Abstracts of Interest
Selected by:
Fedor Tairli
Abstract: 2312.01005
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Title:Generating Images of the M87* Black Hole Using GANs
Download PDFAbstract:In this paper, we introduce a novel data augmentation methodology based on Conditional Progressive Generative Adversarial Networks (CPGAN) to generate diverse black hole (BH) images, accounting for variations in spin and electron temperature prescriptions. These generated images are valuable resources for training deep learning algorithms to accurately estimate black hole parameters from observational data. Our model can generate BH images for any spin value within the range of [-1, 1], given an electron temperature distribution. To validate the effectiveness of our approach, we employ a convolutional neural network to predict the BH spin using both the GRMHD images and the images generated by our proposed model. Our results demonstrate a significant performance improvement when training is conducted with the augmented dataset while testing is performed using GRMHD simulated data, as indicated by the high R2 score. Consequently, we propose that GANs can be employed as cost effective models for black hole image generation and reliably augment training datasets for other parameterization algorithms.
Abstract: 2312.02091
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Title:Physics simulation capabilities of LLMs
Download PDFAbstract:[Abridged abstract] Large Language Models (LLMs) can solve some undergraduate-level to graduate-level physics textbook problems and are proficient at coding. Combining these two capabilities could one day enable AI systems to simulate and predict the physical world.
We present an evaluation of state-of-the-art (SOTA) LLMs on PhD-level to research-level computational physics problems. We condition LLM generation on the use of well-documented and widely-used packages to elicit coding capabilities in the physics and astrophysics domains. We contribute $\sim 50$ original and challenging problems in celestial mechanics (with REBOUND), stellar physics (with MESA), 1D fluid dynamics (with Dedalus) and non-linear dynamics (with SciPy). Since our problems do not admit unique solutions, we evaluate LLM performance on several soft metrics: counts of lines that contain different types of errors (coding, physics, necessity and sufficiency) as well as a more "educational" Pass-Fail metric focused on capturing the salient physical ingredients of the problem at hand.
As expected, today's SOTA LLM (GPT4) zero-shot fails most of our problems, although about 40\% of the solutions could plausibly get a passing grade. About $70-90 \%$ of the code lines produced are necessary, sufficient and correct (coding \& physics). Physics and coding errors are the most common, with some unnecessary or insufficient lines. We observe significant variations across problem class and difficulty. We identify several failure modes of GPT4 in the computational physics domain.
Our reconnaissance work provides a snapshot of current computational capabilities in classical physics and points to obvious improvement targets if AI systems are ever to reach a basic level of autonomy in physics simulation capabilities.
Abstract: 2312.02130
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Title:Fundamental Physics Opportunities with the Next-Generation Event Horizon Telescope
Download PDFAbstract:The Event Horizon Telescope (EHT) Collaboration recently published the first images of the supermassive black holes in the cores of the Messier 87 and Milky Way galaxies. These observations have provided a new means to study supermassive black holes and probe physical processes occurring in the strong-field regime. We review the prospects of future observations and theoretical studies of supermassive black hole systems with the next-generation Event Horizon Telescope (ngEHT), which will greatly enhance the capabilities of the existing EHT array. These enhancements will open up several previously inaccessible avenues of investigation, thereby providing important new insights into the properties of supermassive black holes and their environments. This review describes the current state of knowledge for five key science cases, summarising the unique challenges and opportunities for fundamental physics investigations that the ngEHT will enable.
Abstract: 2312.02645
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Title:Constraints on UHECR sources and extragalactic magnetic fields from directional anisotropies
Download PDFAbstract:A dipole anisotropy in ultra-high-energy cosmic ray (UHECR) arrival directions, of extragalactic origin, is now firmly established at energies E > 8 EeV. Furthermore, the UHECR angular power spectrum shows no power at smaller angular scales than the dipole, apart from hints of possible individual hot or warm spots for energy thresholds $\gtrsim$40 EeV. Here, we exploit the magnitude of the dipole and the limits on smaller-scale anisotropies to place constraints on two quantities: the extragalactic magnetic field (EGMF) and the number density of UHECR sources or the volumetric event rate if UHECR sources are transient. We also vary the bias between the extragalactic matter and the UHECR source densities, reflecting whether UHECR sources are preferentially found in over- or under-dense regions, and find that little or no bias is favored. We follow Ding et al. (2021) in using the Cosmic Flows 2 density distribution of the local universe as our baseline distribution of UHECR sources, but we improve and extend that work by employing an accurate and self-consistent treatment of interactions and energy losses during propagation. Deflections in the Galactic magnetic field are treated using both the full JF12 magnetic field model, with random as well as coherent components, or just the coherent part, to bracket the impact of the GMF on the dipole anisotropy. This Large Scale Structure (LSS) model gives good agreement with both the direction and magnitude of the measured dipole anisotropy and forms the basis for simulations of discrete sources and the inclusion of EGMF effects.
Abstract: 2312.03064
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Title:A robust method for filling in masked data in astronomical images
Download PDFAbstract:Astronomical images often have regions with missing or unwanted information, such as bad pixels, bad columns, cosmic rays, masked objects, or residuals from imperfect model subtractions. In certain situations it can be essential, or preferable, to fill in these regions. Most existing methods use low order interpolations for this task. In this paper a method is described that uses the full information that is contained in the pixels just outside masked regions. These edge pixels are extrapolated inwards, using iterative median filtering. This leads to a smoothly varying spatial resolution within the filled-in regions, and ensures seamless transitions between masked pixels and good pixels. Gaps in continuous, narrow features can be reconstructed with high fidelity, even if they are large. The method is implemented in maskfill, an open-source MIT licensed Python script. Its performance is illustrated with several examples.
Abstract: 2312.04299
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Title:The Extremely Large Telescope
Download PDFAbstract:Extremely large telescopes (ELTs) are considered worldwide to be one of the highest priorities in ground-based astronomy. The European Southern Observatory (ESO) is developing an ELT that will have a 39 m main mirror and will be the largest visible and infrared light telescope in the world. The ELT will be equipped with a lineup of cutting-edge instruments, designed to cover a wide range of scientific possibilities. The leap forwards with the ELT can lead to a paradigm shift in our perception of the Universe, much as Galileo's telescope did 400 years ago. We illustrate here the various components of the ELT, including the dome and main structure, the five mirrors, and the telescope systems. We then describe the ELT instrumentation and some of the astronomical topics it will address. We then conclude by examining the synergies with other astronomical facilities.
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