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lenstronomy is an Astropy-affiliated Python package for gravitational lensing simulations and analyses. lenstronomy was introduced by Birrer and Amara (2018) and is based on the linear basis set approach by Birrer et a. (2015). The user and developer base of lenstronomy has substantially grown since then, and the software has become an integral part of a wide range of recent analyses, such as measuring the Hubble constant with time-delay strong lensing or constraining the nature of dark matter from resolved and unresolved small scale lensing distortion statistics. The modular design has allowed the community to incorporate innovative new methods, as well as to develop enhanced software and wrappers with more specific aims on top of the lenstronomy API. Through community engagement and involvement, lenstronomy has become a foundation of an ecosystem of affiliated packages extending the original scope of the software and proving its robustness and applicability at the forefront of the strong gravitational lensing community in an open source and reproducible manner.
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The difficult task of observing Dark Matter subhaloes is of paramount importance since it would constrain Dark Matter particle properties (cold or warm relic) and confirm once again the longstanding $Lambda$CDM model. In the near future the new generation of ground and space surveys will observe thousands of strong gravitational lensing systems providing a unique probe of Dark Matter substructures. Here, we describe a new strong lensing analysis pipeline that combines deep Convolutional Neural Networks with physical models and exploits traditional sampling techniques such as Hamiltonian Monte Carlo. Using simulated strong gravitational lensing systems, we discuss first results and characterize the accuracy of the reconstruction of the main lensing parameters.
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.
Cosmological numerical simulations of galaxy formation have led to the cuspy density profile of a pure cold dark matter halo toward the center, which is in sharp contradiction with the observations of the rotation curves of cold dark matter-dominated dwarf and low surface brightness disk galaxies, with the latter tending to favor mass profiles with a flat central core. Many efforts have been devoted to resolve this cusp-core problem in recent years, among them, baryon-cold dark matter interactions are considered to be the main physical mechanisms erasing the cold dark matter (CDM) cusp into a flat core in the centers of all CDM halos. Clearly, baryon-cold dark matter interactions are not customized only for CDM-dominated disk galaxies, but for all types, including giant ellipticals. We first fit the most recent high resolution observations of rotation curves with the Burkert profile, then use the constrained core size-halo mass relation to calculate the lensing frequency, and compare the predicted results with strong lensing observations. Unfortunately, it turns out that the core size constrained from rotation curves of disk galaxies cannot be extrapolated to giant ellipticals. We conclude that, in the standard cosmological paradigm, baryon-cold dark matter interactions are not universal mechanisms for galaxy formation, and therefore, they cannot be true solutions to the cusp-core problem.
We present a method to include lensing selection effects due to the finite horizon of a given detector when studying lensing of gravitational wave (GW) sources. When selection effects are included, the mean of the magnification distribution is shifted from one to higher values for sufficiently high-redshift sources. This introduces an irreducible (multiplicative) bias on the luminosity distance reconstruction, in addition to the typical source of uncertainty in the distance determination. We apply this method to study lensing of GWs emitted by massive black hole binary mergers at high redshift detectable by LISA. We estimate the expected bias induced by selection effects on the luminosity distance reconstruction as function of cosmological redshift, and discuss its implications for cosmological and astrophysical analyses with LISA. We also reconstruct the distribution of lensing magnification as a function of the observed luminosity distance to a source, that is the observable quantity in the absence of an electromagnetic counterpart. Lensing provides the dominant source of errors in distance measurements of high-redshift GW sources. Its full characterisation, including the impact of selection effects, is of paramount importance to correctly determine the astrophysical properties of the underlying source population and to be able to use gravitational wave sources as a new cosmological probe.
Aims: In this work, we aim to provide a reliable list of gravitational lens (GL) candidates based on a search performed over the entire Gaia Data Release 2 (Gaia DR2). We also show that the sole astrometric and photometric informations coming from the Gaia satellite yield sufficient insights for supervised learning methods to automatically identify GL candidates with an efficiency that is comparable to methods based on image processing. Methods: We simulated 106,623,188 lens systems composed of more than two images, based on a regular grid of parameters characterizing a non-singular isothermal ellipsoid lens model in the presence of an external shear. These simulations are used as an input for training and testing our supervised learning models consisting of Extremely Randomized Trees. The latter are finally used to assign to each of the 2,129,659 clusters of celestial objects a discriminant value that reflects the ability of our simulations to match the observed relative positions and fluxes from each cluster. Once complemented with additional constraints, these discriminant values allowed us to identify GL candidates out of the list of clusters. Results: We report the discovery of 15 new quadruply-imaged lens candidates with angular separations less than 6 and assess the performance of our approach by recovering 12 out of the 13 known quadruply-imaged systems with all their components detected in Gaia DR2 with a misclassification rate of fortuitous clusters of stars as lens systems that is below one percent. Similarly, the identification capability of our method regarding quadruply-imaged systems where three images are detected in Gaia DR2 is assessed by recovering 10 out of the 13 known quadruply-imaged systems having one of their constituting images discarded. The associated misclassification rate varying then between 5.8% and 20%, depending on the image we decided to remove.
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