] .extend( ['xsec=0.0238', 'kfact=1.000', 'ref=Y'] ) # 0.08732*0.621*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M1500'] .extend( ['xsec=0.00624', 'kfact=1.000', 'ref=Y'] ) # 0.02288*0.621*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M2000'] .extend( ['xsec=0.001924', 'kfact=1.000', 'ref=Y'] ) # 0.007052*0.621*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M2500'] .extend( ['xsec=0.000644', 'kfact=1.000', 'ref=Y'] ) # 0.002360*0.621*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M3000'] .extend( ['xsec=0.000226', 'kfact=1.000', 'ref=Y'] ) # 0.0008253*0.621*0.1086*3*0.6741*2 samples['VBFHToTauTau_M120'] .extend( ['xsec=0.275264', 'kfact=1.000', 'ref=EF'] ) # 3.91*0.0704 samples['VBFHToTauTau_M125'] .extend( ['xsec=0.237000', 'kfact=1.000', 'ref=EF'] ) # 3.75*0.0632 diff --git a/NanoGardener/python/framework/samples/samplesCrossSections2018.py b/NanoGardener/python/framework/samples/samplesCrossSections2018.py index f6801efd1..9c0c307ad 100644 --- a/NanoGardener/python/framework/samples/samplesCrossSections2018.py +++ b/NanoGardener/python/framework/samples/samplesCrossSections2018.py @@ -335,48 +335,48 @@ samples['GluGluHToWWTo2L2Nu_M125_CUETUp'] .extend( ['xsec=0.9913', 'kfact=1.000', 'ref=CF'] ) # 43.92*0.215*0.108*0.108*9 Higgs LHC value samples['GluGluHToWWTo2L2Nu_M125_CUETDown'].extend( ['xsec=0.9913', 'kfact=1.000', 'ref=CF'] ) # 43.92*0.215*0.108*0.108*9 Higgs LHC value -samples['GluGluHToWWToLNuQQ_M115'] .extend( ['xsec=1.0018', 'kfact=1.000', 'ref=Y'] ) # 53.10*0.0859*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M120'] .extend( ['xsec=1.5143', 'kfact=1.000', 'ref=Y'] ) # 48.90*0.141*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M124'] .extend( ['xsec=2.0065', 'kfact=1.000', 'ref=Y'] ) # 45.91*0.199*0.1086*3*0.6741 ### Interpolated Xsec -samples['GluGluHToWWToLNuQQ_M125'] .extend( ['xsec=2.1343', 'kfact=1.000', 'ref=Y'] ) # 45.20*0.215*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M126'] .extend( ['xsec=2.2571', 'kfact=1.000', 'ref=Y'] ) # 44.49*0.231*0.1086*3*0.6741 ### Interpolated Xsec -samples['GluGluHToWWToLNuQQ_M130'] .extend( ['xsec=2.7816', 'kfact=1.000', 'ref=Y'] ) # 41.80*0.303*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M135'] .extend( ['xsec=3.4085', 'kfact=1.000', 'ref=Y'] ) # 38.80*0.400*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M140'] .extend( ['xsec=3.9611', 'kfact=1.000', 'ref=Y'] ) # 36.00*0.501*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M145'] .extend( ['xsec=4.4144', 'kfact=1.000', 'ref=Y'] ) # 33.50*0.600*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M150'] .extend( ['xsec=4.7829', 'kfact=1.000', 'ref=Y'] ) # 31.29*0.696*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M155'] .extend( ['xsec=5.1070', 'kfact=1.000', 'ref=Y'] ) # 29.25*0.795*0.1086*3*0.6741 ### Interpolated Xsec -samples['GluGluHToWWToLNuQQ_M160'] .extend( ['xsec=5.5480', 'kfact=1.000', 'ref=Y'] ) # 27.37*0.908*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M165'] .extend( ['xsec=5.4101', 'kfact=1.000', 'ref=Y'] ) # 25.66*0.960*0.1086*3*0.6741 ### Interpolated Xsec -samples['GluGluHToWWToLNuQQ_M170'] .extend( ['xsec=5.1002', 'kfact=1.000', 'ref=Y'] ) # 24.09*0.964*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M175'] .extend( ['xsec=4.7655', 'kfact=1.000', 'ref=Y'] ) # 22.65*0.958*0.1086*3*0.6741 ### Interpolated Xsec -samples['GluGluHToWWToLNuQQ_M180'] .extend( ['xsec=4.3639', 'kfact=1.000', 'ref=Y'] ) # 21.32*0.932*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M190'] .extend( ['xsec=3.2729', 'kfact=1.000', 'ref=Y'] ) # 18.96*0.786*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M200'] .extend( ['xsec=2.7568', 'kfact=1.000', 'ref=Y'] ) # 16.94*0.741*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M210'] .extend( ['xsec=2.4136', 'kfact=1.000', 'ref=Y'] ) # 15.20*0.723*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M230'] .extend( ['xsec=1.9234', 'kfact=1.000', 'ref=Y'] ) # 12.37*0.708*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M250'] .extend( ['xsec=1.5703', 'kfact=1.000', 'ref=Y'] ) # 