We present a preliminary strategy for modeling multidimensional distributions through neural networks. We study the efficiency of the proposed strategy by considering as input data the two-dimensional next-to-next leading order (NNLO) jet kappa-factors distribution for the ATLAS 7 TeV 2011 data. We then validate the neural network model in terms of interpolation and prediction quality by comparing its results to alternative models.
Modelling NNLO jet corrections with neural networks / S. Carrazza. - In: ACTA PHYSICA POLONICA B. - ISSN 0587-4254. - 48:6(2017 Jun), pp. 947-954. ((Intervento presentato al convegno Cracow Epiphany Conference on Particle Theory Meets the First Data from LHC Run 2 tenutosi a Krakow nel 2017 [10.5506/APhysPolB.48.947].
Modelling NNLO jet corrections with neural networks
S. Carrazza
2017
Abstract
We present a preliminary strategy for modeling multidimensional distributions through neural networks. We study the efficiency of the proposed strategy by considering as input data the two-dimensional next-to-next leading order (NNLO) jet kappa-factors distribution for the ATLAS 7 TeV 2011 data. We then validate the neural network model in terms of interpolation and prediction quality by comparing its results to alternative models.
| File | Dimensione | Formato | |
|---|---|---|---|
|
v48p0947.pdf
accesso aperto
Tipologia:
Publisher's version/PDF
Dimensione
2.02 MB
Formato
Adobe PDF
|
2.02 MB | Adobe PDF | Visualizza/Apri |
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
