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Add CMS_ZPT_8TEV dataset #107

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Add CMS_ZPT_8TEV dataset #107

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@cschwan cschwan commented Sep 30, 2021 •
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This is the theory predictions for CMS's 8 TeV measurement of the Z pt spectrum. This dataset is not suited for the NLO EW fit due to subtracted FSR corrections, but we'll need it for other use cases.

@cschwan cschwan self-assigned this Sep 30, 2021
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cschwan commented Oct 1, 2021

The NNPDF APPLgrid does not include the normalization due to the bin size in rapidity (=0.4), however, the entry on hepdata does.

@alecandido alecandido mentioned this pull request Oct 2, 2021
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cschwan commented Oct 18, 2021

Here's a first comparison of the old APPLgrid with the PineAPPL grid (columns V2 and V3 are the lower and upper limit of the rapidity, V4 and V5 are the lower and upper limit of the transverse momentum, V6 is the double-differential cross section of the PineAPPL grid and V9 is the difference both grids in per mille):

    V2  V3  V4   V5           V6            V9
1  0.0 0.4   0   20 7.3575443000   -0.24298399
2  0.0 0.4  20   40 2.7162840000    0.34304938
3  0.0 0.4  40   60 0.9739845600   -0.08027204
4  0.0 0.4  60   80 0.4184366400    2.02984274
5  0.0 0.4  80  100 0.1982313400   -2.56155737
6  0.0 0.4 100  120 0.1010166300   -4.63845872
7  0.0 0.4 120  140 0.0546957900   -0.80216004
8  0.0 0.4 140  170 0.0275910360    1.31868610
9  0.0 0.4 170  200 0.0128957820   -5.95374092
10 0.0 0.4 200 1000 0.0005787779   13.76749978
11 0.4 0.8   0   20 7.2361391000   -0.51670746
12 0.4 0.8  20   40 2.6195250000    0.82525746
13 0.4 0.8  40   60 0.9352333600    0.92212762
14 0.4 0.8  60   80 0.4047753600    0.35058887
15 0.4 0.8  80  100 0.1926888400   -1.68335157
16 0.4 0.8 100  120 0.0985825870   -0.47336858
17 0.4 0.8 120  140 0.0532756770   -4.25111617
18 0.4 0.8 140  170 0.0270143280    0.87633970
19 0.4 0.8 170  200 0.0125729380   -0.87528143
20 0.4 0.8 200 1000 0.0005615205    8.61561564
21 0.8 1.2   0   20 6.6176019500   -8.94305236
22 0.8 1.2  20   40 2.3433441000   -3.18828691
23 0.8 1.2  40   60 0.8293658600   -3.92954799
24 0.8 1.2  60   80 0.3683807800   -5.71922113
25 0.8 1.2  80  100 0.1782149200    0.42520125
26 0.8 1.2 100  120 0.0915319710   -0.38282340
27 0.8 1.2 120  140 0.0500591500   -0.58097458
28 0.8 1.2 140  170 0.0251661680   -4.46740588
29 0.8 1.2 170  200 0.0118470820   -5.71481325
30 0.8 1.2 200 1000 0.0005159150   10.63734029
31 1.2 1.6   0   20 4.8927089500 -102.49115255
32 1.2 1.6  20   40 1.7484565000  -49.61593011
33 1.2 1.6  40   60 0.6359095300  -37.73161377
34 1.2 1.6  60   80 0.2883937700  -43.35903007
35 1.2 1.6  80  100 0.1450487900  -35.22640276
36 1.2 1.6 100  120 0.0770665090  -19.50632127
37 1.2 1.6 120  140 0.0426770140  -10.56646599
38 1.2 1.6 140  170 0.0218218630   -1.99214252
39 1.2 1.6 170  200 0.0101067590    2.81035697
40 1.2 1.6 200 1000 0.0004182991    9.89464650
41 1.6 2.0   0   20 2.5929016000 -258.68117149
42 1.6 2.0  20   40 0.9106254900 -245.93499456
43 1.6 2.0  40   60 0.3409659300 -176.06749189
44 1.6 2.0  60   80 0.1580456400 -152.15648817
45 1.6 2.0  80  100 0.0802362000 -159.80353299
46 1.6 2.0 100  120 0.0434567060 -165.55060361
47 1.6 2.0 120  140 0.0244784790 -175.10262573
48 1.6 2.0 140  170 0.0131288430 -143.33737668
49 1.6 2.0 170  200 0.0062035758 -109.87071509
50 1.6 2.0 200 1000 0.0002491195  -66.28780141