10.20*0.701*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M270'] .extend( ['xsec=1.3027', 'kfact=1.000', 'ref=Y'] ) # 8.510*0.697*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M300'] .extend( ['xsec=1.0015', 'kfact=1.000', 'ref=Y'] ) # 6.590*0.692*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M350'] .extend( ['xsec=0.6651', 'kfact=1.000', 'ref=Y'] ) # 4.480*0.676*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M400'] .extend( ['xsec=0.4039', 'kfact=1.000', 'ref=Y'] ) # 3.160*0.582*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M450'] .extend( ['xsec=0.2783', 'kfact=1.000', 'ref=Y'] ) # 2.300*0.551*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M500'] .extend( ['xsec=0.2049', 'kfact=1.000', 'ref=Y'] ) # 1.709*0.546*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M550'] .extend( ['xsec=0.1567', 'kfact=1.000', 'ref=Y'] ) # 1.297*0.550*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M600'] .extend( ['xsec=0.1227', 'kfact=1.000', 'ref=Y'] ) # 1.001*0.558*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M650'] .extend( ['xsec=0.0977', 'kfact=1.000', 'ref=Y'] ) # 0.7834*0.568*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M700'] .extend( ['xsec=0.0786', 'kfact=1.000', 'ref=Y'] ) # 0.6206*0.577*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M750'] .extend( ['xsec=0.0639', 'kfact=1.000', 'ref=Y'] ) # 0.4969*0.586*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M750_NWA'] .extend( ['xsec=0.0589', 'kfact=1.000', 'ref=Y'] ) # 0.460*0.586*0.1086*3*0.6741 - shall we keep the same BR for NWA? -samples['GluGluHToWWToLNuQQ_M800'] .extend( ['xsec=0.0524', 'kfact=1.000', 'ref=Y'] ) # 0.4015*0.594*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M900'] .extend( ['xsec=0.0359', 'kfact=1.000', 'ref=Y'] ) # 0.2685*0.609*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M1000'] .extend( ['xsec=0.0252', 'kfact=1.000', 'ref=Y'] ) # 0.1845*0.621*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M1500'] .extend( ['xsec=0.00503', 'kfact=1.000', 'ref=Y'] ) # 0.0369*0.621*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M2000'] .extend( ['xsec=0.00131', 'kfact=1.000', 'ref=Y'] ) # 0.00960*0.621*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M2500'] .extend( ['xsec=0.000394', 'kfact=1.000', 'ref=Y'] ) # 0.00289*0.621*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M3000'] .extend( ['xsec=0.000128', 'kfact=1.000', 'ref=Y'] ) # 0.00094*0.621*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M4000'] .extend( ['xsec=0.0000115', 'kfact=1.000', 'ref=Y'] ) # 0.000114*0.621*0.1086*3*0.6741 -samples['GluGluHToWWToLNuQQ_M5000'] .extend( ['xsec=0.0000019', 'kfact=1.000', 'ref=Y'] ) # 0.000014*0.621*0.1086*3*0.6741 +samples['GluGluHToWWToLNuQQ_M115'] .extend( ['xsec=2.00352', 'kfact=1.000', 'ref=Y'] ) # 53.10*0.0859*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M120'] .extend( ['xsec=3.02854', 'kfact=1.000', 'ref=Y'] ) # 48.90*0.141*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M124'] .extend( ['xsec=4.01297', 'kfact=1.000', 'ref=Y'] ) # 45.91*0.199*0.1086*3*0.6741*2 ### Interpolated Xsec +samples['GluGluHToWWToLNuQQ_M125'] .extend( ['xsec=4.26857', 'kfact=1.000', 'ref=Y'] ) # 45.20*0.215*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M126'] .extend( ['xsec=4.51419', 'kfact=1.000', 'ref=Y'] ) # 44.49*0.231*0.1086*3*0.6741*2 ### Interpolated Xsec +samples['GluGluHToWWToLNuQQ_M130'] .extend( ['xsec=5.56320', 'kfact=1.000', 'ref=Y'] ) # 41.80*0.303*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M135'] .extend( ['xsec=6.81706', 'kfact=1.000', 'ref=Y'] ) # 38.80*0.400*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M140'] .extend( ['xsec=7.92220', 'kfact=1.000', 'ref=Y'] ) # 36.00*0.501*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M145'] .extend( ['xsec=8.82880', 'kfact=1.000', 'ref=Y'] ) # 33.50*0.600*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M150'] .extend( ['xsec=9.56578', 'kfact=1.000', 'ref=Y'] ) # 31.29*0.696*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M155'] .extend( ['xsec=10.21406', 'kfact=1.000', 'ref=Y'] ) # 29.25*0.795*0.1086*3*0.6741*2 ### Interpolated