Publications

Below are papers by the LSST DESC. At the bottom of the page is a link to associated released products. You can read our publication policy here. You can also view the publication list on NASA’s ADS, and download a bibtex file by clicking the “Export” button on ADS.

Published Journal Papers

2019

Serendipitous discoveries of kilonovae in the LSST main survey: maximizing detections of sub-threshold gravitational wave events, Setzer, C. N., Biswas, R., Peiris, H. V., Rosswog, S., Korobkin, O., and Wollaeger, R. T., MNRAS, 485, 4260 (2019) [ADS, arXiv]

Core Cosmology Library: Precision Cosmological Predictions for LSST, Chisari, N. E., Alonso, D., Krause, E., Leonard, C. D., Bull, P., Neveu, J., Villarreal, A., Singh, S., McClintock, T., Ellison, J., Du, Z., Zuntz, J., Mead, A., Joudaki, S., Lorenz, C. S., Tröster, T., Sanchez, J., Lanusse, F., Ishak, M., Hložek, R., Blazek, J., Campagne, J.-E., Almoubayyed, H., Eifler, T., Kirby, M., Kirkby, D., Plaszczynski, S., Slosar, A., Vrastil, M., Wagoner, E. L., and LSST Dark Energy Science Collaboration, ApJS, 242, 2 (2019) [ADS, arXiv]

A unified pseudo-C framework, Alonso, D., Sanchez, J., and Slosar, A., MNRAS, 484, 4127 (2019) [ADS, arXiv]

Self-calibration method for II and GI types of intrinsic alignments of galaxies, Yao, J., Ishak, M., and Troxel, M. A., MNRAS, 483, 276 (2019) [ADS, arXiv]

A unified analysis of four cosmic shear surveys, Chang, C., Wang, M., Dodelson, S., Eifler, T., Heymans, C., Jarvis, M., Jee, M. J., Joudaki, S., Krause, E., Malz, A., Mandelbaum, R., Mohammed, I., Schneider, M., Simet, M., Troxel, M. A., and Zuntz, J., MNRAS, 482, 3696 (2019) [ADS, arXiv]

2018

Measuring the scale dependence of intrinsic alignments using multiple shear estimates, Leonard, C. D. and Mandelbaum, R., MNRAS, 479, 1412 (2018) [ADS, arXiv]

Approximating Photo-z PDFs for Large Surveys, Malz, A. I., Marshall, P. J., DeRose, J., Graham, M. L., Schmidt, S. J., Wechsler, R., and LSST Dark Energy Science Collaboration, AJ, 156, 35 (2018) [ADS, arXiv]

DESCQA: An Automated Validation Framework for Synthetic Sky Catalogs, Mao, Y.-Y., Kovacs, E., Heitmann, K., Uram, T. D., Benson, A. J., Campbell, D., Cora, S. A., DeRose, J., Di Matteo, T., Habib, S., Hearin, A. P., Bryce Kalmbach, J., Krughoff, K. S., Lanusse, F., Lukić, Z., Mandelbaum, R., Newman, J. A., Padilla, N., Paillas, E., Pope, A., Ricker, P. M., Ruiz, A. N., Tenneti, A., Vega-Martínez, C. A., Wechsler, R. H., Zhou, R., Zu, Y., and LSST Dark Energy Science Collaboration, ApJS, 234, 36 (2018) [ADS, arXiv]

Submitted for Publication

Enabling catalog simulations of transient and variable sources based on LSST cadence strategies, Biswas, R., Daniel, S. F., Hložek, R., Kim, A. G., and Yoachim, P., arXiv:1905.02887 [ADS]

Models and Simulations for the Photometric LSST Astronomical Time Series Classification Challenge (PLAsTiCC), Kessler, R., Narayan, G., Avelino, A., Bachelet, E., Biswas, R., Brown, P. J., Chernoff, D. F., Connolly, A. J., Dai, M., Daniel, S., Di Stefano, R., Drout, M. R., Galbany, L., González-Gaitán, S., Graham, M. L., Hložek, R., Ishida, E. E. O., Guillochon, J., Jha, S. W., Jones, D. O., Mandel, K. S., Muthukrishna, D., O’Grady, A., Peters, C. M., Pierel, J. R., Ponder, K. A., Prša, A., Rodney, S., and Villar, V. A., arXiv:1903.11756 [ADS]

Strongly lensed SNe Ia in the era of LSST: observing cadence for lens discoveries and time-delay measurements, Huber, S., Suyu, S. H., Noebauer, U. M., Bonvin, V., Rothchild, D., Chan, J. H. H., Awan, H., Courbin, F., Kromer, M., Marshall, P., Oguri, M., Ribeiro, T., and The LSST Dark Energy Science Collaboration, arXiv:1903.00510 [ADS]

Sparse Bayesian mass-mapping with uncertainties: peak statistics and feature locations, Price, M. A., Cai, X., McEwen, J. D., and Kitching, T. D., arXiv:1812.04018 [ADS]

Sparse Bayesian mass-mapping with uncertainties: local credible intervals, Price, M. A., Cai, X., McEwen, J. D., Pereyra, M., and Kitching, T. D., arXiv:1812.04017 [ADS]

Sparse Bayesian mass-mapping with uncertainties: hypothesis testing of structure, Price, M. A., McEwen, J. D., Cai, X., Kitching, T. D., and Wallis, C. G. R., arXiv:1812.04014 [ADS]

The Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC): Selection of a performance metric for classification probabilities balancing diverse science goals, Malz, A., Hložek, R., Allam, T., Bahmanyar, A., Biswas, R., Dai, M., Galbany, L., Ishida, E., Jha, S., Jones, D., Kessler, R., Lochner, M., Mahabal, A., Mandel, K., Martínez-Galarza, R., McEwen, J., Muthukrishna, D., Narayan, G., Peiris, H., Peters, C., Setzer, C., the LSST Dark Energy Science Collaboration, and the LSST Transients and Variable Stars Science Collaboration, arXiv:1809.11145 [ADS]

Released DESC Notes and White Papers

2019

Deep Multi-object Spectroscopy to Enhance Dark Energy Science from LSST, Newman, J. A., Blazek, J., Chisari, N. E., Clowe, D., Dell’Antonio, I., Gawiser, E., Hložek, R. A., Kim, A. G., von der Linden, A., Lochner, M., Mandelbaum, R., Medezinski, E., Melchior, P., Sánchez, F. J., Schmidt, S. J., Singh, S., and Zhou, R., arXiv:1903.09325 [ADS]

Single-object Imaging and Spectroscopy to Enhance Dark Energy Science from LSST, Hložek, R. A., Collett, T., Galbany, L., Goldstein, D. A., Jha, S. W., Kim, A. G., Mandelbaum, R., Newman, J. A., Perlmutter, S., Perrefort, D. J., Sullivan, M., and Verma, A., arXiv:1903.09324 [ADS]

Wide-field Multi-object Spectroscopy to Enhance Dark Energy Science from LSST, Mandelbaum, R., Blazek, J., Chisari, N. E., Collett, T., Galbany, L., Gawiser, E., Hložek, R. A., Kim, A. G., Leonard, C. D., Lochner, M., Newman, J. A., Perrefort, D. J., Schmidt, S. J., Singh, S., and Sullivan, M., arXiv:1903.09323 [ADS]

2018

Optimizing the LSST Observing Strategy for Dark Energy Science: DESC Recommendations for the Deep Drilling Fields and other Special Programs, Scolnic, D. M., Lochner, M., Gris, P., Regnault, N., Hložek, R., Aldering, G., Allam, T., Awan, H., Biswas, R., Blazek, J., Chang, C., Gawiser, E., Goobar, A., Hook, I. M., Jha, S. W., McEwen, J. D., Mandelbaum, R., Marshall, P., Neilsen, E., Rhodes, J., Rothchild, D., Sevilla Noarbe, I., Slosar, A., and Yoachim, P., arXiv:1812.00516 [ADS]

Optimizing the LSST Observing Strategy for Dark Energy Science: DESC Recommendations for the Wide-Fast-Deep Survey, Lochner, M., Scolnic, D. M., Awan, H., Regnault, N., Gris, P., Mandelbaum, R., Gawiser, E., Almoubayyed, H., Setzer, C. N., Huber, S., Graham, M. L., Hložek, R., Biswas, R., Eifler, T., Rothchild, D., Allam, T., Blazek, J., Chang, C., Collett, T., Goobar, A., Hook, I. M., Jarvis, M., Jha, S. W., Kim, A. G., Marshall, P., McEwen, J. D., Moniez, M., Newman, J. A., Peiris, H. V., Petrushevska, T., Rhodes, J., Sevilla-Noarbe, I., Slosar, A., Suyu, S. H., Tyson, J. A., and Yoachim, P., arXiv:1812.00515 [ADS]

The Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC): Data set, The PLAsTiCC team, Allam, T., Bahmanyar, A., Biswas, R., Dai, M., Galbany, L., Hložek, R., Ishida, E. E. O., Jha, S. W., Jones, D. O., Kessler, R., Lochner, M., Mahabal, A. A., Malz, A. I., Mandel, K. S., Martínez-Galarza, J. R., McEwen, J. D., Muthukrishna, D., Narayan, G., Peiris, H., Peters, C. M., Ponder, K., Setzer, C. N., the LSST Dark Energy Science Collaboration, and the LSST Transients and Variable Stars Science Collaboration, arXiv:1810.00001 [ADS]

The LSST Dark Energy Science Collaboration (DESC) Science Requirements Document, The LSST Dark Energy Science Collaboration, Mandelbaum, R., Eifler, T., Hložek, R., Collett, T., Gawiser, E., Scolnic, D., Alonso, D., Awan, H., Biswas, R., Blazek, J., Burchat, P., Chisari, N. E., Dell’Antonio, I., Digel, S., Frieman, J., Goldstein, D. A., Hook, I., Ivezić, Ž., Kahn, S. M., Kamath, S., Kirkby, D., Kitching, T., Krause, E., Leget, P.-F., Marshall, P. J., Meyers, J., Miyatake, H., Newman, J. A., Nichol, R., Rykoff, E., Sanchez, F. J., Slosar, A., Sullivan, M., and Troxel, M. A., arXiv:1809.01669 [ADS]

2012

Large Synoptic Survey Telescope: Dark Energy Science Collaboration, LSST Dark Energy Science Collaboration, arXiv:1211.0310 [ADS]

Released DESC Data Products

Released products are in the LSST DESC Community on Zenodo.