Daniel de Florian

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Uncertainties Use Lagrange multipliers technique to estimate uncertainties (from exp. errors) on some observables ∆χ 2 n Φ(λi, {aj}) = χ 2 ({aj}) + � i λi Oi({aj}) See how fit <strong>de</strong>teriorates when FFs forced to give different prediction for Oi should be parabolic if data set can <strong>de</strong>termine the observable (otherwise monotonic o flat) We study truncated moments: � 1 0.2 z D H i (z, Q = 5 GeV) dz
# 2 # 2 860 855 850 845 860 855 850 845 "+ ! g "+ ! u+u – ! u – "+ "+ ! c+c – 0.9 1 1.1 0.9 1 1.1 !/! 0 !/! 0 u < 5% s ~ 10 % ∆χ 2 = 15(∼ 2%) "+ ! s+s – "+ ! – b+b 0.9 1 !/! 0 1.1 dominated by z~0.2 Uncertainties #$ 2 #$ i 15 10 5 0 -5 "+ ! u+u – 0.98 1 1.02 #$ 2 #$ TOTAL #$ 2 #$ e+e- #$ 2 #$ HERMES #$ 2 #$ OPAL #$ PHENIX 2 #$ STAR 2 #$ BRAHMS 2 Tension Individual profiles !/! 0 "+ ! g "+ ! s+s – 0.9 1 1.1 Complementarity Constrained parabola as a result of global fit 15 10 5 0 -5 15 10 5 0 -5 Precision RHIC
Page 1 and 2: Global Analysis of Fragmentation Fu
Page 3 and 4: Fragmentation functions Represent t
Page 5 and 6: parabola and the 1σ uncertainty in
Page 7 and 8: Advantages Very precise data from L
Page 9 and 10: SIDIS Advantages : allows flavor/ch
Page 11 and 12: Global FIT Advantages : Constrain F
Page 13 and 14: nt of experimental Technical detail
Page 15 and 16: SIDIS ad-hoc charge separation from
Page 17 and 18: 1 10 -1 10 -2 10 -3 10 -4 10 -5 0.5
Page 19 and 20: 1.4 1.2 1 0.8 0.6 0.4 0.2 2 1.5 1 0
Page 21: 0.3 0.25 0.2 0.15 0.1 0.05 2 1.5 1

Daniel de Florian

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