Xsec +samples['GluGluHToWWToLNuQQ_M160'] .extend( ['xsec=10.91606', 'kfact=1.000', 'ref=Y'] ) # 27.37*0.908*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M165'] .extend( ['xsec=10.82015', 'kfact=1.000', 'ref=Y'] ) # 25.66*0.960*0.1086*3*0.6741*2 ### Interpolated Xsec +samples['GluGluHToWWToLNuQQ_M170'] .extend( ['xsec=10.20045', 'kfact=1.000', 'ref=Y'] ) # 24.09*0.964*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M175'] .extend( ['xsec=9.53101', 'kfact=1.000', 'ref=Y'] ) # 22.65*0.958*0.1086*3*0.6741*2 ### Interpolated Xsec +samples['GluGluHToWWToLNuQQ_M180'] .extend( ['xsec=8.72787', 'kfact=1.000', 'ref=Y'] ) # 21.32*0.932*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M190'] .extend( ['xsec=6.54585', 'kfact=1.000', 'ref=Y'] ) # 18.96*0.786*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M200'] .extend( ['xsec=5.51362', 'kfact=1.000', 'ref=Y'] ) # 16.94*0.741*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M210'] .extend( ['xsec=4.82711', 'kfact=1.000', 'ref=Y'] ) # 15.20*0.723*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M230'] .extend( ['xsec=3.84688', 'kfact=1.000', 'ref=Y'] ) # 12.37*0.708*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M250'] .extend( ['xsec=3.14068', 'kfact=1.000', 'ref=Y'] ) # 10.20*0.701*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M270'] .extend( ['xsec=2.60536', 'kfact=1.000', 'ref=Y'] ) # 8.510*0.697*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M300'] .extend( ['xsec=2.00307', 'kfact=1.000', 'ref=Y'] ) # 6.590*0.692*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M350'] .extend( ['xsec=1.33024', 'kfact=1.000', 'ref=Y'] ) # 4.480*0.676*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M400'] .extend( ['xsec=0.80782', 'kfact=1.000', 'ref=Y'] ) # 3.160*0.582*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M450'] .extend( ['xsec=0.55665', 'kfact=1.000', 'ref=Y'] ) # 2.300*0.551*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M500'] .extend( ['xsec=0.40986', 'kfact=1.000', 'ref=Y'] ) # 1.709*0.546*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M550'] .extend( ['xsec=0.31333', 'kfact=1.000', 'ref=Y'] ) # 1.297*0.550*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M600'] .extend( ['xsec=0.24534', 'kfact=1.000', 'ref=Y'] ) # 1.001*0.558*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M650'] .extend( ['xsec=0.19545', 'kfact=1.000', 'ref=Y'] ) # 0.7834*0.568*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M700'] .extend( ['xsec=0.15729', 'kfact=1.000', 'ref=Y'] ) # 0.6206*0.577*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M750'] .extend( ['xsec=0.12790', 'kfact=1.000', 'ref=Y'] ) # 0.4969*0.586*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M750_NWA'] .extend( ['xsec=0.11840', 'kfact=1.000', 'ref=KF'] ) # 0.460*0.586*0.1086*3*0.6741*2 - shall we keep the same BR for NWA? +samples['GluGluHToWWToLNuQQ_M800'] .extend( ['xsec=0.10476', 'kfact=1.000', 'ref=Y'] ) # 0.4015*0.594*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M900'] .extend( ['xsec=0.07182', 'kfact=1.000', 'ref=Y'] ) # 0.2685*0.609*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M1000'] .extend( ['xsec=0.05033', 'kfact=1.000', 'ref=Y'] ) # 0.1845*0.621*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M1500'] .extend( ['xsec=0.01007', 'kfact=1.000', 'ref=Y'] ) # 0.0369*0.621*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M2000'] .extend( ['xsec=0.00262', 'kfact=1.000', 'ref=Y'] ) # 0.00960*0.621*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M2500'] .extend( ['xsec=0.00078831', 'kfact=1.000', 'ref=Y'] ) # 0.00289*0.621*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M3000'] .extend( ['xsec=0.00025640', 'kfact=1.000', 'ref=Y'] ) # 0.00094*0.621*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M4000'] .extend( ['xsec=0.00003110', 'kfact=1.000', 'ref=Y'] ) # 0.000114*0.621*0.1086*3*0.6741*2 +samples['GluGluHToWWToLNuQQ_M5000'] .extend( ['xsec=0.00000382', 'kfact=1.000', 'ref=Y'] ) # 0.000014*0.621*0.1086*3*0.6741*2 samples['GluGluHToZZTo4L_M125'] .extend( ['xsec=0.0118', 'kfact=1.000', 'ref=CF'] ) # 43.92*0.0264*0.033658*0.033658*9 