10 - H1 - Desy

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152 to overtraining may be partially removed by the pruning (removing of statistically insignificant nodes) of decision trees. The advantage of the boosted decision tree method is its transparency and little tuning requirement. Inclusion of poorly discriminating input variables does not hurt the method in any way, while it may cause problems for other methods. Though in theory boosted decision tree should underperform in comparison to other, more theoretically advanced methods, in practice in many cases it is working better because either there are not enough training events to properly populate multi dimensional probability density function or the neural network architecture has not been correctly optimised. The main advantage of boosted decision tree method, in spite of its slowness and memory demand, is the simplicity and easiness of usage. All the methods described above have been to some extend trained and tested for their possible application in this analysis. All of them appeared to explore comparable discriminating performance, with some being significantly slower. The results obtained with two most efficient methods: maximum likelihood and neural network agree within 2%. One way to compare the performance of the different classifiers is to plot signal efficiency and background rejection determined for any possible cut introduced on the discriminator. The example of such a comparison for the first MVA bin in E γ T and the CB1 wheel is presented in figure 1. The further the graph tends towards the upper right corner (high signal efficiency, high background rejection power), the better the classifier. In the case of this analysis though all the classifiers perform equally well with the sole exception of the Support Vector Machine method, which probably was not tuned well enough. Background rejection 1 0.8 0.6 0.4 0.2 5.0 < E T Likelihood Range Search k-Nearest Neighbour Fisher discriminant Neural Network Support Vector Machine Boosted Decision Tree < 5.5 GeV CB1 0 0 0.2 0.4 0.6 0.8 1 Signal efficiency Figure 1: The signal efficiency versus background rejection graph for all studied classifiers in the first MVA E T bin and the CB1 wheel.
C Cross Section Tables 153 C Cross Section Tables η γ dσ/dη γ uncorr. corr. [pb] [−1.00 −0.57] 18.35 ±1.34 ±2.47 [−0.57 0.20] 23.94 ±1.53 ±1.46 [0.20 0.94] 27.69 ±1.18 ±2.31 [0.94 1.42] 19.35 ±1.27 ±3.02 [1.42 2.40] 11.01 ±0.97 ±3.46 Table C-1a: Differential cross sections in bins of η γ for inclusive prompt photon production in the phase space specified in the table 1.4. η γ f MPI theor f isol theor f had theor δ PDF [−1.00 −0.57] 0.97 +0.00 −0.03 1.02 1.00 ± 0.02 +2% −1% [−0.57 0.20] 0.95 +0.02 −0.03 1.02 0.98 ± 0.01 +6% −0% [0.20 0.94] 0.93 +0.02 −0.04 1.03 0.95 ± 0.00 +6% −0% [0.94 1.42] 0.92 +0.03 −0.04 1.05 0.92 ± 0.00 +7% −0% [1.42 2.40] 0.90 +0.02 −0.05 1.07 0.89 ± 0.01 +7% −0% Table C-1b: Corrections applied to the calculations in bins of η γ .
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PROMPT PHOTON PRODUCTION IN PHOTOPR
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Abstract This thesis presents measu
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x Contents 4 Event reconstruction a
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xii Contents
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xiv Contents the two photon decay c
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xvi Contents
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2 Theoretical framework Gene- Quark
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4 Theoretical framework charged W
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6 Theoretical framework Neutral and
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8 Theoretical framework α s 0.20 H
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10 Theoretical framework 1.2.4.1 DG
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12 Theoretical framework for the em
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14 Theoretical framework F 2 ⋅ 2
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- - 2) 9O9O9 4 - 70 6- - 7 - Q-
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18 Theoretical framework e e e a) b
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20 Theoretical framework than in th
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22 Theoretical framework ZEUS dσ/E
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24 Theoretical framework claim thou
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26 Theoretical framework Figure 1.2
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28 Theoretical framework Inclusive
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30 Theoretical predictions partons
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32 Theoretical predictions 2.1.2 MC
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34 Theoretical predictions rejectio
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MC set Generator Comments Simulatio
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38 Theoretical predictions contribu
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40 The H1 experiment at HERA Hall N
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42 The H1 experiment at HERA y θ z
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44 The H1 experiment at HERA resolu
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46 The H1 experiment at HERA the el
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48 The H1 experiment at HERA under
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50 The H1 experiment at HERA not be
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52 Event reconstruction and presele
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64 Prompt photon selection the rema
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66 Prompt photon selection ∆η <
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68 Prompt photon selection reconstr
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70 Prompt photon selection Events 5
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72 Prompt photon selection Selectio
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74 Photon signal extraction γ π0
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76 Photon signal extraction Events
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78 Photon signal extraction CB1 CB2
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80 Photon signal extraction Figure
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82 Photon signal extraction n∏ va
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84 Photon signal extraction 〈S〉
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86 Calibration and tuning • Backg
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88 Calibration and tuning χ 2 /NDF
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90 Calibration and tuning energy on
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92 Calibration and tuning D Ej 1.1
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94 Calibration and tuning
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96 Cross section building The corre
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98 Cross section building • L-cur
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100 Cross section building (Reconst
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Page 134 and 135: 118 Cross section building The doub
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Page 138 and 139: 122 Cross section building one outp
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Page 142 and 143: f f f f f f f f f f f f 126 Cross s
Page 144 and 145: 128 Cross section building PDF unce
Page 146 and 147: 130 Results description of the shap
Page 148 and 149: 132 Results 9.2 Prompt photon + jet
Page 150 and 151: 134 Results [pb] LO dσ / dx γ 100
Page 152 and 153: 136 Results 9.3 NLO corrections The
Page 154 and 155: 138 Results [pb/GeV] dσ / dp 15 10
Page 156 and 157: 140 Results transverse momentum imb
Page 158 and 159: 142 Results [pb/GeV] γ dσ / E T 2
Page 160 and 161: 144 Conclusions and outlook is cons
Page 163 and 164: A Analysis Binning 147 A Analysis B
Page 165 and 166: A Analysis Binning 149 Bin E γ T,O
Page 167: B Alternative classifier definition
Page 171 and 172: C Cross Section Tables 155 η γ E
Page 173 and 174: C Cross Section Tables 157 η γ d
Page 175 and 176: C Cross Section Tables 159 η jet d
Page 177 and 178: C Cross Section Tables 161 x LO γ
Page 179 and 180: C Cross Section Tables 163 x LO γ
Page 181 and 182: List of Figures 1.1 Lowest order Fe
Page 183 and 184: List of Figures 167 6.2 Shower shap
Page 185 and 186: List of Tables 169 List of Tables 1
Page 187 and 188: List of Tables 171 C-8b Corrections
Page 189 and 190: References [1] C. Amsler et al.,
Page 191 and 192: References 175 [32] R. Nisius, “T
Page 193 and 194: References 177 [63] P. A. Aarnio et
Page 195 and 196: References 179 [97] W. Braunschweig
Page 197 and 198: References 181 [129] J. M. Butterwo
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10 - H1 - Desy

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