Fast machine-learning online optimization of ultra-cold-atom experiments

Fast machine-learning online optimization of ultra-cold-atom experiments

We apply an online optimization process based on machine learning to the production of Bose-Einstein condensates (BEC). BEC is typically created with an exponential evaporation ramp that is optimal for ergodic dynamics with two-body s-wave interactions and no other loss rates, but likely sub-optimal for real experiments. Through repeated machine-controlled scientific experimentation and observa...

Electromagnet Design Basics for Cold Atom Experiments

Double-electron transfer with an ultra-cold atom target as a preparation for experiments at HITRAP

We have set up an ultra-cold target of rubidium atoms combined with a recoil ion momentum spectrometer for kinematically complete scattering experiments at the GSIHITRAP facility. First experiments will focus on multiple electron-transfer reactions in collisions of highly charged ions with many-electron atoms such as U + Rb. In particular, we want to investigate correlation effects in multielec...

Effects of Atom-Laser Interaction on Ultra-Cold Atoms

Ultra-Fast Optimization Algorithm for Sparse Multi Kernel Learning

Many state-of-the-art approaches for Multi Kernel Learning (MKL) struggle at finding a compromise between performance, sparsity of the solution and speed of the optimization process. In this paper we look at the MKL problem at the same time from a learning and optimization point of view. So, instead of designing a regularizer and then struggling to find an efficient method to minimize it, we de...