samples['GluGluHToTauTau_M120'] .extend( ['xsec=2.2676', 'kfact=1.000', 'ref=KF'] ) # 32.21*0.0704 @@ -534,46 +534,46 @@ samples['VBFHToWWTo2L2Nu_M125_CUETUp'] .extend( ['xsec=0.0846', 'kfact=1.000', 'ref=EF'] ) # 3.75*0.215*0.108*0.108*9 YR value samples['VBFHToWWTo2L2Nu_M125_CUETDown'] .extend( ['xsec=0.0846', 'kfact=1.000', 'ref=EF'] ) # 3.75*0.215*0.108*0.108*9 YR value -samples['VBFHToWWToLNuQQ_M115'] .extend( ['xsec=0.0803', 'kfact=1.000', 'ref=Y'] ) # 4.255*0.0859*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M120'] .extend( ['xsec=0.1265', 'kfact=1.000', 'ref=Y'] ) # 4.086*0.141*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M124'] .extend( ['xsec=0.1729', 'kfact=1.000', 'ref=Y'] ) # 3.956*0.199*0.1086*3*0.6741 ### Interpolated Xsec -samples['VBFHToWWToLNuQQ_M125'] .extend( ['xsec=0.1853', 'kfact=1.000', 'ref=Y'] ) # 3.925*0.215*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M126'] .extend( ['xsec=0.1976', 'kfact=1.000', 'ref=Y'] ) # 3.894*0.231*0.1086*3*0.6741 ### Interpolated Xsec -samples['VBFHToWWToLNuQQ_M130'] .extend( ['xsec=0.2511', 'kfact=1.000', 'ref=Y'] ) # 3.773*0.303*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M135'] .extend( ['xsec=0.3188', 'kfact=1.000', 'ref=Y'] ) # 3.629*0.400*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M140'] .extend( ['xsec=0.3842', 'kfact=1.000', 'ref=Y'] ) # 3.492*0.501*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M145'] .extend( ['xsec=0.4430', 'kfact=1.000', 'ref=Y'] ) # 3.362*0.600*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M150'] .extend( ['xsec=0.4951', 'kfact=1.000', 'ref=Y'] ) # 3.239*0.696*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M155'] .extend( ['xsec=0.5451', 'kfact=1.000', 'ref=Y'] ) # 3.122*0.795*0.1086*3*0.6741 ### Interpolated Xsec -samples['VBFHToWWToLNuQQ_M160'] .extend( ['xsec=0.6002', 'kfact=1.000', 'ref=Y'] ) # 3.010*0.908*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M165'] .extend( ['xsec=0.6123', 'kfact=1.000', 'ref=Y'] ) # 2.904*0.960*0.1086*3*0.6741 ### Interpolated Xsec -samples['VBFHToWWToLNuQQ_M170'] .extend( ['xsec=0.5932', 'kfact=1.000', 'ref=Y'] ) # 2.802*0.964*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M175'] .extend( ['xsec=0.5691', 'kfact=1.000', 'ref=Y'] ) # 2.705*0.958*0.1086*3*0.6741 ### Interpolated Xsec -samples['VBFHToWWToLNuQQ_M180'] .extend( ['xsec=0.5346', 'kfact=1.000', 'ref=Y'] ) # 2.612*0.932*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M190'] .extend( ['xsec=0.4212', 'kfact=1.000', 'ref=Y'] ) # 2.440*0.786*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M200'] .extend( ['xsec=0.3714', 'kfact=1.000', 'ref=Y'] ) # 2.282*0.741*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M210'] .extend( ['xsec=0.3395', 'kfact=1.000', 'ref=Y'] ) # 2.138*0.723*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M230'] .extend( ['xsec=0.2929', 'kfact=1.000', 'ref=Y'] ) # 1.884*0.708*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M250'] .extend( ['xsec=0.2570', 'kfact=1.000', 'ref=Y'] ) # 1.669*0.701*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M270'] .extend( ['xsec=0.2273', 'kfact=1.000', 'ref=Y'] ) # 1.485*0.697*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M300'] .extend( ['xsec=0.1909', 'kfact=1.000', 'ref=Y'] ) # 1.256*0.692*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M350'] .extend( ['xsec=0.1435', 'kfact=1.000', 'ref=Y'] ) # 0.9666*0.676*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M400'] .extend( ['xsec=0.0969', 'kfact=1.000', 'ref=Y'] ) # 0.7580*0.582*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M450'] .extend( ['xsec=0.0731', 'kfact=1.000', 'ref=Y'] ) # 0.6038*0.551*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M500'] .extend( ['xsec=0.0584', 'kfact=1.000', 'ref=Y'] ) # 0.4872*0.546*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M550'] .extend( ['xsec=0.0480', 'kfact=1.000', 'ref=Y'] ) # 0.3975*0.550*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M600'] .extend( ['xsec=0.0401', 'kfact=1.000', 'ref=Y'] ) # 0.3274*0.558*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M650'] .extend( ['xsec=0.0339', 