In this video, we consider two examples of usage the

Bayesian optimization with Gaussian processes in real high energy physics experiments.

The first example is a muon shield optimization,

is the SHiP experiment at CERN.

The SHiP experiment is a new general purpose fixed target facility proposed at

the CERN SPS accelerator to search for new physics in

the largely explored domain of very weakly interacting particles.

The moun shield is a critical component of the SHiP experiment,

which deflects the high flux of muons produced in the target,

that would represent a very serious background for the particles searches,

away from the detector.

The shield consist of eight magnets and each magnet is parameterized by seven values.

The loss function depends on the physical performance of the shield and its weight.

So, it is a 42-dimensional optimization problem and

optimal moun shield geometry was found using 5000 iterations,

using the Bayesian optimization with Gaussian processes.

The optimal geometry of the shield is shown on the slide.

It's 25 percent lighter and as a result 25 percent cheaper.

Next example is about collisions simulation optimization.

Bayesian methods are used to optimize parameters of heavy-ion collision simulation.

Evaluating a simulation model for a single set of

parameters requires thousands of individual event simulations,

so direct optimization techniques,

like grid search, quickly become intractable.

This slide just shows simulated examples of

entropy density in the transverse plane for several,

typical ion-ion collision events.

With optimization, 300 initial points were generated in nine-dimensional parameter space.

These parameters are listed in the table on the slide.

For each of these 300 points,

about 10,000 of events were executed.

Figure on this slide demonstrates validation of Gaussian process emulator predictions.

Each panel shows predictions compared to explicit model calculations,

at a 50 validation design points.

This figure shows the quality of the simulation models with the optimal parameters.

Lines correspond to different simulation models with optimal parameters.

Points are data from the ALICE experiment.

In the bottom, it's a ratio of model calculations to data,

where the gray band indicates +/- 10 percent.

In this video, we considered that just several examples of using

Bayesian optimization methods in high energy physics.

In the programming assignment for this week,

you will be able to find optimal design for a simple tracking system.