We apply Support Vector Machines (SVMs)—a machine learning algorithm—to the task of classifying structures in the interstellar medium (ISM). As a case study, we present a position-position-velocity (PPV) data cube of 12 CO J = 3-2 emission toward G16.05-0.57, a supernova remnant that lies behind the M17 molecular cloud. Despite the fact that these two objects partially overlap in PPV space, the two structures can easily be distinguished by eye based on their distinct morphologies. The SVM algorithm is able to infer these morphological distinctions, and associate individual pixels with each object at >90% accuracy. This case study suggests that similar techniques may be applicable to classifying other structures in the ISM—a task that has thus far proven difficult to automate.
The VAO (Virtual Astronomical Observatory) Science Council (VAO-SC) met on July 27-28, 2011 at the Harvard-Smithsonian Center for Astrophysics in Cambridge MA, to review the VAO performance during its first year of operations. In this meeting the VAO demonstrated the new tools for astronomers that are being released in September 2011 and presented plans for the second year of activities, resulting from studies conducted during the first year. This document contains the recommendations of the VAO-SC for the second year of activity of the VAO.
About 50 participants came to a discussion on the benefits and potential obstacles of using astronomy visualization tools for education and public outreach (EPO). Representatives of five different EPO organizations shared information on their project goals and outcomes. Public users need support to learn how to use these programs effectively for education, but the efforts are worthwhile because the thrill that comes from working with real data and the natural beauty of astronomical imagery are great attractors for new science enthusiasts.
Abstract. We review an approach to observation-theory comparisons we call \Taste-Testing."
In this approach, synthetic observations are made of numerical simulations, and then both real
and synthetic observations are \tasted" (compared) using a variety of statistical tests. We rst
lay out arguments for bringing theory to observational space rather than observations to theory
space. Next, we explain that generating synthetic observations is only a step along the way to
the quantitative, statistical, taste tests that oer the most insight. We oer a set of examples
focused on polarimetry, scattering and emission by dust, and spectral-line mapping in starforming
regions. We conclude with a discussion of the connection between statistical tests used
to date and the physics we seek to understand. In particular, we suggest that the \lognormal"
nature of molecular clouds can be created by the interaction of many random processes, as can
the lognormal nature of the IMF, so that the fact that both the \Clump Mass Function" (CMF)
and IMF appear lognormal does not necessarily imply a direct relationship between them.
In the coming era of data-intensive science, it will be increasingly important to be able to seamlessly move between scientific results, the data analyzed in them, and the processes used to produce them. As observations, derived data products, publications, and object metadata are curated by different projects and archived in different locations, establishing the proper linkages between these resources and describing their relationships becomes an essential activity in their curation and preservation. In this paper we describe initial efforts to create a semantic knowledge base allowing easier integration and linking of the body of heterogeneous astronomical resources which we call the Virtual Observatory (VO). The ultimate goal of this effort is the creation of a semantic layer over existing resources, allowing applications to cross boundaries between archives. The proposed approach follows the current best practices in Semantic Computing and the architecture of the web, allowing the use of off-the-shelf technologies and providing a path for VO resources to become part of the global web of linked data.