ترغب بنشر مسار تعليمي؟ اضغط هنا

Advanced Data Visualization in Astrophysics: the X3D Pathway

160   0   0.0 ( 0 )
 نشر من قبل Fr\\'ed\\'eric P.A. Vogt
 تاريخ النشر 2015
  مجال البحث فيزياء
والبحث باللغة English




اسأل ChatGPT حول البحث

Most modern astrophysical datasets are multi-dimensional; a characteristic that can nowadays generally be conserved and exploited scientifically during the data reduction/simulation and analysis cascades. Yet, the same multi-dimensional datasets are systematically cropped, sliced and/or projected to printable two-dimensional (2-D) diagrams at the publication stage. In this article, we introduce the concept of the X3D pathway as a mean of simplifying and easing the access to data visualization and publication via three-dimensional (3-D) diagrams. The X3D pathway exploits the facts that 1) the X3D 3-D file format lies at the center of a product tree that includes interactive HTML documents, 3-D printing, and high-end animations, and 2) all high-impact-factor & peer-reviewed journals in Astrophysics are now published (some exclusively) online. We argue that the X3D standard is an ideal vector for sharing multi-dimensional datasets, as it provides direct access to a range of different data visualization techniques, is fully-open source, and is a well defined ISO standard. Unlike other earlier propositions to publish multi-dimensional datasets via 3-D diagrams, the X3D pathway is not tied to specific software (prone to rapid and unexpected evolution), but instead compatible with a range of open-source software already in use by our community. The interactive HTML branch of the X3D pathway is also actively supported by leading peer-reviewed journals in the field of Astrophysics. Finally, this article provides interested readers with a detailed set of practical astrophysical examples designed to act as a stepping stone towards the implementation of the X3D pathway for any other dataset.



قيم البحث

اقرأ أيضاً

Experience suggests that structural issues in how institutional Astrophysics approaches data-driven science and the development of discovery technology may be hampering the communitys ability to respond effectively to a rapidly changing environment i n which increasingly complex, heterogeneous datasets are challenging our existing information infrastructure and traditional approaches to analysis. We stand at the confluence of a new epoch of multimessenger science, remote co-location of data and processing power and new observing strategies based on miniaturized spacecraft. Significant effort will be required by the community to adapt to this rapidly evolving range of possible discovery moduses. In the suggested creation of a new Astrophysics element, Advanced Astrophysics Discovery Technology, we offer an affirmative solution that places the visibility of discovery technologies at a level that we suggest is fully commensurate with their importance to the future of the field.
We present a state-of-the-art report on visualization in astrophysics. We survey representative papers from both astrophysics and visualization and provide a taxonomy of existing approaches based on data analysis tasks. The approaches are classified based on five categories: data wrangling, data exploration, feature identification, object reconstruction, as well as education and outreach. Our unique contribution is to combine the diverse viewpoints from both astronomers and visualization experts to identify challenges and opportunities for visualization in astrophysics. The main goal is to provide a reference point to bring modern data analysis and visualization techniques to the rich datasets in astrophysics.
We review some aspects of the current state of data-intensive astronomy, its methods, and some outstanding data analysis challenges. Astronomy is at the forefront of big data science, with exponentially growing data volumes and data rates, and an eve r-increasing complexity, now entering the Petascale regime. Telescopes and observatories from both ground and space, covering a full range of wavelengths, feed the data via processing pipelines into dedicated archives, where they can be accessed for scientific analysis. Most of the large archives are connected through the Virtual Observatory framework, that provides interoperability standards and services, and effectively constitutes a global data grid of astronomy. Making discoveries in this overabundance of data requires applications of novel, machine learning tools. We describe some of the recent examples of such applications.
The past year has witnessed discovery of the first identified counterparts to a gravitational wave transient (GW 170817A) and a very high-energy neutrino (IceCube-170922A). These source identifications, and ensuing detailed studies, have realized lon gstanding dreams of astronomers and physicists to routinely carry out observations of cosmic sources by other than electromagnetic means, and inaugurated the era of multi-messenger astronomy. While this new era promises extraordinary physical insights into the universe, it brings with it new challenges, including: highly heterogeneous, high-volume, high-velocity datasets; globe-spanning cross-disciplinary teams of researchers, regularly brought together into transient collaborations; an extraordinary breadth and depth of domain-specific knowledge and computing resources required to anticipate, model, and interpret observations; and the routine need for adaptive, distributed, rapid-response observing campaigns to fully exploit the scientific potential of each source. We argue, therefore, that the time is ripe for the community to conceive and propose an Institute for Multi-Messenger Astrophysics that would coordinate its resources in a sustained and strategic fashion to efficiently address these challenges, while simultaneously serving as a center for education and key supporting activities. In this fashion, we can prepare now to realize the bright future that we see, beyond, through these newly opened windows onto the universe.
NASA regards data handling and archiving as an integral part of space missions, and has a strong track record of serving astrophysics data to the public, beginning with the the IRAS satellite in 1983. Archives enable a major science return on the sig nificant investment required to develop a space mission. In fact, the presence and accessibility of an archive can more than double the number of papers resulting from the data. In order for the community to be able to use the data, they have to be able to find the data (ease of access) and interpret the data (ease of use). Funding of archival research (e.g., the ADAP program) is also important not only for making scientific progress, but also for encouraging authors to deliver data products back to the archives to be used in future studies. NASA has also enabled a robust system that can be maintained over the long term, through technical innovation and careful attention to resource allocation. This article provides a brief overview of some of NASAs major astrophysics archive systems, including IRSA, MAST, HEASARC, KOA, NED, the Exoplanet Archive, and ADS.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا