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1. Data management and execution systems for the Rubin Observatory Science Pipelines

Author

Lust, Nate B., Jenness, Tim, Bosch, James F., Salnikov, Andrei, Pease, Nathan M., Gower, Michelle, Kowalik, Mikolaj, Dubois-Felsmann, Gregory P., Mueller, Fritz, and Schellart, Pim

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

We present the Rubin Observatory system for data storage/retrieval and pipelined code execution. The layer for data storage and retrieval is named the Butler. It consists of a relational database, known as the registry, to keep track of metadata and relations, and a system to manage where the data is located, named the datastore. Together these systems create an abstraction layer that science algorithms can be written against. This abstraction layer manages the complexities of the large data volumes expected and allows algorithms to be written independently, yet be tied together automatically into a coherent processing pipeline. This system consists of tools which execute these pipelines by transforming them into execution graphs which contain concrete data stored in the Butler. The pipeline infrastructure is designed to be scalable in nature, allowing execution on environments ranging from a laptop all the way up to multi-facility data centers. This presentation will focus on the data management aspects as well as an overview on the creation of pipelines and the corresponding execution graphs., Comment: 4 pages, submitted to Astronomical Data Analysis Software and Systems XXXII, October 2022

Published
2023

2. Adding Workflow Management Flexibility to LSST Pipelines Execution

Author

Gower, Michelle, Kowalik, Mikolaj, Lust, Nate B., Bosch, James F., and Jenness, Tim

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

Data processing pipelines need to be executed at scales ranging from small runs up through large production data release runs resulting in millions of data products. As part of the Rubin Observatory's pipeline execution system, BPS is the abstraction layer that provides an interface to different Workflow Management Systems (WMS) such as HTCondor and PanDA. During the submission process, the pipeline execution system interacts with the Data Butler to produce a science-oriented execution graph from algorithmic tasks. BPS converts this execution graph to a workflow graph and then uses a WMS-specific plugin to submit and manage the workflow. Here we will discuss the architectural design of this interface and report briefly on the recent production of the Data Preview 0.2 release and how the system is used by pipeline developers., Comment: 4 pages, submitted to Astronomical Data Analysis Software and Systems XXXII, October 2022

Published
2022

3. The Vera C. Rubin Observatory Data Butler and Pipeline Execution System

Author

Jenness, Tim, Bosch, James F., Lust, Nate B., Pease, Nathan M., Gower, Michelle, Kowalik, Mikolaj, Dubois-Felsmann, Gregory P., Mueller, Fritz, and Schellart, Pim

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

The Rubin Observatory's Data Butler is designed to allow data file location and file formats to be abstracted away from the people writing the science pipeline algorithms. The Butler works in conjunction with the workflow graph builder to allow pipelines to be constructed from the algorithmic tasks. These pipelines can be executed at scale using object stores and multi-node clusters, or on a laptop using a local file system. The Butler and pipeline system are now in daily use during Rubin construction and early operations., Comment: 14 pages, 3 figures, submitted to Proc SPIE 12189, "Software and Cyberinfrastructure for Astronomy VII", Montreal, CA, July 2022

Published
2022

4. Abstracting the storage and retrieval of image data at the LSST

Author

Jenness, Tim, Bosch, James F., Schellart, Pim, Lim, Kian-Ta, Salnikov, Andrei, and Gower, Michelle

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

Writing generic data processing pipelines requires that the algorithmic code does not ever have to know about data formats of files, or the locations of those files. At LSST we have a software system known as "the Data Butler," that abstracts these details from the software developer. Scientists can specify the dataset they want in terms they understand, such as filter, observation identifier, date of observation, and instrument name, and the Butler translates that to one or more files which are read and returned to them as a single Python object. Conversely, once they have created a new dataset they can give it back to the Butler, with a label describing its new status, and the Butler can write it in whatever format it has been configured to use. All configuration is in YAML and supports standard defaults whilst allowing overrides., Comment: 4 pages, 1 figure, submitted to proceedings of ADASS XXVIII to be published in ASP Conf. Series

