2018
DOI: 10.1016/j.ascom.2018.03.001
|View full text |Cite
|
Sign up to set email alerts
|

MKID digital readout tuning with deep learning

Abstract: The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that: • a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1

Citation Types

0
3
0

Year Published

2019
2019
2020
2020

Publication Types

Select...
4

Relationship

1
3

Authors

Journals

citations
Cited by 4 publications
(3 citation statements)
references
References 25 publications
(30 reference statements)
0
3
0
Order By: Relevance
“…This is determined by doing frequency sweeps of every feedline at a grid of powers and then using a machine learning code to both find the resonators and determine their optimal probe powers. An algorithm to find the probe power of known resonators is outlined in Dodkins et al (2018), but has been superseded by an algorithm that simultaneously finds resonances and their ideal power which will be detailed in an upcoming publication. Our python software then computes a lookup table (LUT) containing the time-domain sum of the resonator probe tones.…”
Section: Readout Proceduresmentioning
confidence: 99%
“…This is determined by doing frequency sweeps of every feedline at a grid of powers and then using a machine learning code to both find the resonators and determine their optimal probe powers. An algorithm to find the probe power of known resonators is outlined in Dodkins et al (2018), but has been superseded by an algorithm that simultaneously finds resonances and their ideal power which will be detailed in an upcoming publication. Our python software then computes a lookup table (LUT) containing the time-domain sum of the resonator probe tones.…”
Section: Readout Proceduresmentioning
confidence: 99%
“…Each pixel has a background phase height noise from twolevel system states (Gao et al 2008), and amplifier noise (Zobrist et al 2019). Additionally, the phase height responsivity, r = λ/φ, varies from pixel to pixel depending on their quality factor and consequently their bias power (Dodkins et al 2018). Only when the phase height signal to noise ratio (S/N) exceeds a defined threshold is the photon data stored to disk by the readout electronics.…”
Section: Quantizationmentioning
confidence: 99%
“…The number of functioning pixels is however lower than this value. Pixels are lost because of fabrication uncertainties leading to the spectral profile of two or more pixels overlapping, pixels having low responsivity, or pixels being driven with too much readout power Dodkins et al (2018). The dead_pix Boolean dictates whether or not to apply the pix_yield parameter.…”
Section: A3 Mkids Parametersmentioning
confidence: 99%