Speaker
jacob scott
Description
In HEP, we preform multidimensional integrals to compute observables to compare with experimental data. To do so, we use Monte Carlo integration -- it scales well with dimensionality but it suffers from a slow convergence rate. As such, it is important to reduce the variance of the result as much as possible and so many techniques have been created for this task. In this talk I will introduce one such method called "control variates" which, when applied to the existing vegas algorithm, returns better results than vegas alone.
Primary author
Co-authors
KC Kong
Konstantin Matchev
(University of Florida (US))
Prasanth Shyamsundar
(Fermi National Accelerator Laboratory)
Stephen Mrenna
(FERMILAB)