Published
2018

5. LSST: From Science Drivers to Reference Design and Anticipated Data Products

Author

Ivezić, Željko, Kahn, Steven M, Tyson, J Anthony, Abel, Bob, Acosta, Emily, Allsman, Robyn, Alonso, David, AlSayyad, Yusra, Anderson, Scott F, Andrew, John, Angel, James Roger P, Angeli, George Z, Ansari, Reza, Antilogus, Pierre, Araujo, Constanza, Armstrong, Robert, Arndt, Kirk T, Astier, Pierre, Aubourg, Éric, Auza, Nicole, Axelrod, Tim S, Bard, Deborah J, Barr, Jeff D, Barrau, Aurelian, Bartlett, James G, Bauer, Amanda E, Bauman, Brian J, Baumont, Sylvain, Bechtol, Ellen, Bechtol, Keith, Becker, Andrew C, Becla, Jacek, Beldica, Cristina, Bellavia, Steve, Bianco, Federica B, Biswas, Rahul, Blanc, Guillaume, Blazek, Jonathan, Blandford, Roger D, Bloom, Josh S, Bogart, Joanne, Bond, Tim W, Booth, Michael T, Borgland, Anders W, Borne, Kirk, Bosch, James F, Boutigny, Dominique, Brackett, Craig A, Bradshaw, Andrew, Brandt, William Nielsen, Brown, Michael E, Bullock, James S, Burchat, Patricia, Burke, David L, Cagnoli, Gianpietro, Calabrese, Daniel, Callahan, Shawn, Callen, Alice L, Carlin, Jeffrey L, Carlson, Erin L, Chandrasekharan, Srinivasan, Charles-Emerson, Glenaver, Chesley, Steve, Cheu, Elliott C, Chiang, Hsin-Fang, Chiang, James, Chirino, Carol, Chow, Derek, Ciardi, David R, Claver, Charles F, Cohen-Tanugi, Johann, Cockrum, Joseph J, Coles, Rebecca, Connolly, Andrew J, Cook, Kem H, Cooray, Asantha, Covey, Kevin R, Cribbs, Chris, Cui, Wei, Cutri, Roc, Daly, Philip N, Daniel, Scott F, Daruich, Felipe, Daubard, Guillaume, Daues, Greg, Dawson, William, Delgado, Francisco, Dellapenna, Alfred, de Peyster, Robert, de Val-Borro, Miguel, Digel, Seth W, Doherty, Peter, Dubois, Richard, Dubois-Felsmann, Gregory P, Durech, Josef, Economou, Frossie, Eifler, Tim, Eracleous, Michael, Emmons, Benjamin L, and Neto, Angelo Fausti

Subjects
Astronomical Sciences, Physical Sciences, astrometry, cosmology: observations, Galaxy: general, methods: observational, stars: general, surveys, astro-ph, Astronomical and Space Sciences, Atomic, Molecular, Nuclear, Particle and Plasma Physics, Physical Chemistry (incl. Structural), Astronomy & Astrophysics, Astronomical sciences, Particle and high energy physics, Space sciences
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 deg 2 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 deg 2 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.

