Machine Learning for Achieving Bose-Einstein Condensation of Thulium Atoms

Abstract

Bose-Einstein condensation (BEC) is a powerful tool for a wide range of research activities, a large fraction of which are related to quantum simulations. Various problems may benefit from different atomic species, but cooling down novel species interesting for quantum simulations to BEC temperatures requires a substantial amount of optimization and is usually considered as a hard experimental task. In this work, we implemented the Bayesian machine learning technique to optimize the evaporative cooling of thulium atoms and achieved BEC in an optical dipole trap operating near 532 nm. The developed approach could be used to cool down other novel atomic species to quantum degeneracy without additional studies of their properties.

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• Polarized cold cloud of thulium atom
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• Scalar, tensor and vector polarizability of Tm atoms in 532 nm dipole trap

Cited by in corpus (28)

• Dipolar physics: A review of experiments with magnetic quantum gases
• Unsupervised machine learning of topological phase transitions from experimental data
• Laser cooling for quantum gases
• Machine-learning-accelerated Bose-Einstein condensation
• Bose-Einstein Condensation of Europium
• Single-site-resolved imaging of ultracold atoms in a triangular optical lattice
• Machine learner optimization of optical nanofiber-based dipole traps for cold ${}^{87}$Rb atoms
• Narrow-line magneto-optical trap for europium
• A two-dimensional magneto-optical trap of dysprosium atoms as a compact source for efficient loading of a narrow-line three-dimensional magneto-optical trap
• Active Learning Approach to Optimization of Experimental Control
• A Data Driven Sequential Learning Framework to Accelerate and Optimize Multi-Objective Manufacturing Decisions
• Characterizing the temperature dependence of Fano-Feshbach resonances of Ultracold Polarized Thulium
• High-dimensional reinforcement learning for optimization and control of ultracold quantum gases
• Vortex detection in atomic Bose-Einstein condensates using neural networks trained on synthetic images
• Microwave coherent spectroscopy of ultracold thulium atoms
• Deep Laser Cooling of Thulium Atoms to Sub-$\mu$K Temperatures in Magneto-Optical Trap
• Losses of thulium atoms from optical dipole traps operating at 532 and 1064 nm
• Simulating two-dimensional dynamics within a large-size atomic spin
• Review and experimental benchmarking of machine learning algorithms for efficient optimization of cold atom experiments
• New opportunites for interactions and control with ultracold lanthanides
• Bose-Einstein condensate as a diagnostic tool for an optical lattice formed by 1064 nm laser light
• Inspiration from machine learning on example of optimization of the Bose-Einstein condensate of thulium atoms in a 1064-nm trap
• Understanding one-body losses in magnetically trapped metastable europium atoms
• Transport of magnetically sensitive atoms in a magnetic environment
• Hyperfine spectroscopy of optical-cycling transitions in singly ionized thulium
• Efficient production of sodium Bose-Einstein condensates in a hybrid trap
• Factor of 1000 suppression of the depolarization rate in ultracold thulium collisions
• Tune-out wavelength for the thulium atom near 576 nm