Molly E. C. Swanson
Massachusetts Institute of Technology
5 Papers
4 Citations
Molly E. C. Swanson is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Polygon & HEALPix. The author has an hindex of 3, co-authored 5 publications.
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Papers
Cosmological constraints from the SDSS luminous red galaxies
Max Tegmark,Daniel J. Eisenstein,Michael A. Strauss,David H. Weinberg,Michael R. Blanton,Joshua A. Frieman,Joshua A. Frieman,Masataka Fukugita,James E. Gunn,Andrew J. S. Hamilton,Gillian R. Knapp,Robert C. Nichol,Jeremiah P. Ostriker,Nikhil Padmanabhan,Will J. Percival,David J. Schlegel,Donald P. Schneider,Roman Scoccimarro,Uroš Seljak,Uroš Seljak,Hee-Jong Seo,Molly E. C. Swanson,Alexander S. Szalay,Michael S. Vogeley,Jaiyul Yoo,Idit Zehavi,Kevork N. Abazajian,Scott F. Anderson,James Annis,Neta A. Bahcall,Bruce A. Bassett,Andreas A. Berlind,Jon Brinkmann,Tamás Budavári,Francisco J. Castander,Andrew J. Connolly,István Csabai,Mamoru Doi,Douglas P. Finkbeiner,Douglas P. Finkbeiner,Bruce Gillespie,Karl Glazebrook,Gregory S. Hennessy,David W. Hogg,Željko Ivezić,Željko Ivezić,Bhuvnesh Jain,David Johnston,Stephen M. Kent,D. Q. Lamb,Brian C. Lee,Huan Lin,Jon Loveday,Robert H. Lupton,Jeffrey A. Munn,Kaike Pan,Changbom Park,John Peoples,Jeffrey R. Pier,Adrian Pope,Michael Richmond,Constance M. Rockosi,Ryan Scranton,Ravi K. Sheth,Albert Stebbins,Christopher Stoughton,István Szapudi,Douglas L. Tucker,Daniel E. Vanden Berk,Brian Yanny,Donald G. York +70 more
TL;DR: In this paper, the authors employed a matrix-based power spectrum estimation method using pseudo-Karhunen-Loeve eigenmodes, producing uncorrelated minimum-variance measurements in 20 k-bands of both the clustering power and its anisotropy due to redshift-space distortions.
SDSS galaxy clustering: luminosity and colour dependence and stochasticity
TL;DR: In this paper, a count-in-cells analysis of galaxies in the Sloan Digital Sky Survey was performed to measure the relative bias between pairs of galaxy subsamples of different luminosities and colours.
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Methods for Rapidly Processing Angular Masks of Next-Generation Galaxy Surveys
TL;DR: The mangle tool as mentioned in this paper splits the angular mask into predefined regions called "pixels", such that each polygon is in only one pixel, and then performs further computations such as checking for overlap, on the polygons within each pixel separately.
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Methods for rapidly processing angular masks of next-generation galaxy surveys
TL;DR: A 'divide-and-conquer' solution to this challenge of managing angular masks on a sphere, such that each polygon is in only one pixel, and then performs further computations on the polygons within each pixel separately, which reduces O(N 2 ) tasks to 0(N), and also reduces the important task of determining in which polygon a point on the sky lies from0(N) to O(1), resulting in significant computational speedup.