Recap of PDF fitting to the HERA average data Oct 12 th 2007 AMCS. Model dependence from assumptions about averaging procedure Model dependence from PDF parametrisation Model dependence from fitting programme! Free alphas Compare ZEUS and H1-style parametrisation.
Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.
Recap of PDF fitting to the HERA average data
Oct 12th 2007
Version of the combination agreed as standard in July was: all sources of error relative; y>0.35 for NC 820,920 not combined
Had considered variations on this see talk of 04june07
Illustrated here: only normalisations relative and NC 820,920 combined to 920
Conclusion model dependence from averaging procedure not large
To test model dependence from parametrisation – change Q02 from 7 GeV2 to 4 GeV2 to 2 GeV2
Changes are as expected – gluon gets a bit humpier, reflects instability at Q02=2
d valence is also affected
Sea and u valence stable
Conclusion: Model dependence not too bad- but certainly as large as changes due to the choices in our procedure – see talk of 22 june07
Model dependence – why chose Q02 as low as 2 GeV2? Because for QCDNUM17 you have to! It HAS to start below the charm threshold whether you chose NLO or NNLO.
These fits show only NLO.
There is a choice of linear spline interpolation (as for QCDNUM16) or Quadratic spline interpolation which is more accurate at high-x…and it makes a difference! See talk of 22June07
αS(MZ) =0.121 ± 0.003 χ2=500
Investigate scale dependence
Renormalisation scale usually the biggest so start with that
Scale= 2 Q2 αS(MZ) =0.123 χ2=502
Scale= 4Q2αS(MZ) =0.124 χ2=507
Scale= Q2/2 αS(MZ) =0.118 χ2=526
Scale= Q2/4 job blows up!
Scale dependence 0.002-0.003?
Not as bad as I thought!
Nature of the parametrization forces the errors on the low-x valence distributions to be very small- better to compare U,D
Central values of the fits are very close- good news I
haven’t added the charm into the sea for H1 in the plot, so it’s a bit low
Very similar χ2 ~ 500 for 584 points
xf(x) = A xa (1-x)b (1+ c x)
For u,d valence, sea and glue
Au,Ad,Ag from sum rules, ad=au, cs=0
xf(x) = A xa (1-x)b (1+ c x) (no x3 term)
For U=u+c, D=d+s, Ubar, Dbar and glue AU,AD,Ag from sum-rules,
cU=31 ± 2
bD=4.5 ± 0.22
cD=34 ± 8
bUbar = 10.8 ± 0.5
bDbar = 2.7 ± 0.9
ag= -0.115 ± 0.03
bg= 11 ± 5
cg= 12 ± 5
au=0.72 ± 0.04
bu=3.99 ± 0.13
bd=5.18 ± 0.72
cd=2.54 ± 2.02
As=0.63 ± 0.02
as= -0.197 ± 0.004
bs=6.85 ± 0.71
ag= -0.128 ± 0.02
bg= 11 ± 1
cg= 16 ± 5
Low-x sea similar
Looking back at old slides cg was sometimes written negative, sorry it is always positive!
Then we had the final decision to combine all data at 920, with all input errors relative AND the output had extra uncertainties from our procedures
Here I combined all output errors in quadrature for the PDF fit
Here I treat the 4 procedural errors by the OFFSET method for the PDF fit
Here I compare fits with Q2_min > 2.5 and Q2min > 1.5- since the fits to the lowets Q2 point didn’t look so goof
No of data points 584
NC e+ data points 371 χ2/dp = 0.93
No of data points 596
NC e+ data points 383 χ2/dp = 1.14
The shape of the low-x gluon is significantly affected
BUT the chisq for these low Q2 points is not good