Published
2019

6. LSST: from Science Drivers to Reference Design and Anticipated Data Products

Author

Ivezić, Željko, Kahn, Steven M., Tyson, J. Anthony, Abel, Bob, Acosta, Emily, Allsman, Robyn, Alonso, David, AlSayyad, Yusra, Anderson, Scott F., Andrew, John, Angel, James Roger P., Angeli, George Z., Ansari, Reza, Antilogus, Pierre, Araujo, Constanza, Armstrong, Robert, Arndt, Kirk T., Astier, Pierre, Aubourg, Éric, Auza, Nicole, Axelrod, Tim S., Bard, Deborah J., Barr, Jeff D., Barrau, Aurelian, Bartlett, James G., Bauer, Amanda E., Bauman, Brian J., Baumont, Sylvain, Becker, Andrew C., Becla, Jacek, Beldica, Cristina, Bellavia, Steve, Bianco, Federica B., Biswas, Rahul, Blanc, Guillaume, Blazek, Jonathan, Blandford, Roger D., Bloom, Josh S., Bogart, Joanne, Bond, Tim W., Borgland, Anders W., Borne, Kirk, Bosch, James F., Boutigny, Dominique, Brackett, Craig A., Bradshaw, Andrew, Brandt, William Nielsen, Brown, Michael E., Bullock, James S., Burchat, Patricia, Burke, David L., Cagnoli, Gianpietro, Calabrese, Daniel, Callahan, Shawn, Callen, Alice L., Chandrasekharan, Srinivasan, Charles-Emerson, Glenaver, Chesley, Steve, Cheu, Elliott C., Chiang, Hsin-Fang, Chiang, James, Chirino, Carol, Chow, Derek, Ciardi, David R., Claver, Charles F., Cohen-Tanugi, Johann, Cockrum, Joseph J., Coles, Rebecca, Connolly, Andrew J., Cook, Kem H., Cooray, Asantha, Covey, Kevin R., Cribbs, Chris, Cui, Wei, Cutri, Roc, Daly, Philip N., Daniel, Scott F., Daruich, Felipe, Daubard, Guillaume, Daues, Greg, Dawson, William, Delgado, Francisco, Dellapenna, Alfred, de Peyster, Robert, de Val-Borro, Miguel, Digel, Seth W., Doherty, Peter, Dubois, Richard, Dubois-Felsmann, Gregory P., Durech, Josef, Economou, Frossie, Eracleous, Michael, Ferguson, Henry, Figueroa, Enrique, Fisher-Levine, Merlin, Focke, Warren, Foss, Michael D., Frank, James, Freemon, Michael D., Gangler, Emmanuel, Gawiser, Eric, Geary, John C., Gee, Perry, Geha, Marla, Gessner, Charles J. B., Gibson, Robert R., Gilmore, D. Kirk, Glanzman, Thomas, Glick, William, Goldina, Tatiana, Goldstein, Daniel A., Goodenow, Iain, Graham, Melissa L., Gressler, William J., Gris, Philippe, Guy, Leanne P., Guyonnet, Augustin, Haller, Gunther, Harris, Ron, Hascall, Patrick A., Haupt, Justine, Hernandez, Fabio, Herrmann, Sven, Hileman, Edward, Hoblitt, Joshua, Hodgson, John A., Hogan, Craig, Huang, Dajun, Huffer, Michael E., Ingraham, Patrick, Innes, Walter R., Jacoby, Suzanne H., Jain, Bhuvnesh, Jammes, Fabrice, Jee, James, Jenness, Tim, Jernigan, Garrett, Jevremović, Darko, Johns, Kenneth, Johnson, Anthony S., Johnson, Margaret W. G., Jones, R. Lynne, Juramy-Gilles, Claire, Jurić, Mario, Kalirai, Jason S., Kallivayalil, Nitya J., Kalmbach, Bryce, Kantor, Jeffrey P., Karst, Pierre, Kasliwal, Mansi M., Kelly, Heather, Kessler, Richard, Kinnison, Veronica, Kirkby, David, Knox, Lloyd, Kotov, Ivan V., Krabbendam, Victor L., Krughoff, K. Simon, Kubánek, Petr, Kuczewski, John, Kulkarni, Shri, Ku, John, Kurita, Nadine R., Lage, Craig S., Lambert, Ron, Lange, Travis, Langton, J. Brian, Guillou, Laurent Le, Levine, Deborah, Liang, Ming, Lim, Kian-Tat, Lintott, Chris J., Long, Kevin E., Lopez, Margaux, Lotz, Paul J., Lupton, Robert H., Lust, Nate B., MacArthur, Lauren A., Mahabal, Ashish, Mandelbaum, Rachel, Marsh, Darren S., Marshall, Philip J., Marshall, Stuart, May, Morgan, McKercher, Robert, McQueen, Michelle, Meyers, Joshua, Migliore, Myriam, Miller, Michelle, Mills, David J., Miraval, Connor, Moeyens, Joachim, Monet, David G., Moniez, Marc, Monkewitz, Serge, Montgomery, Christopher, Mueller, Fritz, Muller, Gary P., Arancibia, Freddy Muñoz, Neill, Douglas R., Newbry, Scott P., Nief, Jean-Yves, Nomerotski, Andrei, Nordby, Martin, O'Connor, Paul, Oliver, John, Olivier, Scot S., Olsen, Knut, O'Mullane, William, Ortiz, Sandra, Osier, Shawn, Owen, Russell E., Pain, Reynald, Palecek, Paul E., Parejko, John K., Parsons, James B., Pease, Nathan M., Peterson, J. Matt, Peterson, John R., Petravick, Donald L., Petrick, M. E. Libby, Petry, Cathy E., Pierfederici, Francesco, Pietrowicz, Stephen, Pike, Rob, Pinto, Philip A., Plante, Raymond, Plate, Stephen, Price, Paul A., Prouza, Michael, Radeka, Veljko, Rajagopal, Jayadev, Rasmussen, Andrew P., Regnault, Nicolas, Reil, Kevin A., Reiss, David J., Reuter, Michael A., Ridgway, Stephen T., Riot, Vincent J., Ritz, Steve, Robinson, Sean, Roby, William, Roodman, Aaron, Rosing, Wayne, Roucelle, Cecille, Rumore, Matthew R., Russo, Stefano, Saha, Abhijit, Sassolas, Benoit, Schalk, Terry L., Schellart, Pim, Schindler, Rafe H., Schmidt, Samuel, Schneider, Donald P., Schneider, Michael D., Schoening, William, Schumacher, German, Schwamb, Megan E., Sebag, Jacques, Selvy, Brian, Sembroski, Glenn H., Seppala, Lynn G., Serio, Andrew, Serrano, Eduardo, Shaw, Richard A., Shipsey, Ian, Sick, Jonathan, Silvestri, Nicole, Slater, Colin T., Smith, J. Allyn, Smith, R. Chris, Sobhani, Shahram, Soldahl, Christine, Storrie-Lombardi, Lisa, Stover, Edward, Strauss, Michael A., Street, Rachel A., Stubbs, Christopher W., Sullivan, Ian S., Sweeney, Donald, Swinbank, John D., Szalay, Alexander, Takacs, Peter, Tether, Stephen A., Thaler, Jon J., Thayer, John Gregg, Thomas, Sandrine, Thukral, Vaikunth, Tice, Jeffrey, Trilling, David E., Turri, Max, Van Berg, Richard, Berk, Daniel Vanden, Vetter, Kurt, Virieux, Francoise, Vucina, Tomislav, Wahl, William, Walkowicz, Lucianne, Walsh, Brian, Walter, Christopher W., Wang, Daniel L., Wang, Shin-Yawn, Warner, Michael, Wiecha, Oliver, Willman, Beth, Winters, Scott E., Wittman, David, Wolff, Sidney C., Wood-Vasey, W. Michael, Wu, Xiuqin, Xin, Bo, Yoachim, Peter, and Zhan, Hu

