Remote optimization of an ultracold atoms experiment by experts and citizen scientists

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Documents

  • E11231.full

    Final published version, 965 KB, PDF document

DOI

We introduce a 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 used a remote version of their dressed chopped random basis 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 searches. This form of multiagent 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 is encouraging because a much wider range of challenges could potentially be opened to gamification in the future.

Original languageEnglish
JournalProceedings of the National Academy of Sciences of the United States of America
Volume115
Issue48
Pages (from-to)E11231-E11237
Number of pages7
ISSN0027-8424
DOIs
Publication statusPublished - 27 Nov 2018

    Research areas

  • citizen science, optimal control, ultracold atoms, human problem solving, closed-loop optimization, QUANTUM, ADAPTATION

See relations at Aarhus University Citationformats

Download statistics

No data available

ID: 138259231