'kfact=1.000', 'ref=Y'] ) # 0.2719*0.568*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M700'] .extend( ['xsec=0.0288', 'kfact=1.000', 'ref=Y'] ) # 0.2275*0.577*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M750'] .extend( ['xsec=0.0261', 'kfact=1.000', 'ref=Y'] ) # 0.1915*0.621*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M750_NWA'] .extend( ['xsec=0.0252', 'kfact=1.000', 'ref=KF'] ) # 0.186*0.621*0.1086*3*0.6741 - shall we keep the same BR for NWA? -samples['VBFHToWWToLNuQQ_M800'] .extend( ['xsec=0.0212', 'kfact=1.000', 'ref=Y'] ) # 0.1622*0.594*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M900'] .extend( ['xsec=0.0158', 'kfact=1.000', 'ref=Y'] ) # 0.1180*0.609*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M1000'] .extend( ['xsec=0.0119', 'kfact=1.000', 'ref=Y'] ) # 0.08732*0.621*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M1500'] .extend( ['xsec=0.00312', 'kfact=1.000', 'ref=Y'] ) # 0.02288*0.621*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M2000'] .extend( ['xsec=0.000962', 'kfact=1.000', 'ref=Y'] ) # 0.007052*0.621*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M2500'] .extend( ['xsec=0.000322', 'kfact=1.000', 'ref=Y'] ) # 0.002360*0.621*0.1086*3*0.6741 -samples['VBFHToWWToLNuQQ_M3000'] .extend( ['xsec=0.000113', 'kfact=1.000', 'ref=Y'] ) # 0.0008253*0.621*0.1086*3*0.6741 +samples['VBFHToWWToLNuQQ_M115'] .extend( ['xsec=0.1606', 'kfact=1.000', 'ref=Y'] ) # 4.255*0.0859*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M120'] .extend( ['xsec=0.2530', 'kfact=1.000', 'ref=Y'] ) # 4.086*0.141*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M124'] .extend( ['xsec=0.3458', 'kfact=1.000', 'ref=Y'] ) # 3.956*0.199*0.1086*3*0.6741*2 ### Interpolated Xsec +samples['VBFHToWWToLNuQQ_M125'] .extend( ['xsec=0.3706', 'kfact=1.000', 'ref=Y'] ) # 3.925*0.215*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M126'] .extend( ['xsec=0.3952', 'kfact=1.000', 'ref=Y'] ) # 3.894*0.231*0.1086*3*0.6741*2 ### Interpolated Xsec +samples['VBFHToWWToLNuQQ_M130'] .extend( ['xsec=0.5022', 'kfact=1.000', 'ref=Y'] ) # 3.773*0.303*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M135'] .extend( ['xsec=0.6376', 'kfact=1.000', 'ref=Y'] ) # 3.629*0.400*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M140'] .extend( ['xsec=0.7684', 'kfact=1.000', 'ref=Y'] ) # 3.492*0.501*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M145'] .extend( ['xsec=0.8860', 'kfact=1.000', 'ref=Y'] ) # 3.362*0.600*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M150'] .extend( ['xsec=0.9902', 'kfact=1.000', 'ref=Y'] ) # 3.239*0.696*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M155'] .extend( ['xsec=1.0902', 'kfact=1.000', 'ref=Y'] ) # 3.122*0.795*0.1086*3*0.6741*2 ### Interpolated Xsec +samples['VBFHToWWToLNuQQ_M160'] .extend( ['xsec=1.2004', 'kfact=1.000', 'ref=Y'] ) # 3.010*0.908*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M165'] .extend( ['xsec=1.2246', 'kfact=1.000', 'ref=Y'] ) # 2.904*0.960*0.1086*3*0.6741*2 ### Interpolated Xsec +samples['VBFHToWWToLNuQQ_M170'] .extend( ['xsec=1.1864', 'kfact=1.000', 'ref=Y'] ) # 2.802*0.964*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M175'] .extend( ['xsec=1.1382', 'kfact=1.000', 'ref=Y'] ) # 2.705*0.958*0.1086*3*0.6741*2 ### Interpolated Xsec +samples['VBFHToWWToLNuQQ_M180'] .extend( ['xsec=1.0692', 'kfact=1.000', 'ref=Y'] ) # 2.612*0.932*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M190'] .extend( ['xsec=0.8424', 'kfact=1.000', 'ref=Y'] ) # 2.440*0.786*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M200'] .extend( ['xsec=0.7428', 'kfact=1.000', 'ref=Y'] ) # 2.282*0.741*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M210'] .extend( ['xsec=0.6790', 'kfact=1.000', 'ref=Y'] ) # 2.138*0.723*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M230'] .extend( ['xsec=0.5858', 'kfact=1.000', 'ref=Y'] ) # 1.884*0.708*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M250'] .extend( ['xsec=0.5140', 'kfact=1.000', 'ref=Y'] ) # 1.669*0.701*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M270'] .extend( ['xsec=0.4546', 