Conference Proceedings

Goodman AA. A Guide to Comparisons of Star Formation Simulations with Observations. Computational Star Formation [Internet]. 2011. Publisher's VersionAbstract
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 o er the most insight. We o er 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.
Accomazzi A. Astronomy 3.0 Style {E. Isaksson, J. Lagerstrom A H, N. Bawdekar}. Library and Information Services in Astronomy VI: 21st Century Astronomy Librarianship, From New Ideas to Action [Internet]. 2010;433:273. Publisher's Version
Goodman AA. Seeing Science. Proceedings of the International Festival of Scientific Visualization [Internet]. 2009. Publisher's VersionAbstract
The ability to represent scientific data and concepts visually is becoming increasingly important due to the unprecedented exponential growth of computational power during the present digital age. The data sets and simulations scientists in all fields can now create are literally thousands of times as large as those created just 20 years ago. Historically successful methods for data visualization can, and should, be applied to today's huge data sets, but new approaches, also enabled by technology, are needed as well. Increasingly, "modular craftsmanship" will be applied, as relevant functionality from the graphically and technically best tools for a job are combined as-needed, without low-level programming.