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Title: WEAK-LENSING PEAK FINDING: ESTIMATORS, FILTERS, AND BIASES

Journal Article · · Astrophysical Journal
 [1];  [2]
  1. Theoretical Astrophysics, California Institute of Technology, M/C 350-17, Pasadena, CA 91125 (United States)
  2. Department of Astronomy and Astrophysics, University of Chicago, Chicago, IL 60637 (United States)

Large catalogs of shear-selected peaks have recently become a reality. In order to properly interpret the abundance and properties of these peaks, it is necessary to take into account the effects of the clustering of source galaxies, among themselves and with the lens. In addition, the preferred selection of magnified galaxies in a flux- and size-limited sample leads to fluctuations in the apparent source density that correlate with the lensing field. In this paper, we investigate these issues for two different choices of shear estimators that are commonly in use today: globally normalized and locally normalized estimators. While in principle equivalent, in practice these estimators respond differently to systematic effects such as magnification and cluster member dilution. Furthermore, we find that the answer to the question of which estimator is statistically superior depends on the specific shape of the filter employed for peak finding; suboptimal choices of the estimator+filter combination can result in a suppression of the number of high peaks by orders of magnitude. Magnification and size bias generally act to increase the signal-to-noise {nu} of shear peaks; for high peaks the boost can be as large as {Delta}{nu} {approx} 1-2. Due to the steepness of the peak abundance function, these boosts can result in a significant increase in the observed abundance of shear peaks. A companion paper investigates these same issues within the context of stacked weak-lensing mass estimates.

OSTI ID:
21578366
Journal Information:
Astrophysical Journal, Vol. 735, Issue 2; Other Information: DOI: 10.1088/0004-637X/735/2/119; ISSN 0004-637X
Country of Publication:
United States
Language:
English