Speaker
Description
We present a range of conceptual improvements and extensions to the popular
tuning tool "Professor".
Its core functionality remains the construction of multivariate analytic
approximations to an otherwise computationally expensive function. A typical
example would be histograms obtained from Monte-Carlo (MC) event generators for
standard model and new physics processes.
The fast Professor model then allows for numerical optimisation in a number of
different contexts such as chi-square minimisation and likelihood evaluation.
Previously, Professor was based on ordinary polynomials. Those, albeit highly
successful, showed limitations whenever the true functional form exhibited some
form of 1/x behaviour (e.g. due to masses in propagators). We describe our efforts to
replace the polynomials with rational, or "Pade", approximations as well as
radial basis functions (RBF).
Further, we introduce a new and better optimization routine that replaces the
gradient-based optimization inside Professor by an RBF-based approach that can
be shown to generate superior parameter fits.
We illustrate our improvements for the task of MC-generator tuning and limit setting.
