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Register a new userAdvanced Astrophysics Discovery Technology in the Era of Data Driven Astronomy
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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 in 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.
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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 ever-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.
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.
This report provides an overview of recent work that harnesses the Big Data Revolution and Large Scale Computing to address grand computational challenges in Multi-Messenger Astrophysics, with a particular emphasis on real-time discovery campaigns. Acknowledging the transdisciplinary nature of Multi-Messenger Astrophysics, this document has been prepared by members of the physics, astronomy, computer science, data science, software and cyberinfrastructure communities who attended the NSF-, DOE- and NVIDIA-funded Deep Learning for Multi-Messenger Astrophysics: Real-time Discovery at Scale workshop, hosted at the National Center for Supercomputing Applications, October 17-19, 2018. Highlights of this report include unanimous agreement that it is critical to accelerate the development and deployment of novel, signal-processing algorithms that use the synergy between artificial intelligence (AI) and high performance computing to maximize the potential for scientific discovery with Multi-Messenger Astrophysics. We discuss key aspects to realize this endeavor, namely (i) the design and exploitation of scalable and computationally efficient AI algorithms for Multi-Messenger Astrophysics; (ii) cyberinfrastructure requirements to numerically simulate astrophysical sources, and to process and interpret Multi-Messenger Astrophysics data; (iii) management of gravitational wave detections and triggers to enable electromagnetic and astro-particle follow-ups; (iv) a vision to harness future developments of machine and deep learning and cyberinfrastructure resources to cope with the scale of discovery in the Big Data Era; (v) and the need to build a community that brings domain experts together with data scientists on equal footing to maximize and accelerate discovery in the nascent field of Multi-Messenger Astrophysics.
High-quality, usable, and effective software is essential for supporting astronomers in the discovery-focused tasks of data analysis and visualisation. As the volume, and perhaps more crucially, the velocity of astronomical data grows, the role of the astronomer is changing. There is now an increased reliance on automated and autonomous discovery and decision-making workflows rather than visual inspection. We assert the need for an improved understanding of how astronomers (humans) currently make visual discoveries from data. This insight is a critical element for the future design, development and effective use of cyber-human discovery systems, where astronomers work in close collaboration with automated systems to gain understanding from continuous, real-time data streams. We discuss how relevant human performance data could be gathered, specifically targeting the domains of expertise and skill at visual discovery, and the identification and management of cognitive factors. By looking to other disciplines where human performance is assessed and measured, we propose four early-stage applications that would: (1) allow astronomers to evaluate, and potentially improve, their own visual discovery skills; (2) support just-in-time coaching; (3) enable talent identification; and (4) result in user interfaces that automatically respond to skill level and cognitive state. Throughout, we advocate for the importance of user studies and the incorporation of participatory design and co-design practices into the planning, implementation and evaluation of alternative user interfaces and visual discovery environments.
This paper reviews gravitational wave sources and their detection. One of the most exciting potential sources of gravitational waves are coalescing binary black hole systems. They can occur on all mass scales and be formed in numerous ways, many of which are not understood. They are generally invisible in electromagnetic waves, and they provide opportunities for deep investigation of Einsteins general theory of relativity. Sect. 1 of this paper considers ways that binary black holes can be created in the universe, and includes the prediction that binary black hole coalescence events are likely to be the first gravitational wave sources to be detected. The next parts of this paper address the detection of chirp waveforms from coalescence events in noisy data. Such analysis is computationally intensive. Sect. 2 reviews a new and powerful method of signal detection based on the GPU-implemented summed parallel infinite impulse response filters. Such filters are intrinsically real time alorithms, that can be used to rapidly detect and localise signals. Sect. 3 of the paper reviews the use of GPU processors for rapid searching for gravitational wave bursts that can arise from black hole births and coalescences. In sect. 4 the use of GPU processors to enable fast efficient statistical significance testing of gravitational wave event candidates is reviewed. Sect. 5 of this paper addresses the method of multimessenger astronomy where the discovery of electromagnetic counterparts of gravitational wave events can be used to identify sources, understand their nature and obtain much greater science outcomes from each identified event.
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