Speaker
Description
We demonstrate a neural network training, capable of accounting for the effects of
systematic variations of the utilized data model in the training process and describe
its extension towards neural network multiclass classification. We show the importance
of adjusting backpropagation to be able to handle derivatives of histogram bins during
training and add an interpretation of the optimization process itself, highlighting
the differences between the systematic aware and conventional training strategies.
Trainings for binary and multiclass classification with seven output classes are
performed, based on a comprehensive data model with 86 nontrivial shape-altering
systematic variations, as used for a previous measurement. The neural network output
functions are used to infer the signal strengths for inclusive Higgs boson production,
as well as for Higgs boson production via gluon-fusion ($r_{\mathrm{ggH}}$) and vector
boson fusion ($r_{\mathrm{qqH}}$). With respect to a conventional training, based on
cross-entropy, we observe improvements of $12$ and $16\,\%$, for the sensitivity in
$r_{\mathrm{ggH}}$ and $r_{\mathrm{qqH}}$, respectively.
| Primary Field of Research | Machine Learning |
|---|
