Search

Gravitational lensing is the deflection of light rays due to the gravity of intervening masses. This phenomenon is observed in a variety of scales and configurations, involving any non-uniform mass such as planets, stars, galaxies, clusters of galaxies, and even the large scale structure of the universe. Strong lensing occurs when the distortions are significant and multiple images of the background source are observed. The lens objects must align on the sky of order ~1 arcsecond for galaxy-galaxy lensing, or 10's of arcseonds for cluster-galaxy lensing. As the discovery of lens systems has grown to the low thousands, these systems have become pivotal for precision measurements and addressing critical questions in astrophysics. Notably, they facilitate the measurement of the Universe's expansion rate, dark matter, supernovae, quasars, and the first stars among other topics. With future surveys expected to discover hundreds of thousands of lensing systems, the modelling and simulation of such systems must occur at orders of magnitude larger scale then ever before. Here we present `caustics`, a Python package designed to handle the extensive computational demands of modeling such a vast number of lensing systems., Comment: 13 pages, 3 figures, submitted to JOSS

High-frequency sonar systems deployed on a wide array of ocean observing platforms are creating a deluge of water column sonar data at an unprecedented speed from all corners of the ocean. Efficient and integrative analysis of these data, either across different sonar instruments or with other oceanographic datasets, holds the key to understanding the response of marine ecosystems to the rapidly changing climate. Here we present Echopype, an open-source Python software library designed to address this need. By standardizing water column sonar data from diverse instruments following a community convention and utilizing the widely embraced netCDF data model to encode sonar data as labeled, multi-dimensional arrays, Echopype facilitates intuitive, user-friendly exploration and use of sonar data in an instrument-agnostic manner. By leveraging existing open-source Python libraries optimized for distributed computing, Echopype directly enables computational interoperability and scalability in both local and cloud computing environments. Echopype's modularized package structure further provides a conceptually unified implementation framework for expanding its support for additional instrument raw data formats and incorporating new data analysis functionalities. We envision the continued development of Echopype as a catalyst for making information derived from water column sonar data an integrated component of regional and global ocean observation strategies., Comment: Fix erroneous annotations in use case example flowchart