Machine Learning, Markov Chain Monte Carlo, and Optimal Algorithms to Characterize the AdvACT Kilopixel Transition-Edge Sensor Arrays

Maria Salatino, Jason Austermann, James A. Beall, Steve Choi, Kevin T. Crowley, Shannon Duff, Shawn W. Henderson, Gene Hilton, S. P.P. Ho, Johannes Hubmayr, Yaqiong Li, Michael D. Niemack, Sara M. Simon, Suzanne T. Staggs, Edward J. Wollack

Research output: Contribution to journalArticlepeer-review

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 languageEnglish (US)
Article number8688425
JournalIEEE Transactions on Applied Superconductivity
Volume29
Issue number5
DOIs
StatePublished - 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

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