Optimal Probabilistic Catalogue Matching for Radio Sources

Cross-matching catalogues from radio surveys to catalogues of sources at other wavelengths is extremely hard, because radio sources are often extended, often consist of several spatially separated components, and often no radio component is coincident with the optical/infrared host galaxy. Traditionally, the cross-matching is done by eye, but this does not scale to the millions of radio sources expected from the next generation of radio surveys. We present an innovative automated procedure, using Bayesian hypothesis testing, that models trial radio-source morphologies with putative positions of the host galaxy. This new algorithm differs from an earlier version by allowing more complex radio source morphologies, and performing a simultaneous fit over a large field. We show that this technique performs well in an unsupervised mode.Comment: 9 pages, 7 figure

Enhanced Management of Personal Astronomical Data with FITSManager

Although the roles of data centers and computing centers are becoming more and more important, and on-line research is becoming the mainstream for astronomy, individual research based on locally hosted data is still very common. With the increase of personal storage capacity, it is easy to find hundreds to thousands of FITS files in the personal computer of an astrophysicist. Because Flexible Image Transport System (FITS) is a professional data format initiated by astronomers and used mainly in the small community, data management toolkits for FITS files are very few. Astronomers need a powerful tool to help them manage their local astronomical data. Although Virtual Observatory (VO) is a network oriented astronomical research environment, its applications and related technologies provide useful solutions to enhance the management and utilization of astronomical data hosted in an astronomer's personal computer. FITSManager is such a tool to provide astronomers an efficient management and utilization of their local data, bringing VO to astronomers in a seamless and transparent way. FITSManager provides fruitful functions for FITS file management, like thumbnail, preview, type dependent icons, header keyword indexing and search, collaborated working with other tools and online services, and so on. The development of the FITSManager is an effort to fill the gap between management and analysis of astronomical data.Comment: 12 pages, 9 figures, Accepted for publication in New Astronom

Prompt-NER: Zero-shot Named Entity Recognition in Astronomy Literature via Large Language Models

This study delves into the application of Large Language Models (LLMs) for Named Entity Recognition (NER) tasks in the field of astronomy literature. To enhance the zero-shot recognition capabilities of LLMs for astronomical named entities, we propose a strategy called Prompt-NER. Prompt-NER includes five prompt elements: Task Descriptions, Entity Definitions, Task Emphasis, Task Examples, and Second Conversation. To assess the effectiveness of the Prompt-NER strategy, we utilize three representative LLMs (Claude-2, GPT-3.5, and LLaMA-2-70b) to identify telescope and celestial object named entities in astronomical literature. Our experiments are conducted based on two distinct datasets. The first dataset comprises 30 original PDF documents, which we split into paragraphs in sequential order, resulting in a second dataset consisting of 30 paragraph collections. Additionally, we incorporate 30 astronomical telegrams to diversify our experiments and assess the performance of LLMs based on Prompt-NER on concise, complete texts. Our experimental results indicate that the Prompt-NER strategy enables LLMs to effectively accomplish NER tasks in the field of astronomy, even without prior astronomical knowledge during training. We carefully analyze the experimental results, including the mechanism of different prompt elements and the influence of different features of long and short texts on their respective experimental results. This research provides experience for zero-shot NER tasks in astronomical literature and suggests future work in this area