Atom Cloud Detection and Segmentation Using a Deep Neural Network

Machine Learning: Science and Technology IOP Publishing (2021)

Authors:

Lucas Hofer, Milan Krstajić, Peter Juhasz, Anna Marchant, Robert Smith

Atom Cloud Detection and Segmentation Using a Deep Neural Network (Data)

University of Oxford (2021)

Authors:

Lucas R Hofer, Milan Krstajic, Péter Juhász, Anna L Marchant, Robert P Smith

Abstract:

The data for the the paper "Atom Cloud Detection and Segmentation Using a Deep Neural Network." See the readme file in the .zip file for a detailed explanation of the data contents and structure.

Atom Cloud Detection Using a Deep Neural Network

(2020)

Authors:

Lucas R Hofer, Milan Krstajić, Péter Juhász, Anna L Marchant, Robert P Smith

Bidirectional dynamic scaling in an isolated Bose gas far from equilibrium

(2020)

Authors:

Jake AP Glidden, Christoph Eigen, Lena H Dogra, Timon A Hilker, Robert P Smith, Zoran Hadzibabic

Synthetic dissipation and cascade fluxes in a turbulent quantum gas

Science (2019)

Authors:

Nir Navon, Christoph Eigen, Jinyi Zhang, Raphael Lopes, Alexander L Gaunt, Kazuya Fujimoto, Makoto Tsubota, Robert P Smith, Zoran Hadzibabic

Abstract:

Scale-invariant fluxes are the defining property of turbulent cascades, but their direct measurement is a challenging experimental problem. Here we perform such a measurement for a direct energy cascade in a turbulent quantum gas. Using a time-periodic force, we inject energy at a large lengthscale and generate a cascade in a uniformly-trapped three-dimensional Bose gas. The adjustable trap depth provides a high-momentum cutoff kD, which realizes a synthetic dissipation scale. This gives us direct access to the particle flux across a momentum shell of radius kD, and the tunability of kD allows for a clear demonstration of the zeroth law of turbulence. Moreover, our time-resolved measurements give unique access to the pre-steady-state dynamics, when the cascade front propagates in momentum space.