Mar 10 – 15, 2019
Steinmatte conference center
Europe/Zurich timezone

Excursion Set Estimation using Sequential Entropy Reduction for Efficient Searches for New Physics at the LHC

Mar 13, 2019, 6:40 PM
Steinmatte Room A

Steinmatte Room A

Oral Track 2: Data Analysis - Algorithms and Tools Track 2: Data Analysis - Algorithms and Tools


Lukas Alexander Heinrich (New York University (US))


A common goal in the search for new physics is the determination of sets of New Physics models, typically parametrized by a number of parameters such as masses or couplings, that are either compatible with the observed data or excluded by it, where the determination into which category a given model belong requires expensive computation of the expected signal. This problem may be abstracted into the generalized problem of finding excursion sets (or, equivalently, iso-surfaces) of scalar, multivariate functions in $n$ dimensions.

We present an iterative algorithm for choosing points within the problem domain for which the functions are evaluated in order to estimate such sets at a significantly lower computational cost. The algorithm implements a Bayesian Optimization procedure, in which a information-based acquisition function seeks to maximally reduce the uncertainty on a excursion set. Further extension of the basic algorithm to the simultaneous estimation of excursion sets of multiple functions as well as batched selection of multiple points is presented.

Finally, a python package, excursion[1], is presented, which implements the algorithm and performance benchmarks are presented comparing this active-learning approach to other strategies commonly used in the high energy physics context, such as random sampling and grid searches.


Primary authors

Lukas Alexander Heinrich (New York University (US)) Gilles Louppe (New York University (US)) Kyle Stuart Cranmer (New York University (US))

Presentation materials