arXiv subjects

James Butler

Publications and source records attributed to James Butler.

3 recordsLinked to original sources

arXiv2021

Validating Synthetic Galaxy Catalogs for Dark Energy Science in the LSST Era

Large simulation efforts are required to provide synthetic galaxy catalogs for ongoing and upcoming cosmology surveys. These extragalactic catalogs are being used for many diverse purposes covering a wide range of scientific topics. In order to be useful, they must offer realistically complex information about the galaxies they contain. Hence, it is critical to implement a rigorous validation procedure that ensures that the simulated galaxy properties faithfully capture observations and delivers an assessment of the level of realism attained by the catalog. We present here a suite of validation tests that have been developed by the Rubin Observatory Legacy Survey of Space and Time (LSST) Dark Energy Science Collaboration (DESC). We discuss how the inclusion of each test is driven by the scientific targets for static ground-based dark energy science and by the availability of suitable validation data. The validation criteria that are used to assess the performance of a catalog are flexible and depend on the science goals. We illustrate the utility of this suite by showing examples for the validation of cosmoDC2, the extragalactic catalog recently released for the LSST DESC second Data Challenge.

astro-ph.CO↗

arXiv2019

A Modular Deep Learning Pipeline for Galaxy-Scale Strong Gravitational Lens Detection and Modeling

Upcoming large astronomical surveys are expected to capture an unprecedented number of strong gravitational lensing systems. Deep learning is emerging as a promising practical tool for the detection and quantification of these galaxy-scale image distortions. The absence of large quantities of representative data from current astronomical surveys motivates the development of a robust forward-modeling approach using synthetic lensing images. Using a mock sample of strong lenses created upon a state-of-the-art extragalactic catalogs, we train a modular deep learning pipeline for uncertainty-quantified detection and modeling with intermediate image processing components for denoising and deblending the lensing systems. We demonstrate a high degree of interpretability and controlled systematics due to domain-specific task modules trained with different stages of synthetic image generation. For lens detection and modeling, we obtain semantically meaningful latent spaces that separate classes of strong lens images and yield uncertainty estimates that explain the origin of misclassified images and provide probabilistic predictions for the lens parameters. Validation of the inference pipeline has been carried out using images from the Subaru telescope's Hyper Suprime-Cam camera, and LSST DESC simulated DC2 sky survey catalogues.

astro-ph.IM↗

arXiv2007

Fabrication and Characterization of Two-Dimensional Photonic Crystal Microcavities in Nanocrystalline Diamond

Diamond-based photonic devices offer exceptional opportunity to study cavity QED at room temperature. Here we report fabrication and optical characterization of high quality photonic crystal (PC) microcavities based on nanocrystalline diamond. Fundamental modes near the emission wavelength of negatively charged nitrogen-vacancy (N-V) centers (637 nm) with quality factors (Qs) as high as 585 were observed. Three-dimensional Finite-Difference Time-Domain (FDTD) simulations were carried out and had excellent agreement with experimental results in the values of the mode frequencies. Polarization measurements of the modes were characterized; their anomalous behavior provides important insights to scattering loss in these structures.

cond-mat.mtrl-sci↗