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Detection of (dark) matter concentrations via weak gravitational lensing

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arxiv astro-ph/9601039 v2 pith:OZSLS3L4 submitted 1996-01-10 astro-ph

classification astro-ph
keywords massusedapertureconcentrationsdarkimagesmethodsignal-to-noise
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abstract

The distortion of images of faint background galaxies by (weak) gravitational lensing can be used to measure the mass distribution of the deflector. The image distortions can be used to define a weighted mean of the mass inside a circular aperture, as was first suggested by Kaiser. The aperture mass can be used to {\it detect} dark matter concentrations. Keeping in mind that wide-field cameras will become increasingly available, this method can be used to search for mass concentrations on wide-field images. To do this, the aperture mass measure is generalized to account for different weighting functions. For each such weighting function, a signal-to-noise ratio can be calculated. For an assumed mass profile of the density concentrations, the weighting function can be chosen such as to maximize the resulting signal-to-noise ratio. Numerical simulations which adopt parameters characteristic of 4-m class telescopes are then used to show that dark halos with a velocity dispersion in excess of $\sim 600$\ts km/s can be reliably detected as significant peaks in the signal-to-noise map. The effects of seeing and an anisotropic PSF are then investigated and shown to be less important than might be feared. It is thus suggested that the method of aperture mass measures developed here can be used to obtain a mass-selected sample of dark halos, in contrast to flux-selected samples. Shear fields around high-redshift bright QSOs as detected by Fort et al. provide a first successful application of this strategy. The simplicity of the method allows its routine application to wide-field images of sufficient depth and image quality.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 338 citations worldwide. Full citation record

  1. Machine-learning applications for weak-lensing cosmology

    astro-ph.CO 2026-05 unverdicted novelty 2.0 of 10

    Machine learning techniques can mitigate limitations in traditional weak-lensing analyses and enhance extraction of cosmological information from galaxy imaging surveys.

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