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Register a new userSupernova forecast with strong lensing
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Yudai Suwa
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In the coming LSST era, we will observe $mathcal{O}(100)$ of lensed supernovae (SNe). In this paper, we investigate possibility for predicting time and sky position of a supernova using strong lensing. We find that it will be possible to predict the time and position of the fourth image of SNe which produce four images by strong lensing, with combined information from the three previous images. It is useful to perform multi-messenger observations of the very early phase of supernova explosions including the shock breakout.
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Measurements of time delays between multiple quasar images produced by strong lensing are reaching a sensitivity that makes them a promising cosmological probe. Future surveys will provide significantly more measurements, reaching unprecedented depth in redshift, making strong lensing time delay (SLTD) observations competitive with other background probes. We forecast constraints on the nature of dark energy from upcoming SLTD surveys, simulating future catalogues with different numbers of lenses distributed up to redshift $zsim 1$ and focusing on cosmological parameters such as the Hubble constant $H_0$ and parametrisations of the dark energy equation of state. We also explore the impact of our ability to precisely model the lens mass profile and its environment, on the forecasted constraints. We find that in the most optimistic cases, SLTD will constrain $H_0$ at the level of $sim 0.1%$, while the CPL equation of state parameters, $w_0$ and $w_a$, can be determined with errors $sigma_{w_0}sim 0.05$ and $sigma_{w_a}sim 0.3$, respectively. Furthermore, we investigate the bias introduced when a wrong cosmological model is assumed for the analysis. We find that the value of $H_0$ could be biased up to $10 sigma$, assuming a perfect knowledge of the lens profile, when a $Lambda$CDM model is used to analyse data that really belong to a $w$CDM cosmology with $w=-0.9$. Based on these findings, we identify a consistency check of the assumed cosmological model in future SLTD surveys, by splitting the dataset in several redshift bins. Depending on the characteristics of the survey, this could provide a smoking gun for dark energy.
Strong-lensing images provide a wealth of information both about the magnified source and about the dark matter distribution in the lens. Precision analyses of these images can be used to constrain the nature of dark matter. However, this requires high-fidelity image reconstructions and careful treatment of the uncertainties of both lens mass distribution and source light, which are typically difficult to quantify. In anticipation of future high-resolution datasets, in this work we leverage a range of recent developments in machine learning to develop a new Bayesian strong-lensing image analysis pipeline. Its highlights are: (A) a fast, GPU-enabled, end-to-end differentiable strong-lensing image simulator; (B) a new, statistically principled source model based on a computationally highly efficient approximation to Gaussian processes that also takes into account pixellation; and (C) a scalable variational inference framework that enables simultaneously deriving posteriors for tens of thousands of lens and source parameters and optimising hyperparameters via stochastic gradient descent. Besides efficient and accurate parameter estimation and lens model uncertainty quantification, the main aim of the pipeline is the generation of training data for targeted simulation-based inference of dark matter substructure, which we will exploit in a companion paper.
Fossil galaxy systems are classically thought to be the end result of galaxy group/cluster evolution, as galaxies experiencing dynamical friction sink to the center of the group potential and merge into a single, giant elliptical that dominates the rest of the members in both mass and luminosity. Most fossil systems discovered lie within $z < 0.2$, which leads to the question: what were these systems progenitors? Such progenitors are expected to have imminent or ongoing major merging near the brightest group galaxy (BGG) that, when concluded, will meet the fossil criteria within the look back time. Since strong gravitational lensing preferentially selects groups merging along the line of sight, or systems with a high mass concentration like fossil systems, we searched the CASSOWARY survey of strong lensing events with the goal of determining if lensing systems have any predisposition to being fossil systems or progenitors. We find that $sim$13% of lensing groups are identified as traditional fossils while only $sim$3% of non-lensing control groups are. We also find that $sim$23% of lensing systems are traditional fossil progenitors compared to $sim$17% for the control sample. Our findings show that strong lensing systems are more likely to be fossil/pre-fossil systems than comparable non-lensing systems. Cumulative galaxy luminosity functions of the lensing and non-lensing groups also indicate a possible, fundamental difference between strong lensing and non-lensing systems galaxy populations with lensing systems housing a greater number of bright galaxies even in the outskirts of groups.
Automated searches for strong gravitational lensing in optical imaging survey datasets often employ machine learning and deep learning approaches. These techniques require more example systems to train the algorithms than have presently been discovered, which creates a need for simulated images as training dataset supplements. This work introduces and summarizes deeplenstronomy, an open-source Python package that enables efficient, large-scale, and reproducible simulation of images of astronomical systems. A full suite of unit tests, documentation, and example notebooks are available at https://deepskies.github.io/deeplenstronomy/ .
We investigate how strong gravitational lensing can test contemporary models of massive elliptical (ME) galaxy formation, by combining a traditional decomposition of their visible stellar distribution with a lensing analysis of their mass distribution. As a proof of concept, we study a sample of three ME lenses, observing that all are composed of two distinct baryonic structures, a `red central bulge surrounded by an extended envelope of stellar material. Whilst these two components look photometrically similar, their distinct lensing effects permit a clean decomposition of their mass structure. This allows us to infer two key pieces of information about each lens galaxy: (i) the stellar mass distribution (without invoking stellar populations models) and (ii) the inner dark matter halo mass. We argue that these two measurements are crucial to testing models of ME formation, as the stellar mass profile provides a diagnostic of baryonic accretion and feedback whilst the dark matter mass places each galaxy in the context of LCDM large scale structure formation. We also detect large rotational offsets between the two stellar components and a lopsidedness in their outer mass distributions, which hold further information on the evolution of each ME. Finally, we discuss how this approach can be extended to galaxies of all Hubble types and what implication our results have for studies of strong gravitational lensing.
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