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Register a new userSports stars: analyzing the performance of astronomers at visualization-based discovery
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In this data-rich era of astronomy, there is a growing reliance on automated techniques to discover new knowledge. The role of the astronomer may change from being a discoverer to being a confirmer. But what do astronomers actually look at when they distinguish between sources and noise? What are the differences between novice and expert astronomers when it comes to visual-based discovery? Can we identify elite talent or coach astronomers to maximize their potential for discovery? By looking to the field of sports performance analysis, we consider an established, domain-wide approach, where the expertise of the viewer (i.e. a member of the coaching team) plays a crucial role in identifying and determining the subtle features of gameplay that provide a winning advantage. As an initial case study, we investigate whether the SportsCode performance analysis software can be used to understand and document how an experienced HI astronomer makes discoveries in spectral data cubes. We find that the process of timeline-based coding can be applied to spectral cube data by mapping spectral channels to frames within a movie. SportsCode provides a range of easy to use methods for annotation, including feature-based codes and labels, text annotations associated with codes, and image-based drawing. The outputs, including instance movies that are uniquely associated with coded events, provide the basis for a training program or team-based analysis that could be used in unison with discipline specific analysis software. In this coordinated approach to visualization and analysis, SportsCode can act as a visual notebook, recording the insight and decisions in partnership with established analysis methods. Alternatively, in situ annotation and coding of features would be a valuable addition to existing and future visualisation and analysis packages.
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The scientific community is presently witnessing an unprecedented growth in the quality and quantity of data sets coming from simulations and real-world experiments. To access effectively and extract the scientific content of such large-scale data sets (often sizes are measured in hundreds or even millions of Gigabytes) appropriate tools are needed. Visual data exploration and discovery is a robust approach for rapidly and intuitively inspecting large-scale data sets, e.g. for identifying new features and patterns or isolating small regions of interest within which to apply time-consuming algorithms. This paper presents a high performance parallelized implementation of Splotch, our previously developed visual data exploration and discovery algorithm for large-scale astrophysical data sets coming from particle-based simulations. Splotch has been improved in order to exploit modern massively parallel architectures, e.g. multicore CPUs and CUDA-enabled GPUs. We present performance and scalability benchmarks on a number of test cases, demonstrating the ability of our high performance parallelized Splotch to handle efficiently large-scale data sets, such as the outputs of the Millennium II simulation, the largest cosmological simulation ever performed.
Astronomers have played many roles in their engagement with the larger astronomy education ecosystem. Their activities have served both the formal and informal education communities worldwide, with levels of involvement from the occasional participant to the full-time professional. We discuss these many diverse roles, giving background, context, and perspective on their value in encouraging and improving astronomy education. This review covers the large amounts of new research on best practices for diverse learning environments. For the formal education learning environment, we cover pre-university roles and engagement activities. This evidence-based perspective can support astronomers in contributing to the broad astronomy education ecosystem in more productive and efficient ways and in identifying new niches and approaches for developing the science capital necessary for a science literate society and for greater involvement of underrepresented groups in the science enterprise.
While both society and astronomy have evolved greatly over the past fifty years, the academic institutions and incentives that shape our field have remained largely stagnant. As a result, the astronomical community is faced with several major challenges, including: (1) the training that we provide does not align with the skills that future astronomers will need, (2) the postdoctoral phase is becoming increasingly demanding and demoralizing, and (3) our jobs are increasingly unfriendly to families with children. Solving these problems will require conscious engineering of our profession. Fortunately, this Decadal Review offers the opportunity to revise outmoded practices to be more effective and equitable. The highest priority of the Subcommittee on the State of the Profession should be to recommend specific, funded activities that will ensure the field meets the challenges we describe.
Societys view of astronomers has changed over time and from culture to culture. This review discusses some of the many ways that astronomers have been perceived by their societies and suggests ways that astronomers can influence public perception of ourselves and our profession in the future.
I analyze the postdoctoral career tracks of a nearly-complete sample of astronomers from 28 United States graduate astronomy and astrophysics programs spanning 13 graduating years (N=1063). A majority of both men and women (65% and 66%, respectively) find long-term employment in astronomy or closely-related academic disciplines. No significant difference is observed in the rates at which men and women are hired into these jobs following their PhDs, or in the rates at which they leave the field. Applying a two-outcome survival analysis model to the entire data set, the relative academic hiring probability ratio for women vs. men at a common year post-PhD is H_(F/M) = 1.08 (+0.20, -0.17; 95% CI); the relative leaving probability ratio is L_(F/M) = 1.03 (+0.31, -0.24). These are both consistent with equal outcomes for both genders (H_(F/M) = L_(F/M) = 1) and rule out more than minor gender differences in hiring or in the decision to abandon an academic career. They suggest that despite discrimination and adversity, women scientists are successful at managing the transition between PhD, postdoctoral, and faculty/staff positions.
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