Abstract
Next-generation focal planes comprising dozens of kilopixel transition-edge sensor (TES) arrays require new methods to rapidly screen candidate arrays, evaluate array non-idealities in the field, identify outlier devices for removal, and optimize the array performance in the field. We demonstrate robust methods to estimate TES parameters (critical temperatures and thermal conductivity parameters) and their uncertainties using a custom Markov Chain Monte Carlo (MCMC) algorithm. We also constrain systematic effects in estimating the TES parameters from non-isothermal current-voltage curves (IVs) at approximately a ∼3% level. Additionally, for the first time, we have applied Machine Learning (ML) algorithms to tune detector arrays and optimize their performance.
Original language | English (US) |
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Article number | 8688425 |
Journal | IEEE Transactions on Applied Superconductivity |
Volume | 29 |
Issue number | 5 |
DOIs | |
State | Published - Aug 2019 |
All Science Journal Classification (ASJC) codes
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Electrical and Electronic Engineering
Keywords
- Kilopixel focal planes
- Machine Learning
- Markov Chain Monte Carlo
- Transition-Edge Sensor