Remote optimization of an ultra-cold atoms experiment by experts and citizen scientists

TL;DR

This paper presents a remote interface for optimizing Bose-Einstein condensate production, combining expert algorithms and citizen science, demonstrating effective collective search strategies and success in abstract problem visualization.

Contribution

It introduces a novel remote control system for BEC experiments, integrating expert algorithms and gamified citizen science, expanding the scope of gamification in scientific optimization.

Findings

Citizen scientists use combined local and global search strategies.

Gamified interface enables successful optimization of complex experiments.

Abstract problem visualization can be effectively used in citizen science.

Abstract

We introduce a novel remote interface to control and optimize the experimental production of Bose-Einstein condensates (BECs) and find improved solutions using two distinct implementations. First, a team of theoreticians employed a Remote version of their dCRAB optimization algorithm (RedCRAB), and second a gamified interface allowed 600 citizen scientists from around the world to participate in real-time optimization. Quantitative studies of player search behavior demonstrated that they collectively engage in a combination of local and global search. This form of adaptive search prevents premature convergence by the explorative behavior of low-performing players while high-performing players locally refine their solutions. In addition, many successful citizen science games have relied on a problem representation that directly engaged the visual or experiential intuition of the players. Here we demonstrate that citizen scientists can also be successful in an entirely abstract problem visualization. This gives encouragement that a much wider range of challenges could potentially be open to gamification in the future.