'kfact=1.000', 'ref=Y'] ) # 1.485*0.697*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M300'] .extend( ['xsec=0.3818', 'kfact=1.000', 'ref=Y'] ) # 1.256*0.692*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M350'] .extend( ['xsec=0.2870', 'kfact=1.000', 'ref=Y'] ) # 0.9666*0.676*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M400'] .extend( ['xsec=0.1938', 'kfact=1.000', 'ref=Y'] ) # 0.7580*0.582*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M450'] .extend( ['xsec=0.1462', 'kfact=1.000', 'ref=Y'] ) # 0.6038*0.551*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M500'] .extend( ['xsec=0.1168', 'kfact=1.000', 'ref=Y'] ) # 0.4872*0.546*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M550'] .extend( ['xsec=0.0960', 'kfact=1.000', 'ref=Y'] ) # 0.3975*0.550*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M600'] .extend( ['xsec=0.0802', 'kfact=1.000', 'ref=Y'] ) # 0.3274*0.558*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M650'] .extend( ['xsec=0.0678', 'kfact=1.000', 'ref=Y'] ) # 0.2719*0.568*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M700'] .extend( ['xsec=0.0576', 'kfact=1.000', 'ref=Y'] ) # 0.2275*0.577*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M750'] .extend( ['xsec=0.0522', 'kfact=1.000', 'ref=Y'] ) # 0.1915*0.621*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M750_NWA'] .extend( ['xsec=0.0504', 'kfact=1.000', 'ref=KF'] ) # 0.186*0.621*0.1086*3*0.6741*2 - shall we keep the same BR for NWA? +samples['VBFHToWWToLNuQQ_M800'] .extend( ['xsec=0.0424', 'kfact=1.000', 'ref=Y'] ) # 0.1622*0.594*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M900'] .extend( ['xsec=0.0316', 'kfact=1.000', 'ref=Y'] ) # 0.1180*0.609*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M1000'] .extend( ['xsec=0.0238', 'kfact=1.000', 'ref=Y'] ) # 0.08732*0.621*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M1500'] .extend( ['xsec=0.00624', 'kfact=1.000', 'ref=Y'] ) # 0.02288*0.621*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M2000'] .extend( ['xsec=0.001924', 'kfact=1.000', 'ref=Y'] ) # 0.007052*0.621*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M2500'] .extend( ['xsec=0.000644', 'kfact=1.000', 'ref=Y'] ) # 0.002360*0.621*0.1086*3*0.6741*2 +samples['VBFHToWWToLNuQQ_M3000'] .extend( ['xsec=0.000226', 'kfact=1.000', 'ref=Y'] ) # 0.0008253*0.621*0.1086*3*0.6741*2 samples['VBFHToTauTau_M120'] .extend( ['xsec=0.275264', 'kfact=1.000', 'ref=EF'] ) # 3.91*0.0704 samples['VBFHToTauTau_M125'] .extend( ['xsec=0.237000', 'kfact=1.000', 'ref=EF'] ) # 3.75*0.0632 diff --git a/ShapeAnalysis/python/PlotFactory.py b/ShapeAnalysis/python/PlotFactory.py index 34ce78a69..b900164ab 100644 --- a/ShapeAnalysis/python/PlotFactory.py +++ b/ShapeAnalysis/python/PlotFactory.py @@ -386,10 +386,11 @@ def makePlot(self, inputFile, outputDirPlots, variables, cuts, samples, plot, nu sigForAdditionalRatioList[sampleName] = histos[sampleName] sigForAdditionalDifferenceList[sampleName] = histos[sampleName] else : - nexpected += histos[sampleName].Integral(1,histos[sampleName].GetNbinsX()) # it was (-1, -1) in the past, correct now + nexpected += histos[sampleName].Integral(1,histos[sampleName].GetNbinsX()) # it was (-1, -1) in the past, correct now. Overflow and underflow bins not taken into account if variable['divideByBinWidth'] == 1: histos[sampleName].Scale(1,"width") + # this is really background only, meaning if "isSignal" is 1,2,3 then it's not included here thsBackground.Add(histos[sampleName]) #print " adding to background: ", sampleName @@ -484,8 +485,10 @@ def makePlot(self, inputFile, outputDirPlots, variables, cuts, samples, plot, nu # if you had self._SkipMissingNuisance set to true, put the variation the same as the nominal histoVar = histo.Clone(shapeNameVar.replace('/', '__')) nuisanceHistos[ivar][nuisanceName] = histoVar - - + + # + # I fill the nuisances_vy_up and nuisances_vy_do with the sum of all "up" and all "down" variations + # for ivar, nuisances_vy in enumerate([nuisances_vy_up, nuisances_vy_do]): for nuisanceName, nuisance in mynuisances.iteritems(): try: @@ -587,6 +590,11 @@ def