Maximized atom number for a grating magneto-optical trap via machine-learning assisted parameter optimization.

Area of Science:

• Atomic, Molecular, and Optical Physics
• Quantum Optics
• Laser Physics

Background:

• Grating magneto-optical traps (gMOTs) are crucial for atom manipulation and cooling.
• Optimizing gMOT parameters is challenging due to complex, multi-dimensional parameter spaces.
• Existing methods for parameter optimization may not achieve maximum atom loading.

Purpose of the Study:

• To develop and apply a machine learning algorithm for optimizing the parameter set of a gMOT.
• To maximize the number of trapped atoms in a gMOT across various atomic species.
• To identify optimal grating reflectivity and diffraction angle for enhanced gMOT performance.

Main Methods:

• Utilized Bayesian optimization for efficient modeling within a high-dimensional parameter space.
• Employed Monte Carlo simulations to evaluate trap performance and atom number.
• Modeled gMOT performance for six atomic species: 7Li, 23Na, 87Rb, 88Sr, 133Cs, and 174Yb.

Main Results:

• Discovered that optimal grating reflectivity is consistently higher than estimations based on balanced optical molasses.
• Identified an optimal diffraction angle that is independent of the beam waist.
• The machine learning approach efficiently modeled the atom number in the gMOT.

Conclusions:

• The developed machine learning algorithm successfully optimizes gMOT parameters for maximum atom loading.
• Experimental verification for 87Rb confirmed the validity of the optimal parameter set.
• This work provides a pathway for enhancing atom trapping in gMOTs for diverse atomic species.