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The case for a 'sub-millimeter SDSS': a 3D map of galaxy evolution to z~10

(2019)

Authors:

James E Geach, Manda Banerji, Frank Bertoldi, Matthieu Bethermin, Caitlin M Casey, Chian-Chou Chen, David L Clements, Claudia Cicone, Francoise Combes, Christopher Conselice, Asantha Cooray, Kristen Coppin, Emanuele Daddi, Helmut Dannerbauer, Romeel Dave, Matthew Doherty, James S Dunlop, Alastair Edge, Duncan Farrah, Maximilien Franco, Gary Fuller, Tracy Garratt, Walter Gear, Thomas R Greve, Evanthia Hatziminaoglou, Christopher C Hayward, Rob J Ivison, Ryohei Kawabe, Pamela Klaassen, Kirsten K Knudsen, Kotaro Kohno, Maciej Koprowski, Claudia DP Lagos, Georgios E Magdis, Benjamin Magnelli, Sean L McGee, Michal Michalowski, Tony Mroczkowski, Omid Noroozian, Seb Oliver, Dominik Riechers, Wiphu Rujopakarn, Douglas Scott, Stephen Serjeant, Matthew WL Smith, Mark Swinbank, Yoichi Tamura, Paul van der Werf, Eelco van Kampen, Aprajita Verma, Joaquin Vieira, Jeff Wagg, Fabian Walter, Lingyu Wang, Al Wootten, Min S Yun

LSST: From science drivers to reference design and anticipated data products

Astrophysical Journal American Astronomical Society 873:2 (2019) 111

Authors:

Z Ivezic, SM Kahn, JA Tyson, B Abel, E Acosta, R Allsman, D Alonso, Y Alsayyad, SF Anderson, J Andrew, JRP Angel, GZ Angeli, R Ansari, P Antilogus, C Araujo, R Armstrong, Kirk Arndt, P Astier, E Aubourg, N Auza, TS Axelrod, DJ Bard, JD Barr, A Barrau, JG Bartlett, AE Bauer, BJ Bauman, S Baumont, E Bechtol, K Bechtol, AC Becker, J Becla, C Beldica, S Bellavia, FB Bianco, R Biswas, G Blanc, J Blazek, RD Blandford, JS Bloom, J Bogart, TW Bond, MT Booth, AW Borgland, K Borne, JF Bosch, D Boutigny, CA Brackett, A Bradshaw, WN Brandt

Abstract:

We describe here the most ambitious survey currently planned in the optical, the Large Synoptic Survey Telescope (LSST). The LSST design is driven by four main science themes: probing dark energy and dark matter, taking an inventory of the solar system, exploring the transient optical sky, and mapping the Milky Way. LSST will be a large, wide-field ground-based system designed to obtain repeated images covering the sky visible from Cerro Pachón in northern Chile. The telescope will have an 8.4 m (6.5 m effective) primary mirror, a 9.6 deg2 field of view, a 3.2-gigapixel camera, and six filters (ugrizy) covering the wavelength range 320–1050 nm. The project is in the construction phase and will begin regular survey operations by 2022. About 90% of the observing time will be devoted to a deep-wide-fast survey mode that will uniformly observe a 18,000 deg2 region about 800 times (summed over all six bands) during the anticipated 10 yr of operations and will yield a co-added map to r ~ 27.5. These data will result in databases including about 32 trillion observations of 20 billion galaxies and a similar number of stars, and they will serve the majority of the primary science programs. The remaining 10% of the observing time will be allocated to special projects such as Very Deep and Very Fast time domain surveys, whose details are currently under discussion. We illustrate how the LSST science drivers led to these choices of system parameters, and we describe the expected data products and their characteristics.

Identification of low surface brightness tidal features in galaxies using convolutional neural networks

Monthly Notices of the Royal Astronomical Society Oxford University Press (OUP) 483:3 (2019) 2968-2982

Authors:

Mike Walmsley, Annette MN Ferguson, Robert G Mann, Chris J Lintott

Machine Learning for the Zwicky Transient Facility

PUBLICATIONS OF THE ASTRONOMICAL SOCIETY OF THE PACIFIC 131:997 (2019) ARTN 038002

Authors:

Ashish Mahabal, Umaa Rebbapragada, Richard Walters, Frank J Masci, Nadejda Blagorodnova, Jan van Roestel, Quan-Zhi Ye, Rahul Biswas, Kevin Burdge, Chan-Kao Chang, Dmitry A Duev, V Zach Golkhou, Adam A Miller, Jakob Nordin, Charlotte Ward, Scott Adams, Eric C Bellm, Doug Branton, Brian Bue, Chris Cannella, Andrew Connolly, Richard Dekany, Ulrich Feindt, Tiara Hung, Lucy Fortson, Sara Frederick, C Fremling, Suvi Gezari, Matthew Graham, Steven Groom, Mansi M Kasliwal, Shrinivas Kulkarni, Thomas Kupfer, Hsing Wen Lin, Chris Lintott, Ragnhild Lunnan, John Parejko, Thomas A Prince, Reed Riddle, Ben Rusholme, Nicholas Saunders, Nima Sedaghat, David L Shupe, Leo P Singer, Maayane T Soumagnac, Paula Szkody, Yutaro Tachibana, Kushal Tirumala, Sjoert van Velzen, Darryl Wright

Citizen science frontiers: Efficiency, engagement, and serendipitous discovery with human-machine systems.

Proceedings of the National Academy of Sciences of the United States of America 116:6 (2019) 1902-1909

Authors:

Laura Trouille, Chris J Lintott, Lucy F Fortson

Abstract:

Citizen science has proved to be a unique and effective tool in helping science and society cope with the ever-growing data rates and volumes that characterize the modern research landscape. It also serves a critical role in engaging the public with research in a direct, authentic fashion and by doing so promotes a better understanding of the processes of science. To take full advantage of the onslaught of data being experienced across the disciplines, it is essential that citizen science platforms leverage the complementary strengths of humans and machines. This Perspectives piece explores the issues encountered in designing human-machine systems optimized for both efficiency and volunteer engagement, while striving to safeguard and encourage opportunities for serendipitous discovery. We discuss case studies from Zooniverse, a large online citizen science platform, and show that combining human and machine classifications can efficiently produce results superior to those of either one alone and how smart task allocation can lead to further efficiencies in the system. While these examples make clear the promise of human-machine integration within an online citizen science system, we then explore in detail how system design choices can inadvertently lower volunteer engagement, create exclusionary practices, and reduce opportunity for serendipitous discovery. Throughout we investigate the tensions that arise when designing a human-machine system serving the dual goals of carrying out research in the most efficient manner possible while empowering a broad community to authentically engage in this research.