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JAXtronomy: A JAX port of lenstronomy

Gravitational lensing is a phenomenon where light bends around massive objects, resulting in distorted images seen by an observer. Studying gravitationally lensed systems provides insights into cosmology and astrophysics, including constraints of the expansion rate of the Universe and the distribution of dark matter. Thus, we introduce JAXtronomy, a re-implementation of the gravitational lensing software package lenstronomy (Birrer, 2021; Birrer & Amara, 2018) using JAX (Bradbury et al., 2018). JAX is a Python library that uses an accelerated linear algebra (XLA) compiler to improve the performance of computing software. Our core design principle of JAXtronomy is to maintain an identical API to that of lenstronomy. The main JAX features utilized in JAXtronomy are just-in-time compilation, which can lead to significant reductions in execution time, and automatic differentiation, which allows for the implementation of gradient-based algorithms that were previously impossible. Additionally, JAX allows code to be run on GPUs or parallelized across CPU cores, further boosting the performance of JAXtronomy.

astronomy↗

Introduction to: Atlantic Meridional Overturning Circulation(AMOC)

A striking conclusion of the Intergovernmental Panel on Climate Change 2007 report is the crucial role that the Atlantic Meridional Overturning Circulation (AMOC) may play in anthropogenic climate change. However, these IPCC coupled climate simulations show a broad range of uncertainty in the magnitude and timing of AMOC transport change ranging from none to nearly complete collapse within the 21st century. The potential consequences of large changes in the characteristics of AMOC have motivated the creation in the United States of an interagency program and implementation plan to develop monitoring and prediction capabilities for the AMOC This program parallels the development of substantial monitoring efforts by European, South American and African countries -- notably the UK Rapid and Rapid-Watch programs. The papers contained in this volume are derived from presentations at the First U.S. Atlantic Meridional Overturning Circulation (AMOC) Meeting held 4 - 6 May, 2009 to review the US implementation plan and its coordination with other monitoring activities. The Atlantic Meridional Overturning Circulation consists of multiple components illustrated in an attached figure. Water enters the South Atlantic at upper and intermediate depths through both western and eastern routes (where eddy transport is especially important) and is transported northward across the equator, where it recirculates within the northern subtropical and subpolar gyres. The northern end is defined by the sinking regions of the Nordic Seas and the Labrador Sea where the waters that eventually form the upper and lower branches of North Atlantic Deep Water are conditioned. High surface salinities, the result of high net evaporation in the tropics and subtropics (including the Mediterranean Sea), and presence of regions of the Arctic Ocean that remain ice-free even in winter allow for the rapid cooling and thus densification of surface water. This dense surface water becomes the source of deep water formation in the sinking regions. In addition to transporting mass, the AMOC transports roughly half of the total amount of heat carried northward through the northern subtropics (down the temperature-gradient) by the ocean. In contrast in the Southern Hemisphere AMOC transports heat up-gradient from the cool Circumpolar Current to the warm tropics. Paleoevidence suggests that AMOC heat transport in the two hemispheres has varied over time in ways intimately tied to millennial changes in the Earth's climate. In one example, the abrupt Younger Dryas spell of cold weather over the North Atlantic, which began 13,000 years ago, has generally been linked to a millennial shutdown of the AMOC as a result of massive freshwater discharge from the North American continent. The current AMOC monitoring array consists of a series of instrumented transects located across key passages (see Cunningham et al., 2010 for a recent review). In the Arctic and sub-Arctic, transects cross Fram Strait, Denmark Strait and the Faroe Channel (connecting Greenland, Iceland, and the United Kingdom), as well as the entrance to the Labrador Sea. Further south and extending outwards from the east coast of North America there are a series of monitoring arrays including arrays of the Canadian Atlantic Zone Monitoring Program, deployments of the Rapid Western Atlantic Variability Experiment (WAVE), Line W at 39 N, as well as the Rapid-MOC moored array. The latter spans the entire Atlantic basin along 26.5 N. At tropical latitudes we have the Meridional Overturning Variability Experiment (MOVE) array at 16 N, while in the Southern Hemisphere a corresponding basin-spanning transect is being established at the latitude of Cape of Good Hope, complemented by arrays at Drake Passage.

Hakkinen, Sirpa↗

Encoder–decoder neural network for solving the nonlinear Fokker–Planck–Landau collision operator in XGC

An encoder–decoder neural network has been used to examine the possibility for acceleration of a partial integro-differential equation, the Fokker–Planck–Landau collision operator. This is part of the governing equation in the massively parallel particle-in-cell code XGC, which is used to study turbulence in fusion energy devices. The neural network emphasizes physics-inspired learning, where it is taught to respect physical conservation constraints of the collision operator by including them in the training loss, along with the ℓ 2 loss. In particular, network architectures used for the computer vision task of semantic segmentation have been used for training. A penalization method is used to enforce the ‘soft’ constraints of the system and integrate error in the conservation properties into the loss function. During training, quantities representing the particle density, momentum and energy for all species of the system are calculated at each configuration vertex, mirroring the procedure in XGC. This simple training has produced a median relative loss, across configuration space, of the order of 10 –4 , which is low enough if the error is of random nature, but not if it is of drift nature in time steps. The run time for the current Picard iterative solver of the operator is O(n 2 ), where n is the number of plasma species. As the XGC1 code begins to attack problems including a larger number of species, the collision operator will become expensive computationally, making the neural network solver even more important, especially since its training only scales as O(n). Here, a wide enough range of collisionality has been considered in the training data to ensure the full domain of collision physics is captured. An advanced technique to decrease the losses further will be subject of a subsequent report. Eventual work will include expansion of the network to include multiple plasma species.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