Optimization of the Observing Cadence for the Rubin Observatory Legacy Survey of Space and Time: A Pioneering Process of Community-focused Experimental Design

The Astrophysical Journal Supplement Series American Astronomical Society 258:1 (2022) 1

Authors:

Federica B Bianco, Željko Ivezić, R Lynne Jones, Melissa L Graham, Phil Marshall, Abhijit Saha, Michael A Strauss, Peter Yoachim, Tiago Ribeiro, Timo Anguita, AE Bauer, Franz E Bauer, Eric C Bellm, Robert D Blum, William N Brandt, Sarah Brough, Márcio Catelan, William I Clarkson, Andrew J Connolly, Eric Gawiser, John E Gizis, Renée Hložek, Sugata Kaviraj, Charles T Liu, Michelle Lochner, Ashish A Mahabal, Rachel Mandelbaum, Peregrine McGehee, Eric H Neilsen, Knut AG Olsen, Hiranya V Peiris, Jason Rhodes, Gordon T Richards, Stephen Ridgway, Megan E Schwamb, Dan Scolnic, Ohad Shemmer, Colin T Slater, Anže Slosar, Stephen J Smartt, Jay Strader, Rachel Street, David E Trilling, Aprajita Verma, AK Vivas, Risa H Wechsler, Beth Willman

The Fornax Cluster VLT Spectroscopic Survey

Astronomy & Astrophysics EDP Sciences 657 (2022) a94

Authors:

NR Napolitano, M Gatto, C Spiniello, M Cantiello, M Hilker, M Arnaboldi, C Tortora, A Chaturvedi, R D’Abrusco, R Li, M Paolillo, R Peletier, T Saifollahi, M Spavone, A Venhola, M Capaccioli, G Longo

The Fornax Cluster VLT Spectroscopic Survey

Astronomy & Astrophysics EDP Sciences 657 (2022) a93

Authors:

Avinash Chaturvedi, Michael Hilker, Michele Cantiello, Nicola R Napolitano, Glenn van de Ven, Chiara Spiniello, Katja Fahrion, Maurizio Paolillo, Massimiliano Gatto, Thomas Puzia

WALLABY pilot survey: Public release of H i data for almost 600 galaxies from phase 1 of ASKAP pilot observations

Publications of the Astronomical Society of Australia Cambridge University Press (CUP) 39 (2022) e058

Authors:

T Westmeier, N Deg, K Spekkens, TN Reynolds, AX Shen, S Gaudet, S Goliath, MT Huynh, P Venkataraman, X Lin, T O’Beirne, B Catinella, L Cortese, H Dénes, A Elagali, B-Q For, GIG Józsa, C Howlett, JM van der Hulst, RJ Jurek, P Kamphuis, VA Kilborn, D Kleiner, BS Koribalski, K Lee-Waddell, C Murugeshan, J Rhee, P Serra, L Shao, L Staveley-Smith, J Wang, OI Wong, MA Zwaan, JR Allison, CS Anderson, Lewis Ball, DC-J Bock, D Brodrick, JD Bunton, FR Cooray, N Gupta, DB Hayman, EK Mahony, VA Moss, A Ng, SE Pearce, W Raja, DN Roxby, MA Voronkov, KA Warhurst, HM Courtois, K Said

Building high accuracy emulators for scientific simulations with deep neural architecture search

Machine Learning: Science and Technology IOP Science 3:1 (2021) 015013

Authors:

MF Kasim, D Watson-Parris, L Deaconu, S Oliver, Peter Hatfield, DH Froula, Gianluca Gregori, M Jarvis, Samar Khatiwala, J Korenaga, Jonas Topp-Mugglestone, E Viezzer, Sam Vinko

Abstract:

Computer simulations are invaluable tools for scientific discovery. However, accurate simulations are often slow to execute, which limits their applicability to extensive parameter exploration, large-scale data analysis, and uncertainty quantification. A promising route to accelerate simulations by building fast emulators with machine learning requires large training datasets, which can be prohibitively expensive to obtain with slow simulations. Here we present a method based on neural architecture search to build accurate emulators even with a limited number of training data. The method successfully emulates simulations in 10 scientific cases including astrophysics, climate science, biogeochemistry, high energy density physics, fusion energy, and seismology, using the same super-architecture, algorithm, and hyperparameters. Our approach also inherently provides emulator uncertainty estimation, adding further confidence in their use. We anticipate this work will accelerate research involving expensive simulations, allow more extensive parameters exploration, and enable new, previously unfeasible computational discovery.