Isotropic-Nematic Phase Transitions in Gravitational Systems

(2017)

Authors:

Zacharias Roupas, Bence Kocsis, Scott Tremaine

Feedback-regulated star formation and escape of LyC photons from mini-haloes during reionization

MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY 466:4 (2017) 4826-4846

Authors:

T Kimm, H Katz, M Haehnelt, J Rosdahl, J Devriendt, A Slyz

The interstellar medium in high-redshift submillimeter galaxies as probed by infrared spectroscopy

(2017)

Authors:

Julie L Wardlow, Asantha Cooray, Willow Osage, Nathan Bourne, David Clements, Helmut Dannerbauer, Loretta Dunne, Simon Dye, Steve Eales, Duncan Farrah, Cristina Furlanetto, Edo Ibar, Rob Ivison, Steve Maddox, Michał M Michałowski, Dominik Riechers, Dimitra Rigopoulou, Douglas Scott, Matthew WL Smith, Lingyu Wang, Paul van der Werf, Elisabetta Valiante, Ivan Valtchanov, Aprajita Verma

The European Far-Infrared Space Roadmap

(2017)

Authors:

D Rigopoulou, F Helmich, L Hunt, J Goicoechea, P Hartogh, D Fedele, M Matsuura, L Spinoglio, D Elbaz, M Griffin, GL Pilbratt, E Chapillon

A fast machine learning based algorithm for MKID readout power tuning

ISSTT 2017 - 28th International Symposium on Space Terahertz Technology 2017-March (2017)

Authors:

RH Dodkins, K O'Brien, N Thatte, S Mahashabde, N Fruitwala, S Meeker, A Walter, P Szypryt, B Mazin

Abstract:

As high pixel count Microwave Kinetic Inductance Detector (MKID) arrays become widely adopted, there is a growing demand for automated device readout calibration. These calibrations include ascertaining the optimal driving power for best pixel sensitivity, which, because of large variations in MKID behavior, is typically performed by manual inspection. This process takes roughly 1 hour per 1000 MKIDs, making the manual characterization of ten-kilopixel scale arrays unfeasible. We propose the concept of using a machine-learning algorithm, based on a convolution neural network (CNN) architecture, which should reliably tune ten-kilopixel scale MKID arrays on the order of several minutes.