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Atom Cloud Detection and Segmentation Using a Deep Neural Network

ORAL

Abstract

We use a deep neural network to detect and place region-of-interest boxes around ultracold atom clouds in absorption and fluorescence images—with the ability to identify and bound multiple clouds within a single image. The neural network also outputs segmentation masks that identify the size, shape and orientation of each cloud from which we extract the clouds' Gaussian parameters. This allows 2D Gaussian fits to be reliably seeded thereby enabling fully automatic image processing. The method developed performs significantly better than a more conventional method based on a standardized image analysis library (Scikit-image) both for identifying regions-of-interest and extracting Gaussian parameters.

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Publication: Atom Cloud Detection Using a Deep Neural Network; arXiv:2011.1053<br>Atom Cloud Detection and Segmentation Using a Deep Neural Network; Machine Learning Science and Technology, submitted

Presenters

  • Lucas Hofer

    University of Oxford, Clarendon Laboratory, University of Oxford

Authors

  • Lucas Hofer

    University of Oxford, Clarendon Laboratory, University of Oxford

  • Milan Krstajic

    University of Cambridge, Cavendish Laboratory, University of Cambridge; Clarendon Laboratory, University of Oxford, Clarendon Laboratory, University of Oxford, Parks Road, Oxford OX1 3PU, United Kingdom ; Cavendish Laboratory, University of Cambridge, J. J. Thomson Avenue, Cambridge CB3 0HE

  • Péter Juhász

    University of Oxford, Clarendon Laboratory, University of Oxford, Clarendon Laboratory, University of Oxford, Parks Road, Oxford OX1 3PU, United Kingdom

  • Anna Marchant

    University of Oxford, Clarendon Laboratory, University of Oxford

  • Robert Smith

    University of Oxford, Clarendon Laboratory, University of Oxford, Parks Road, Oxford OX1 3PU, United Kingdom