My Research
Recent Projects:
- Grale Based Reconstructions of Gravitational Lens Models of Synthetic Galaxy Clusters.
- Signatures of Mass-to-Light Deviation Scenarios in Abell 370.
My doctoral research is primarily focussed on Strong Gravitational Lensing. The main motivation of my work lies in finding free-form non-parametric inversions of galaxy clusters using strong gravitational lensing, dark matter dynamics in galaxy clusters and predictions of precision measurement of the Hubble constant using reconstructed time delay estimations. The core theoretical work is mainly related to reconstruction of galaxy cluster mass distributions using Grale, a genetic algorithm-based non-parametric method, which runs on a supercomputer.
Mass Distribution in Galaxy Clusters.
Many scientific discoveries can be done using the mass distribution of clusters of galaxies, like understanding dark matter dynamics and their self-interactions in galaxy clusters, making predictions of precision measurement of the Hubble constant, and study of the very distant, or high-redshift universe. Merging clusters are especially interesting because the nature of interactions between member galaxies and cluster dark matter depends, in part, on non-gravitational forces, and hence properties of dark matter particles. Much has been learned about the distribution of dark matter and hot gas in clusters, but many questions remain, for example, relating to the fraction of dark matter in compact substructures.Clusters as Natural Telescopes.
Clusters of Galaxies are relatively young in the cosmic evolution. They are extremely massive, but gravitationally bound. This makes them good candidates as strong gravitational lenses. To utilize clusters of galaxies as cosmic telescopes, one needs to characterize their uneven optics, i.e. obtain magnification maps. These are derived from the mass distribution maps, which is obtained by computational methods called gravitational lens mass reconstructions.Our Mass Reconstruction Method.
The reconstruction methods are of two types: parametric and free-form. In simply parametrized models, observed galaxies and cluster-wide dark matter halos are assigned one function each. In the early days of lens modeling, when image constraints were sparse, this simplification of the modeling problem was very helpful. However, with increased number of images, simplifying assumptions will become a hindrance. In contrast, free-form methods use more abstract basis functions as their building blocks, so a given galaxy or dark matter halo is represented by a number of these building blocks. This makes methods like Grale flexible. Grale is developed based on the genetic algorithm. The advantage of this algorithm is that Grale uses only the image positions and redshifts, and is completely agnostic about the observed light distribution of the clusters. Grale is ideally suited for reconstructions with plentiful strong lensing data because it uses no parametric assumptions. The number of its model parameters exceeds the number of observational constraints, allowing wider exploration of degenerate mass distributions. But, this also makes Grale computationally expensive, requiring supercomputers, for which we are indebted to the high performance computing resources provided by the Minnesota Supercomputer Institute.More details about Grale can be found here.