new run happened

index.html

@@ -1,5 +1,74 @@
-search_query=cat:astro-ph.*+AND+lastUpdatedDate:[202412052000+TO+202412112000]&start=0&max_results=5000
-<h1>New astro-ph.* submissions cross listed on physics.data-an, stat.*, cs.AI, cs.LG staritng 202412052000 and ending 202412112000</h1>Feed last updated: 2024-12-10T00:00:00-05:00<a href="http://arxiv.org/pdf/2412.06412v1"><h2>StarWhisper Telescope: Agent-Based Observation Assistant System to
+search_query=cat:astro-ph.*+AND+lastUpdatedDate:[202412062000+TO+202412122000]&start=0&max_results=5000
+<h1>New astro-ph.* submissions cross listed on cs.LG, stat.*, cs.AI, physics.data-an staritng 202412062000 and ending 202412122000</h1>Feed last updated: 2024-12-11T00:00:00-05:00<a href="http://arxiv.org/pdf/2412.06946v1"><h2>NRSurNN3dq4: A Deep Learning Powered Numerical Relativity Surrogate for
+  Binary Black Hole Waveforms</h2></a>Authors:  Osvaldo Gramaxo Freitas, Anastasios Theodoropoulos, Nino Villanueva, Tiago Fernandes, Solange Nunes, José A. Font, Antonio Onofre, Alejandro Torres-Forné, José D. Martin-Guerrero</br>Comments: No comment found</br>Primary Category: gr-qc</br>All Categories: gr-qc, astro-ph.HE, astro-ph.IM, cs.LG</br><p>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.</p></br><a href="http://arxiv.org/pdf/2412.08589v1"><h2>SPACE-SUIT: An Artificial Intelligence based chromospheric feature
+  extractor and classifier for SUIT</h2></a>Authors:  Pranava Seth, Vishal Upendran, Megha Anand, Janmejoy Sarkar, Soumya Roy, Priyadarshan Chaki, Pratyay Chowdhury, Borishan Ghosh, Durgesh Tripathi</br>Comments: No comment found</br>Primary Category: astro-ph.SR</br>All Categories: astro-ph.SR, astro-ph.IM, cs.CV, cs.LG</br><p>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.</p></br><a href="http://arxiv.org/pdf/2412.08265v1"><h2>Fast GPU-Powered and Auto-Differentiable Forward Modeling of IFU Data
+  Cubes</h2></a>Authors:  Ufuk Çakır, Anna Lena Schaible, Tobias Buck</br>Comments: accepted to the Machine Learning and the Physical Sciences Workshop,
+  NeurIPS 2024</br>Primary Category: astro-ph.IM</br>All Categories: astro-ph.IM, astro-ph.GA, physics.comp-ph, physics.data-an</br><p>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.</p></br><a href="http://arxiv.org/pdf/2412.08490v1"><h2>SuperCode: Sustainability PER AI-driven CO-DEsign</h2></a>Authors:  P. Chris Broekema, Rob V. van Nieuwpoort</br>Comments: No comment found</br>Primary Category: astro-ph.IM</br>All Categories: astro-ph.IM, cs.AI</br><p>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.</p></br><a href="http://arxiv.org/pdf/2412.06412v1"><h2>StarWhisper Telescope: Agent-Based Observation Assistant System to
   Approach AI Astrophysicist</h2></a>Authors:  Cunshi Wang, Xinjie Hu, Yu Zhang, Xunhao Chen, Pengliang Du, Yiming Mao, Rui Wang, Yuyang Li, Ying Wu, Hang Yang, Yansong Li, Beichuan Wang, Haiyang Mu, Zheng Wang, Jianfeng Tian, Liang Ge, Yongna Mao, Shengming Li, Xiaomeng Lu, Jinhang Zou, Yang Huang, Ningchen Sun, Jie Zheng, Min He, Yu Bai, Junjie Jin, Hong Wu, Chaohui Shang, Jifeng Liu</br>Comments: 21 pages, 18 figures</br>Primary Category: astro-ph.IM</br>All Categories: astro-ph.IM, cs.AI, cs.CL</br><p>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 @@ <h1>New astro-ph.* submissions cross listed on physics.data-an, stat.*, cs.AI, c
 then add them to the next-day observation lists. Additionally, the integration
 of AI agents within the system provides online accessibility, saving
 astronomers' time and encouraging greater participation from amateur
-astronomers in the NGSS project.</p></br>
+astronomers in the NGSS project.</p></br><a href="http://arxiv.org/pdf/2412.07836v1"><h2>AI-driven Conservative-to-Primitive Conversion in Hybrid Piecewise
+  Polytropic and Tabulated Equations of State</h2></a>Authors:  Semih Kacmaz, Roland Haas, E. A. Huerta</br>Comments: 10 pages, 4 figures, 1 table</br>Primary Category: gr-qc</br>All Categories: gr-qc, astro-ph.IM, cs.AI, physics.comp-ph, J.2; I.2</br><p>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.</p></br><a href="http://arxiv.org/pdf/2412.08254v1"><h2>PADÉ FILTERING, Principles and Use: an Introductory Report</h2></a>Authors:  Jean-Daniel Fournier, Mikhaël Pichot du Mézeray</br>Comments: No comment found</br>Primary Category: gr-qc</br>All Categories: gr-qc, astro-ph.IM, physics.data-an</br><p>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.</p></br>