Subjects
Astrophysics
Abstract

(Abridged) We describe here the most ambitious survey currently planned in the optical, the Large Synoptic Survey Telescope (LSST). A vast array of science will be enabled by a single wide-deep-fast sky survey, and LSST will have unique survey capability in the faint time domain. 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 wide-field ground-based system sited at Cerro Pach\'{o}n in northern Chile. The telescope will have an 8.4 m (6.5 m effective) primary mirror, a 9.6 deg$^2$ field of view, and a 3.2 Gigapixel camera. The standard observing sequence will consist of pairs of 15-second exposures in a given field, with two such visits in each pointing in a given night. With these repeats, the LSST system is capable of imaging about 10,000 square degrees of sky in a single filter in three nights. The typical 5$\sigma$ point-source depth in a single visit in $r$ will be $\sim 24.5$ (AB). The project is in the construction phase and will begin regular survey operations by 2022. The survey area will be contained within 30,000 deg$^2$ with $\delta<+34.5^\circ$, and will be imaged multiple times in six bands, $ugrizy$, covering the wavelength range 320--1050 nm. About 90\% of the observing time will be devoted to a deep-wide-fast survey mode which will uniformly observe a 18,000 deg$^2$ region about 800 times (summed over all six bands) during the anticipated 10 years of operations, and yield a coadded map to $r\sim27.5$. The remaining 10\% of the observing time will be allocated to projects such as a Very Deep and Fast time domain survey. The goal is to make LSST data products, including a relational database of about 32 trillion observations of 40 billion objects, available to the public and scientists around the world., Comment: 57 pages, 32 color figures, version with high-resolution figures available from https://www.lsst.org/overview

Published
2008
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