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Register a new userAstronomical Data Formats: What we have and how we got here
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Jessica D. Mink
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Despite almost all being acquired as photons, astronomical data from different instruments and at different stages in its life may exist in different formats to serve different purposes. Beyond the data itself, descriptive information is associated with it as metadata, either included in the data format or in a larger multi-format data structure. Those formats may be used for the acquisition, processing, exchange, and archiving of data. It has been useful to use similar formats, or even a single standard to ease interaction with data in its various stages using familiar tools. Knowledge of the evolution and advantages of present standards is useful before we discuss the future of how astronomical data is formatted. The evolution of the use of world coordinates in FITS is presented as an example.
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Anonymous peer review is used by the great majority of computer science conferences. OpenReview is such a platform that aims to promote openness in peer review process. The paper, (meta) reviews, rebuttals, and final decisions are all released to public. We collect 5,527 submissions and their 16,853 reviews from the OpenReview platform. We also collect these submissions citation data from Google Scholar and their non-peer-review
The Astrophysics Source Code Library (ASCL) is a free online registry of research codes; it is indexed by ADS and Web of Science and has over 1300 code entries. Its entries are increasingly used to cite software; citations have been doubling each year since 2012 and every major astronomy journal accepts citations to the ASCL. Codes in the resource cover all aspects of astrophysics research and many programming languages are represented. In the past year, the ASCL added dashboards for users and administrators, started minting Digital Objective Identifiers (DOIs) for software it houses, and added metadata fields requested by users. This presentation covers the ASCLs growth in the past year and the opportunities afforded it as one of the few domain libraries for science research codes.
Deep learning has led to significant improvement in text summarization with various methods investigated and improved ROUGE scores reported over the years. However, gaps still exist between summaries produced by automatic summarizers and human professionals. Aiming to gain more understanding of summarization systems with respect to their strengths and limits on a fine-grained syntactic and semantic level, we consult the Multidimensional Quality Metric(MQM) and quantify 8 major sources of errors on 10 representative summarization models manually. Primarily, we find that 1) under similar settings, extractive summarizers are in general better than their abstractive counterparts thanks to strength in faithfulness and factual-consistency; 2) milestone techniques such as copy, coverage and hybrid extractive/abstractive methods do bring specific improvements but also demonstrate limitations; 3) pre-training techniques, and in particular sequence-to-sequence pre-training, are highly effective for improving text summarization, with BART giving the best results.
In astronomical and cosmological studies one often wishes to infer some properties of an infinite-dimensional field indexed within a finite-dimensional metric space given only a finite collection of noisy observational data. Bayesian inference offers an increasingly-popular strategy to overcome the inherent ill-posedness of this signal reconstruction challenge. However, there remains a great deal of confusion within the astronomical community regarding the appropriate mathematical devices for framing such analyses and the diversity of available computational procedures for recovering posterior functionals. In this brief research note I will attempt to clarify both these issues from an applied statistics perpective, with insights garnered from my post-astronomy experiences as a computational Bayesian / epidemiological geostatistician.
A year after emph{Fermi} was launched, the number of known gamma-ray pulsars has increased dramatically. For the first time, a sizable population of pulsars has been discovered in gamma-ray data alone. For the first time, millisecond pulsars have been confirmed as powerful sources of gamma-ray emission, and a whole population of these objects is seen with the LAT. The remaining gamma-ray pulsars are young pulsars, discovered via an efficient collaboration with radio and X-ray telescopes. It is now clear that a large fraction of the nearby energetic pulsars are gamma-ray emitters, whose luminosity grows with the spin-down energy loss rate. Many previously unidentified EGRET sources turn out to be pulsars. Many of the detected pulsars are found to be powering pulsar wind nebulae, and some are associated with TeV sources. The emph{Fermi} LAT is expected to detect more pulsars in gamma rays in the coming years, while multi-wavelength follow ups should detect emph{Fermi}-discovered pulsars. The data already revealed that gamma-ray pulsars generally emit fan-like beams sweeping over a large fraction of the sky and produced in the outer magnetosphere.
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