Domain Adaptation for Measurements of Strong Gravitational Lenses

Upcoming surveys are predicted to discover galaxy-scale strong lenses on the order of $10^5$, making deep learning methods necessary in lensing data analysis. Currently, there is insufficient real lensing data to train deep learning algorithms, but the alternative of training only on simulated data results in poor performance on real data. Domain Adaptation may be able to bridge the gap between simulated and real datasets. We utilize domain adaptation for the estimation of Einstein radius ($\Theta_E$) in simulated galaxy-scale gravitational lensing images with different levels of observational realism. We evaluate two domain adaptation techniques - Domain Adversarial Neural Networks (DANN) and Maximum Mean Discrepancy (MMD). We train on a source domain of simulated lenses and apply it to a target domain of lenses simulated to emulate noise conditions in the Dark Energy Survey (DES). We show that both domain adaptation techniques can significantly improve the model performance on the more complex target domain dataset. This work is the first application of domain adaptation for a regression task in strong lensing imaging analysis. Our results show the potential of using domain adaptation to perform analysis of future survey data with a deep neural network trained on simulated data.Comment: Accepted in Machine Learning and the Physical Sciences Workshop at NeurIPS 2023; 9 pages, 2 figures, 2 table

Self-Driving Telescopes: Autonomous Scheduling of Astronomical Observation Campaigns with Offline Reinforcement Learning

Modern astronomical experiments are designed to achieve multiple scientific goals, from studies of galaxy evolution to cosmic acceleration. These goals require data of many different classes of night-sky objects, each of which has a particular set of observational needs. These observational needs are typically in strong competition with one another. This poses a challenging multi-objective optimization problem that remains unsolved. The effectiveness of Reinforcement Learning (RL) as a valuable paradigm for training autonomous systems has been well-demonstrated, and it may provide the basis for self-driving telescopes capable of optimizing the scheduling for astronomy campaigns. Simulated datasets containing examples of interactions between a telescope and a discrete set of sky locations on the celestial sphere can be used to train an RL model to sequentially gather data from these several locations to maximize a cumulative reward as a measure of the quality of the data gathered. We use simulated data to test and compare multiple implementations of a Deep Q-Network (DQN) for the task of optimizing the schedule of observations from the Stone Edge Observatory (SEO). We combine multiple improvements on the DQN and adjustments to the dataset, showing that DQNs can achieve an average reward of 87%+-6% of the maximum achievable reward in each state on the test set. This is the first comparison of offline RL algorithms for a particular astronomical challenge and the first open-source framework for performing such a comparison and assessment task.Comment: Accepted in Machine Learning and the Physical Sciences Workshop at NeurIPS 2023; 6 pages, 5 figure

Virtual Sky Surveys and Multi-wavelength Investigations of Galaxy Clusters.

The advent of large and overlapping sky surveys brings promise of a new era in the study of galaxy clusters and dark energy. Clusters have been used for decades as faithful buoys of space-time, tracing cosmic evolution through their matter content and spatial distribution. High-fidelity tracking relies on a robust connection between observable cluster signatures and the underlying dark matter content, which is otherwise invisible. Until now, clusters have been mostly viewed through independent signals in distinct wavebands. The next era of cluster cosmology may be led by multi-variate, cross-waveband detections and analyses of clusters, where different facets of clusters can be cross-correlated to develop a more com- plete, unified picture of cluster populations. To these ends, in this dissertation, I perform multi-variate analyses of galaxy cluster populations and develop a simulated sky survey, with which to prepare for the next generation of multi-wavelength cluster observations. First, in a new multi-variate framework, I quantify the effects of observational biases on measures of the cluster distribution function and on cosmological constraints derived from X-ray cluster populations. I also demonstrate the indispensability of the multi-variate approach in measuring the evolution of X-ray galaxy clusters; without it, we find that the combination of scatter, intrinsic correlation and irrevocable survey flux limits substantially confuses any measure of redshift evolution. Next, I construct the Millennium Gas Simulation-Virtual Sky Survey (MGSVSS), a multi-wavelength mock sky derived from an N-body gas-dynamic simulation. The MGSVSS contains both sub-mm and optical wave- length sky signals to redshift, z = 1, in a 5x5deg^2 field of view, with ~10^3 halos, ~10^4 optically selected clusters, and ~10^2 clusters selected via the Sunyaev-Zel’dovich (SZ) signature. The SZ sky also includes a minimal level of sky and instrumental noise, which nearly mimics that of modern SZ cluster surveys. I have performed cluster-finding in the optical and sub-mm wavebands (independently) and obtained a scaling relation between the SZ decrement and the optical richness of independently observed clusters. In this preliminary exercise, I begin to address issues regarding SZ-optical cross-correlation and the optimization of cluster-finding methods toward both cross-correlation and joint finding.Ph.D.PhysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/78950/1/bnord_1.pd