MOT loading optimisation

Automatically optimise the magneto-optical trap loading efficiency with Quatm’s parameter scans and multi-dimensional timing control.

Overview

This experiment script provides the class mot_settings: build() declares the full set of lithium MOT/CMOT devices and exposes the axial / radial magnetic-field currents and the MOT/CMOT detunings as GUI parameters, so that the magneto-optical trap loading can be optimised manually or by scanning. run() contains an empty timing sequence, on top of which imaging/counting can be added to assess the loading.

Devices (build excerpt)

Device	Purpose
`li_cooling_aom` / `li_repump_aom` / `li_zeeman_aom`	cooling / repump / Zeeman light switches
`li_cooling_power` / `li_repump_power` / `li_zeeman_power`	power of the individual beams
`li_lock_mot` / `li_lock_cmot`	frequency-lock references

Parameters (build excerpt)

MOT_Axial / _Radial, CMOT_Axial / _Radial (gradient/homogeneous-field currents), MOT_Current / CMOT_Current (trap currents), MOT_detuning / CMOT_detuning (detunings).

Key code (excerpt)

from quatm.experiment import Experiment, NumberValue

class mot_settings(Experiment):
    def build(self):
        self.setattr_device("li_cooling_power")
        self.setattr_argument("MOT_detuning", NumberValue(...))
        # ... remaining devices/parameters omitted

Loading optimisation

After changing the magnetic-field currents and the detuning, the loading efficiency can be judged from the atom number measured by fluorescence counting or absorption imaging (see Absorption imaging in the same directory).