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6 results on '"Daniel, Scott F."'

1. DESC DC2 Data Release Note

Author

LSST Dark Energy Science Collaboration, Abolfathi, Bela, Armstrong, Robert, Awan, Humna, Babuji, Yadu N., Bauer, Franz Erik, Beckett, George, Biswas, Rahul, Bogart, Joanne R., Boutigny, Dominique, Chard, Kyle, Chiang, James, Cohen-Tanugi, Johann, Connolly, Andrew J., Daniel, Scott F., Digel, Seth W., Drlica-Wagner, Alex, Dubois, Richard, Gawiser, Eric, Glanzman, Thomas, Habib, Salman, Hearin, Andrew P., Heitmann, Katrin, Hernandez, Fabio, Hložek, Renée, Hollowed, Joseph, Jarvis, Mike, Jha, Saurabh W., Kalmbach, J. Bryce, Kelly, Heather M., Kovacs, Eve, Korytov, Danila, Krughoff, K. Simon, Lage, Craig S., Lanusse, François, Larsen, Patricia, Li, Nan, Longley, Emily Phillips, Lupton, Robert H., Mandelbaum, Rachel, Mao, Yao-Yuan, Marshall, Phil, Meyers, Joshua E., Park, Ji Won, Peloton, Julien, Perrefort, Daniel, Perry, James, Plaszczynski, Stéphane, Pope, Adrian, Rykoff, Eli S., Sánchez, F. Javier, Schmidt, Samuel J., Uram, Thomas D., Villarreal, Antonia, Walter, Christopher W., Wiesner, Matthew P., Wood-Vasey, W. Michael, Laboratoire d'Annecy de Physique des Particules (LAPP), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), Laboratoire Univers et Particules de Montpellier (LUPM), Université Montpellier 2 - Sciences et Techniques (UM2)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Physique de Clermont (LPC), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA), Centre de Calcul de l'IN2P3 (CC-IN2P3), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Astrophysique Interprétation Modélisation (AIM (UMR_7158 / UMR_E_9005 / UM_112)), Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris Diderot - Paris 7 (UPD7), Laboratoire de Physique des 2 Infinis Irène Joliot-Curie (IJCLab), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), LSST Dark Energy Science, Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Astrophysique Interprétation Modélisation (AIM (UMR7158 / UMR_E_9005 / UM_112)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), and HEP, INSPIRE

Subjects
Cosmology and Nongalactic Astrophysics (astro-ph.CO), [PHYS.PHYS.PHYS-INS-DET] Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det], FOS: Physical sciences, [PHYS.PHYS.PHYS-INS-DET]Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det], Astrophysics - Instrumentation and Methods for Astrophysics, [PHYS.ASTR] Physics [physics]/Astrophysics [astro-ph], [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph], Instrumentation and Methods for Astrophysics (astro-ph.IM), Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

In preparation for cosmological analyses of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), the LSST Dark Energy Science Collaboration (LSST DESC) has created a 300 deg$^2$ simulated survey as part of an effort called Data Challenge 2 (DC2). The DC2 simulated sky survey, in six optical bands with observations following a reference LSST observing cadence, was processed with the LSST Science Pipelines (19.0.0). In this Note, we describe the public data release of the resulting object catalogs for the coadded images of five years of simulated observations along with associated truth catalogs. We include a brief description of the major features of the available data sets. To enable convenient access to the data products, we have developed a web portal connected to Globus data services. We describe how to access the data and provide example Jupyter Notebooks in Python to aid first interactions with the data. We welcome feedback and questions about the data release via a GitHub repository., Comment: 25 pages, 3 figures; 9 tables. A detailed changelog can be found in Appendix A. To obtain data, visit the DESC Data Portal at https://data.lsstdesc.org/

Published
2021

2. The LSST DESC DC2 Simulated Sky Survey.

Author

DESC), tion (LSST, Abolfathi, Bela, Alonso, David, Armstrong, Robert, Aubourg, Éric, Awan, Humna, Babuji, Yadu N., Bauer, Franz Erik, Bean, Rachel, Beckett, George, Biswas, Rahul, Bogart, Joanne R., Boutigny, Dominique, Chard, Kyle, Chiang, James, Claver, Chuck F., Cohen-Tanugi, Johann, Combet, Céline, Connolly, Andrew J., and Daniel, Scott F.

Published
2021
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3. Accelerated Parameter Estimation with DALE$\chi$

Author

Daniel, Scott F. and Linder, Eric V.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

We consider methods for improving the estimation of constraints on a high-dimensional parameter space with a computationally expensive likelihood function. In such cases Markov chain Monte Carlo (MCMC) can take a long time to converge and concentrates on finding the maxima rather than the often-desired confidence contours for accurate error estimation. We employ DALE$\chi$ (Direct Analysis of Limits via the Exterior of $\chi^2$) for determining confidence contours by minimizing a cost function parametrized to incentivize points in parameter space which are both on the confidence limit and far from previously sampled points. We compare DALE$\chi$ to the nested sampling algorithm implemented in MultiNest on a toy likelihood function that is highly non-Gaussian and non-linear in the mapping between parameter values and $\chi^2$. We find that in high-dimensional cases DALE$\chi$ finds the same confidence limit as MultiNest using roughly an order of magnitude fewer evaluations of the likelihood function. DALE$\chi$ is open-source and available at https://github.com/danielsf/Dalex.git.

Published
2017

6. DETERMINING FREQUENTIST CONFIDENCE LIMITS USING A DIRECTED PARAMETER SPACE SEARCH.

Author

Daniel, Scott F., Connolly, Andrew J., and Schneider, Jeff

Subjects
*MACHINE learning, *ARTIFICIAL intelligence, *MACHINE theory, *MARKOV chain Monte Carlo, *MARKOV processes
Abstract

We consider the problem of inferring constraints on a high-dimensional parameter space with a computationally expensive likelihood function. We propose a machine learning algorithm that maps out the Frequentist confidence limit on parameter space by intelligently targeting likelihood evaluations so as to quickly and accurately characterize the likelihood surface in both low- and high-likelihood regions. We compare our algorithm to Bayesian credible limits derived by the well-tested Markov Chain Monte Carlo (MCMC) algorithm using both multi-modal toy likelihood functions and the seven yr Wilkinson Microwave Anisotropy Probe cosmic microwave background likelihood function. We find that our algorithm correctly identifies the location, general size, and general shape of high-likelihood regions in parameter space while being more robust against multi-modality than MCMC. [ABSTRACT FROM AUTHOR]

Published
2014
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