Michael A. C. Johnson
Max Planck Society
8 Papers
5 Citations
Michael A. C. Johnson is an academic researcher from Max Planck Society. The author has contributed to research in topics: Galactic plane & Population. The author has an hindex of 2, co-authored 6 publications. Previous affiliations of Michael A. C. Johnson include University of Southampton.
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Papers
Prospecting for periods with LSST – low-mass X-ray binaries as a test case
Michael A. C. Johnson,Poshak Gandhi,Adriane Chapman,Luc Moreau,Philip A. Charles,Will Clarkson,A. B. Hill +6 more
TL;DR: In this article, the authors assess four candidate observing strategies for measurement of Porb in the range 10 min to 50 d. The results can be used in the ongoing assessment of the effectiveness of various potential cadencing strategies, and conservatively estimate that the most recent version of the LSST baseline strategy (baseline2018a) will allow Porb determination for ∼'18' per'cent of the Milky Way's X-ray binaries.
Peer Review
Rubin Observatory LSST Transients and Variable Stars Roadmap
Kelly Hambleton,Federica B. Bianco,Rachel Street,Keaton J. Bell,David A. H. Buckley,Melissa L. Graham,Nina Hernitschek,Michael B. Lund,Elena Mason,Joshua Pepper,Andrej Prsa,Markus Rabus,C. M. Raiteri,Róbert Szabó,Paula Szkody,Igor Andreoni,S. Antoniucci,Barbara Balmaverde,Eric C. Bellm,Rosaria Bonito,Giuseppe Bono,M. T. Botticella,Enzo Brocato,Katja Bricman,Enrico Cappellaro,M. I. Carnerero,Ryan Chornock,Riley W. Clarke,Philip S. Cowperthwaite,A. Cucchiara,Filippo D'Ammando,Kristen C. Dage,Massimo Dall'Ora,James R. A. Davenport,D. de Martino,Giuliana Somma,M. Di Criscienzo,Rosanne Di Stefano,Maria R. Drout,M. Fabrizio,Giuliana Fiorentino,Poshak Gandhi,A. Garofalo,Teresa Giannini,Andreja Gomboc,Laura Greggio,Patrick Hartigan,M. Hundertmark,Elizabeth N. Johnson,Michael A. C. Johnson,Tomislav Jurkić,Somayeh Khakpash,S. Leccia,Xiaolong Li,D. Magurno,Konstantin Malanchev,Marcella Marconi,Raffaella Margutti,S. Marinoni,Nicolas Mauron,Roberto Molinaro,Anaïs Marie Julie Møller,M. Moniez,Tatiana Muraveva,Ilaria Musella,Chow-Choong Ngeow,Andrea Pastorello,Vincenzo Petrecca,Silvia Piranomonte,F. Ragosta,A. Reguitti,Chiara Righi,Vincenzo Ripepi,L. E. Rivera Sandoval,Keivan G. Stassun,Michael Stroh,Giacomo Terreran,Virginia Trimble,Yiannis Tsapras,S. van Velzen,Laura Venuti,Jorick S. Vink +81 more
- 09 Aug 2022
TL;DR: The Vera C. Rubin Legacy Survey of Space and Time holds the potential to revolutionize time domain astrophysics, reaching completely unexplored areas of the Universe and mapping variability time scales from minutes to a decade as discussed by the authors .
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•Posted Content
The Plane’s The Thing: The Case for Wide-Fast-Deep Coverage of the Galactic Plane and Bulge
Jay Strader,Elias Aydi,Christopher Britt,Adam J. Burgasser,Poshak Gandhi,Dante Minniti,Michael C. Liu,Jacob W. Hogan,John E. Gizis,Michael A. C. Johnson,Thomas J. Maccarone,James T. Lauroesch,Jennifer Sobeck,Alexandre Roman-Lopez,Kirill Sokolovsky,Laura Chomiuk,Xilu Wang,Brian D. Fields,Will Clarkson,Léo Girardi,Peregrine McGehee,Koji Mukai,C. Tanner Murphey,Simone Scaringi +23 more
TL;DR: In this article, the authors argue that the exclusion of the Galactic Plane and Bulge from the uniform wide-fast-deep (WFD) LSST survey cadence is fundamentally inconsistent with two of the main science drivers of LSST: Mapping the Milky Way and Exploring the Transient Optical Sky.
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Using the Provenance from Astronomical Workflows to Increase Processing Efficiency
Michael A. C. Johnson,Luc Moreau,Adriane Chapman,Poshak Gandhi,Carlos Sáenz-Adán +4 more
- 09 Jul 2018
TL;DR: It is deduced that provenance has the potential to produce a net increase in this efficiency if more uses cases are to be considered and is highlighted in this work.
Pipeline Provenance for Analysis, Evaluation, Trust or Reproducibility
Michael A. C. Johnson,Hans-Rainer Klöckner,Albina Muzafarova,Kristen Lackeos,David J. Champion,Marta Dembska,Sirko Schindler,Marcus Paradies +7 more
TL;DR: PRAETOR is a software suite that enables automated generation, modeling, and analysis of provenance information of Python pipelines, and enables the first step of machine learning processes, where such information can be fed into dedicated optimization procedures.