Gravitational wave approximants are widely used tools in gravitational-wave +astronomy. They allow for dense coverage of the parameter space of binary black +hole (BBH) mergers for purposes of parameter inference, or, more generally, +match filtering tasks, while avoiding the computationally expensive full +evolution of numerical relativity simulations. However, this comes at a slight +cost in terms of accuracy when compared to numerical relativity waveforms, +depending on the approach. One way to minimize this is by constructing +so-called~\textit{surrogate models} which, instead of using approximate physics +or phenomenological formulae, rather interpolate within the space of numerical +relativity waveforms. In this work, we introduce~\texttt{NRSurNN3dq4}, a +surrogate model for non-precessing BBH merger waveforms powered by neural +networks. By relying on the power of deep learning, this approximant is +remarkably fast and competitively accurate, as it can generate millions of +waveforms in a tenth of a second, while mismatches with numerical relativity +waveforms are restrained below $10^{-3}$. We implement this approximant within +the~\textsc{bilby} framework for gravitational-wave parameter inference, and +show that it it is suitable for parameter estimation tasks.
The Solar Ultraviolet Imaging Telescope(SUIT) onboard Aditya-L1 is an imager +that observes the solar photosphere and chromosphere through observations in +the wavelength range of 200-400 nm. A comprehensive understanding of the plasma +and thermodynamic properties of chromospheric and photospheric morphological +structures requires a large sample statistical study, necessitating the +development of automatic feature detection methods. To this end, we develop the +feature detection algorithm SPACE-SUIT: Solar Phenomena Analysis and +Classification using Enhanced vision techniques for SUIT, to detect and +classify the solar chromospheric features to be observed from SUIT's Mg II k +filter. Specifically, we target plage regions, sunspots, filaments, and +off-limb structures. SPACE uses You Only Look Once(YOLO), a neural +network-based model to identify regions of interest. We train and validate +SPACE using mock-SUIT images developed from Interface Region Imaging +Spectrometer(IRIS) full-disk mosaic images in Mg II k line, while we also +perform detection on Level-1 SUIT data. SPACE achieves an approximate precision +of 0.788, recall 0.863 and MAP of 0.874 on the validation mock SUIT FITS +dataset. Given the manual labeling of our dataset, we perform "self-validation" +by applying statistical measures and Tamura features on the ground truth and +predicted bounding boxes. We find the distributions of entropy, contrast, +dissimilarity, and energy to show differences in the features. These +differences are qualitatively captured by the detected regions predicted by +SPACE and validated with the observed SUIT images, even in the absence of +labeled ground truth. This work not only develops a chromospheric feature +extractor but also demonstrates the effectiveness of statistical metrics and +Tamura features for distinguishing chromospheric features, offering independent +validation for future detection schemes.
We present RUBIX, a fully tested, well-documented, and modular Open Source +tool developed in JAX, designed to forward model IFU cubes of galaxies from +cosmological hydrodynamical simulations. The code automatically parallelizes +computations across multiple GPUs, demonstrating performance improvements over +state-of-the-art codes by a factor of 600. This optimization reduces compute +times from hours to only seconds. RUBIX leverages JAX's auto-differentiation +capabilities to enable not only forward modeling but also gradient computations +through the entire pipeline paving the way for new methodological approaches +such as e.g. gradient-based optimization of astrophysics model parameters. +RUBIX is open-source and available on GitHub: +https://github.com/ufuk-cakir/rubix.
Currently, data-intensive scientific applications require vast amounts of +compute resources to deliver world-leading science. The climate emergency has +made it clear that unlimited use of resources (e.g., energy) for scientific +discovery is no longer acceptable. Future computing hardware promises to be +much more energy efficient, but without better optimized software this cannot +reach its full potential. In this vision paper, we propose a generic AI-driven +co-design methodology, using specialized Large Language Models (like ChatGPT), +to effectively generate efficient code for emerging computing hardware. We +describe how we will validate our methodology with two radio astronomy +applications, with sustainability as the key performance indicator. This paper +is a modified version of our accepted SuperCode project proposal. We present it +here in this form to introduce the vision behind this project and to +disseminate the work in the spirit of Open Science and transparency. An +additional aim is to collect feedback, invite potential collaboration partners +and use-cases to join the project.
With the rapid advancements in Large Language Models (LLMs), LLM-based agents have introduced convenient and user-friendly methods for leveraging tools across various domains. In the field of astronomical observation, the @@ -21,4 +90,25 @@
We present a novel AI-based approach to accelerate conservative-to-primitive +inversion in relativistic hydrodynamics simulations, focusing on hybrid +piecewise polytropic and tabulated equations of state. Traditional root-finding +methods are computationally intensive, particularly in large-scale simulations. +To address this, we employ feedforward neural networks (NNC2PS and NNC2PL), +trained in PyTorch and optimized for GPU inference using NVIDIA TensorRT, +achieving significant speedups with minimal loss in accuracy. The NNC2PS model +achieves $L_1$ and $L_\infty$ errors of $4.54 \times 10^{-7}$ and $3.44 \times +10^{-6}$, respectively, with the NNC2PL model yielding even lower error values. +TensorRT optimization ensures high accuracy, with FP16 quantization offering 7x +faster performance than traditional root-finding methods. Our AI models +outperform conventional CPU solvers, demonstrating enhanced inference times, +particularly for large datasets. We release the scientific software developed +for this work, enabling the validation and extension of our findings. These +results highlight the potential of AI, combined with GPU optimization, to +significantly improve the efficiency and scalability of numerical relativity +simulations.
This report aims to provide gravitational waves data analysts with an +introduction to the ideas and practice of the Pad\'e Filtering method for +disentangling a signal from the noise. Technically it comes to the tracking of +the zeros and singularities of random z-Transforms by noisy Pad\'e +Approximants.