makePlot(self, inputFile, outputDirPlots, variables, cuts, samples, plot, nu # you need to add the signal as well, since the signal was considered in the nuisances vector # otherwise you would introduce an uncertainty as big as the signal itself!!! # + # IF these lines below are commented it means that the uncertainty band is drawn only on bkg-only stacked historgram, + # meaning that you will get the dashed band on bkg stacked, and signal (typicalli empty red) drawn on top + # without the uncertainty band. + # Is this what you want? I hope so ... + # #if thsSignal.GetNhists() != 0: #for iBin in range(1,thsSignal.GetStack().Last().GetNbinsX()+1): #tgrMC_vy[iBin] += (thsSignal.GetStack().Last().GetBinContent(iBin)) @@ -606,7 +614,14 @@ def makePlot(self, inputFile, outputDirPlots, variables, cuts, samples, plot, nu # now we need to tell wthether the variation is actually up or down ans sum in quadrature those with the same sign up = nuisances_vy_up[nuisanceName] do = nuisances_vy_do[nuisanceName] + # + # NB: the test underneath is to be read "for each bin check if up[bin] > tgrMC_vy[bin] and store in an array of True or False + # In other words, up_is_up is an array([False, False, False]) + # up_is_up = (up > tgrMC_vy) + # + # this one above is an array of booleans + # dup2 = np.square(up - tgrMC_vy) ddo2 = np.square(do - tgrMC_vy) nuisances_err2_up += np.where(up_is_up, dup2, ddo2) @@ -615,6 +630,10 @@ def makePlot(self, inputFile, outputDirPlots, variables, cuts, samples, plot, nu nuisances_err_up = np.sqrt(nuisances_err2_up) nuisances_err_do = np.sqrt(nuisances_err2_do) + # + # NB: reminder: only background uncertainties have been considered in the nuisances, no impacts on the signal (isSignal = 1,2,3) + # + tgrData = ROOT.TGraphAsymmErrors(thsBackground.GetStack().Last().GetNbinsX()) for iBin in range(0, len(tgrData_vx)) : tgrData.SetPoint (iBin, tgrData_vx[iBin], tgrData_vy[iBin]) @@ -639,6 +658,7 @@ def makePlot(self, inputFile, outputDirPlots, variables, cuts, samples, plot, nu tgrDataOverPF = tgrData.Clone("tgrDataOverPF") # use this for ratio with Post-Fit MC histoPF = fileIn.Get(cutName+"/"+variableName+'/histo_total_postfit_b') + # at this stage "thsBackground" and then "last" includes ALSO the signal last = thsBackground.GetStack().Last() tgrDataOverMC = tgrData.Clone("tgrDataOverMC") @@ -658,6 +678,7 @@ def makePlot(self, inputFile, outputDirPlots, variables, cuts, samples, plot, nu # MC could be background only # or it can include the signal. # Default is background+signal (check isSignal = 1,2,3 options). + # tgrMC_vy is "background only" !! # You can activate the data - "background only" by # using the flag "showDataMinusBkgOnly". # NB: this will change also the case of "(data - expected) / expected" @@ -688,6 +709,9 @@ def makePlot(self, inputFile, outputDirPlots, variables, cuts, samples, plot, nu # and use directly the error bars (so far symmetric # see https://hypernews.cern.ch/HyperNews/CMS/get/higgs-combination/995.html ) # from the histogram itself + # + # NB: the post-fit plot includes the signal! + # # special_shapeName = cutName+"/"+variableName+'/histo_total' if type(fileIn) is dict: @@ -695,7 +719,7 @@ def makePlot(self, inputFile, outputDirPlots, variables, cuts, samples, plot, nu histo_total = fileIn['total'].Get(special_shapeName) else: histo_total = None - else: + else: histo_total = fileIn.Get(special_shapeName) if variable['divideByBinWidth'] == 1 and histo_total != None: @@ -708,6 +732,12 @@ def makePlot(self, inputFile, outputDirPlots, variables, cuts, samples, plot, nu for iBin in range(0, len(tgrMC_vx)) : tgrMC.SetPoint (iBin, tgrMC_vx[iBin], tgrMC_vy[iBin]) if histo_total: + # + # if there is histo_total, we plot the uncertainty band on top of sig+bkg, since histo_total IS sig+bkg and it's uncertainty accordingly + # This fix, in case of histo_total overrules the comment + # few lines above "Is this what you want? I hope so ..." + # + tgrMC.SetPoint(iBin, tgrMC_vx[iBin], histo_total.GetBinContent(iBin+1)) tgrMC.SetPointError(iBin, tgrMC_evx[iBin], tgrMC_evx[iBin], histo_total.GetBinError(iBin+1), histo_total.GetBinError(iBin+1)) else : tgrMC.SetPointError(iBin, tgrMC_evx[iBin], tgrMC_evx[iBin], nuisances_err_do[iBin], nuisances_err_up[iBin]) @@ -715,21 +745,39 @@ def makePlot(self, inputFile, outputDirPlots, variables, cuts, samples, plot, nu tgrMCOverMC = tgrMC.Clone("tgrMCOverMC") tgrMCMinusMC = tgrMC.Clone("tgrMCMinusMC") for iBin in range(0, len(tgrMC_vx)) : - tgrMCOverMC.SetPoint (iBin, tgrMC_vx[iBin], 1.) - tgrMCMinusMC.SetPoint (iBin, tgrMC_vx[iBin], 0.) + tgrMCOverMC.SetPoint (iBin, tgrMC_vx[iBin], 1.) # the ratio MC / MC is by construction 1 + tgrMCMinusMC.SetPoint (iBin, tgrMC_vx[iBin], 0.) # the difference MC - MC is by construction 0 if histo_total: - tgrMCOverMC.SetPointError(iBin, tgrMC_evx[iBin], tgrMC_evx[iBin], self.Ratio(histo_total.GetBinError(iBin+1), tgrMC_vy[iBin]), self.Ratio(histo_total.GetBinError(iBin+1), tgrMC_vy[iBin])) - if self._showRelativeRatio : - tgrMCMinusMC.SetPointError(iBin, tgrMC_evx[iBin], tgrMC_evx[iBin], self.Ratio(histo_total.GetBinError(iBin+1), tgrMC_vy[iBin]), self.Ratio(histo_total.GetBinError(iBin+1), tgrMC_vy[iBin])) + # histo_total include also the signal + tgrMCOverMC.SetPointError(iBin, tgrMC_evx[iBin], tgrMC_evx[iBin], self.Ratio(histo_total.GetBinError(iBin+1), last.GetBinContent(iBin+1)), self.Ratio(histo_total.GetBinError(iBin+1), last.GetBinContent(iBin+1) )) + if self._showDataMinusBkgOnly : + if self._showRelativeRatio : + tgrMCMinusMC.SetPointError(iBin, tgrMC_evx[iBin], tgrMC_evx[iBin], self.Ratio(histo_total.GetBinError(iBin+1), tgrMC_vy[iBin]), self.Ratio(histo_total.GetBinError(iBin+1), tgrMC_vy[iBin])) + else : + # ok, this should have been the error on background only, without signal, properly propagated ... in first approximation it's the uncertainty on sig+bkg = histo_total + tgrMCMinusMC.SetPointError(iBin, tgrMC_evx[iBin], tgrMC_evx[iBin], histo_total.GetBinError(iBin+1), histo_total.GetBinError(iBin+1)) else : - tgrMCMinusMC.SetPointError(iBin, tgrMC_evx[iBin], tgrMC_evx[iBin], histo_total.GetBinError(iBin+1), histo_total.GetBinError(iBin+1)) + if self._showRelativeRatio : + # not 100% sure if in the relative ratio I should put an uncertainty bar on the expected ... but I assume yes, as in the ratio plot, since expected-rate uncertainty is not propagated to "data" + tgrMCMinusMC.SetPointError(iBin, tgrMC_evx[iBin], tgrMC_evx[iBin], self.Ratio(histo_total.GetBinError(iBin+1), last.GetBinContent(iBin+1)), self.Ratio(histo_total.GetBinError(iBin+1), last.GetBinContent(iBin+1))) + else : + tgrMCMinusMC.SetPointError(iBin, tgrMC_evx[iBin], tgrMC_evx[iBin], histo_total.GetBinError(iBin+1), histo_total.GetBinError(iBin+1)) else : + # nuisances_err_do and nuisances_err_up do NOT include the signal + # thus in first approximation we say "uncertainty_(sig+bgk) / (sig+bkg) = uncertainty_(bkg) / bkg + # this is why everywhere here we have "tgrMC_vy[iBin]" instead of "last.GetBinContent(iBin+1)" tgrMCOverMC.SetPointError(iBin, tgrMC_evx[iBin], tgrMC_evx[iBin], self.Ratio(nuisances_err_do[iBin], tgrMC_vy[iBin]), self.Ratio(nuisances_err_up[iBin], tgrMC_vy[iBin])) - if self._showRelativeRatio : - tgrMCMinusMC.SetPointError(iBin, tgrMC_evx[iBin], tgrMC_evx[iBin], self.Ratio(nuisances_err_do[iBin], tgrMC_vy[iBin]), self.Ratio(nuisances_err_up[iBin], tgrMC_vy[iBin])) + if self._showDataMinusBkgOnly : + if self._showRelativeRatio : + tgrMCMinusMC.SetPointError(iBin, tgrMC_evx[iBin], tgrMC_evx[iBin], self.Ratio(nuisances_err_do[iBin], tgrMC_vy[iBin]), self.Ratio(nuisances_err_up[iBin], tgrMC_vy[iBin])) + else : + tgrMCMinusMC.SetPointError(iBin, tgrMC_evx[iBin], tgrMC_evx[iBin], nuisances_err_do[iBin], nuisances_err_up[iBin]) else : - tgrMCMinusMC.SetPointError(iBin, tgrMC_evx[iBin], tgrMC_evx[iBin], nuisances_err_do[iBin], nuisances_err_up[iBin]) - + if self._showRelativeRatio : + tgrMCMinusMC.SetPointError(iBin, tgrMC_evx[iBin], tgrMC_evx[iBin], self.Ratio(nuisances_err_do[iBin], tgrMC_vy[iBin]), self.Ratio(nuisances_err_up[iBin], tgrMC_vy[iBin])) + else : + tgrMCMinusMC.SetPointError(iBin, tgrMC_evx[iBin], tgrMC_evx[iBin], nuisances_err_do[iBin], nuisances_err_up[iBin]) + tgrRatioList = {}