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cschwan commented Oct 18, 2021

The same comparison at LO:

    V2  V3  V4   V5           V6            V9
1  0.0 0.4   0   20 7.2749522000   -0.19553693
2  0.0 0.4  20   40 2.0117996000    0.39972735
3  0.0 0.4  40   60 0.6986707300   -0.29478731
4  0.0 0.4  60   80 0.2959139700    1.80943498
5  0.0 0.4  80  100 0.1387945700   -1.88254618
6  0.0 0.4 100  120 0.0700986200   -3.80692517
7  0.0 0.4 120  140 0.0376224270   -3.78318192
8  0.0 0.4 140  170 0.0188694070    0.85858013
9  0.0 0.4 170  200 0.0087575521   -7.36668388
10 0.0 0.4 200 1000 0.0003894766   15.26727964
11 0.4 0.8   0   20 7.1310180000   -0.19201144
12 0.4 0.8  20   40 1.9346250000    1.00533870
13 0.4 0.8  40   60 0.6687793600    0.60948095
14 0.4 0.8  60   80 0.2851983200   -0.32135722
15 0.4 0.8  80  100 0.1343937000   -1.07868491
16 0.4 0.8 100  120 0.0680401260    0.14370057
17 0.4 0.8 120  140 0.0365586190   -2.13122361
18 0.4 0.8 140  170 0.0183378850    0.07035112
19 0.4 0.8 170  200 0.0085071760    1.65852475
20 0.4 0.8 200 1000 0.0003750492    8.41168448
21 0.8 1.2   0   20 6.4799076000   -8.33969135
22 0.8 1.2  20   40 1.7222828000   -3.26213114
23 0.8 1.2  40   60 0.5891681400   -5.07681435
24 0.8 1.2  60   80 0.2578770500   -4.95233649
25 0.8 1.2  80  100 0.1230932600   -0.73114604
26 0.8 1.2 100  120 0.0627603170    1.64972920
27 0.8 1.2 120  140 0.0338649670   -0.39945688
28 0.8 1.2 140  170 0.0170082340    0.16165373
29 0.8 1.2 170  200 0.0078774206   -9.39395740
30 0.8 1.2 200 1000 0.0003391714   10.83231569
31 1.2 1.6   0   20 4.7255112000 -105.11045956
32 1.2 1.6  20   40 1.2753634000  -49.43617814
33 1.2 1.6  40   60 0.4485924500  -36.79511046
34 1.2 1.6  60   80 0.1993131400  -45.47903928
35 1.2 1.6  80  100 0.0991191610  -33.64963409
36 1.2 1.6 100  120 0.0521485880  -17.50938007
37 1.2 1.6 120  140 0.0285407320  -11.84416691
38 1.2 1.6 140  170 0.0144318840   -2.93697940
39 1.2 1.6 170  200 0.0066393508    8.00306682
40 1.2 1.6 200 1000 0.0002698966   10.16680687
41 1.6 2.0   0   20 2.4620252000 -258.38928742
42 1.6 2.0  20   40 0.6582238000 -244.67761971
43 1.6 2.0  40   60 0.2375599900 -174.84094951
44 1.6 2.0  60   80 0.1083313000 -147.45428679
45 1.6 2.0  80  100 0.0539679220 -158.43106923
46 1.6 2.0 100  120 0.0287154070 -169.49166611
47 1.6 2.0 120  140 0.0161457400 -171.17473218
48 1.6 2.0 140  170 0.0084072730 -155.58195985
49 1.6 2.0 170  200 0.0039758956 -115.39235471
50 1.6 2.0 200 1000 0.0001572227  -67.35041204

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cschwan commented Oct 18, 2021

The Madgraph5 runcards are missing a lepton-lepton distance cut of 0.4, which will probably improve the agreement for small rapidity and large transverse momenta, but not change the agreement for last two slices.

@cschwan cschwan mentioned this pull request Oct 18, 2021
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