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Register a new userMocking the Weak Lensing universe: the LensTools python computing package
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Andrea Petri
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We present a newly developed software package which implements a wide range of routines frequently used in Weak Gravitational Lensing (WL). With the continuously increasing size of the WL scientific community we feel that easy to use Application Program Interfaces (APIs) for common calculations are a necessity to ensure efficiency and coordination across different working groups. Coupled with existing open source codes, such as CAMB and Gadget2, LensTools brings together a cosmic shear simulation pipeline which, complemented with a variety of WL feature measurement tools and parameter sampling routines, provides easy access to the numerics for theoretical studies of WL as well as for experiment forecasts. Being implemented in python, LensTools takes full advantage of a range of state--of--the art techniques developed by the large and growing open--source software community (scipy,pandas,astropy,scikit-learn,emcee). We made the LensTools code available on the Python Package Index and published its documentation on http://lenstools.readthedocs.io
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Advanced LIGO and Advanced Virgo could observe the first lensed gravitational waves in the coming years, while the future Einstein Telescope could observe hundreds of lensed events. Ground-based gravitational-wave detectors can resolve arrival time differences of the order of the inverse of the observed frequencies. As LIGO/Virgo frequency band spans from a few $rm Hz$ to a few $ rm kHz$, the typical time resolution of current interferometers is of the order of milliseconds. When microlenses are embedded in galaxies or galaxy clusters, lensing can become more prominent and result in observable time delays at LIGO/Virgo frequencies. Therefore, gravitational waves could offer an exciting alternative probe of microlensing. However, currently, only a few lensing configurations have been worked out in the context of gravitational-wave lensing. In this paper, we present lensingGW, a Python package designed to handle both strong and microlensing of compact binaries and the related gravitational-wave signals. This synergy paves the way for systematic parameter space investigations and the detection of arbitrary lens configurations and compact sources. We demonstrate the working mechanism of lensingGW and its use to study microlenses embedded in galaxies.
Weak gravitational lensing measurements are traditionally made at optical wavelengths where many highly resolved galaxy images are readily available. However, the Square Kilometre Array (SKA) holds great promise for this type of measurement at radio wavelengths owing to its greatly increased sensitivity and resolution over typical radio surveys. The key to successful weak lensing experiments is in measuring the shapes of detected sources to high accuracy. In this document we describe a simulation pipeline designed to simulate radio images of the quality required for weak lensing, and will be typical of SKA observations. We provide as input, images with realistic galaxy shapes which are then simulated to produce images as they would have been observed with a given radio interferometer. We exploit this pipeline to investigate various stages of a weak lensing experiment in order to better understand the effects that may impact shape measurement. We first show how the proposed SKA1-Mid array configurations perform when we compare the (known) input and output ellipticities. We then investigate how making small changes to these array configurations impact on this input-outut ellipticity comparison. We also demonstrate how alternative configurations for SKA1-Mid that are smaller in extent, and with a faster survey speeds produce similar performance to those originally proposed. We then show how a notional SKA configuration performs in the same shape measurement challenge. Finally, we describe ongoing efforts to utilise our simulation pipeline to address questions relating to how applicable current (mostly originating from optical data) shape measurement techniques are to future radio surveys. As an alternative to such image plane techniques, we lastly discuss a shape measurement technique based on the shapelets formalism that reconstructs the source shapes directly from the visibility data.
We describe a new open source package for calculating properties of galaxy clusters, including NFW halo profiles with and without the effects of cluster miscentering. This pure-Python package, cluster-lensing, provides well-documented and easy-to-use classes and functions for calculating cluster scaling relations, including mass-richness and mass-concentration relations from the literature, as well as the surface mass density $Sigma(R)$ and differential surface mass density $DeltaSigma(R)$ profiles, probed by weak lensing magnification and shear. Galaxy cluster miscentering is especially a concern for stacked weak lensing shear studies of galaxy clusters, where offsets between the assumed and the true underlying matter distribution can lead to a significant bias in the mass estimates if not accounted for. This software has been developed and released in a public GitHub repository, and is licensed under the permissive MIT license. The cluster-lensing package is archived on Zenodo (Ford 2016). Full documentation, source code, and installation instructions are available at http://jesford.github.io/cluster-lensing/.
We construct the spin flaglet transform, a wavelet transform to analyze spin signals in three dimensions. Spin flaglets can probe signal content localized simultaneously in space and frequency and, moreover, are separable so that their angular and radial properties can be controlled independently. They are particularly suited to analyzing of cosmological observations such as the weak gravitational lensing of galaxies. Such observations have a unique 3D geometrical setting since they are natively made on the sky, have spin angular symmetries, and are extended in the radial direction by additional distance or redshift information. Flaglets are constructed in the harmonic space defined by the Fourier-Laguerre transform, previously defined for scalar functions and extended here to signals with spin symmetries. Thanks to various sampling theorems, both the Fourier-Laguerre and flaglet transforms are theoretically exact when applied to bandlimited signals. In other words, in numerical computations the only loss of information is due to the finite representation of floating point numbers. We develop a 3D framework relating the weak lensing power spectrum to covariances of flaglet coefficients. We suggest that the resulting novel flaglet weak lensing estimator offers a powerful alternative to common 2D and 3D approaches to accurately capture cosmological information. While standard weak lensing analyses focus on either real or harmonic space representations (i.e., correlation functions or Fourier-Bessel power spectra, respectively), a wavelet approach inherits the advantages of both techniques, where both complicated sky coverage and uncertainties associated with the physical modeling of small scales can be handled effectively. Our codes to compute the Fourier-Laguerre and flaglet transforms are made publicly available.
One of the most powerful techniques to study the dark sector of the Universe is weak gravitational lensing. In practice, to infer the reduced shear, weak lensing measures galaxy shapes, which are the consequence of both the intrinsic ellipticity of the sources and of the integrated gravitational lensing effect along the line of sight. Hence, a very large number of galaxies is required in order to average over their individual properties and to isolate the weak lensing cosmic shear signal. If this `shape noise can be reduced, significant advances in the power of a weak lensing surveys can be expected. This paper describes a general method for extracting the probability distributions of parameters from catalogues of data using Voronoi cells, which has several applications, and has synergies with Bayesian hierarchical modelling approaches. This allows us to construct a probability distribution for the variance of the intrinsic ellipticity as a function of galaxy property using only photometric data, allowing a reduction of shape noise. As a proof of concept the method is applied to the CFHTLenS survey data. We use this approach to investigate trends of galaxy properties in the data and apply this to the case of weak lensing power spectra.
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