No Arabic abstract
The answers to fundamental science questions in astrophysics, ranging from the history of the expansion of the universe to the sizes of nearby stars, hinge on our ability to make precise measurements of diverse astronomical objects. As our knowledge of the underlying physics of objects improves along with advances in detectors and instrumentation, the limits on our capability to extract science from measurements is set, not by our lack of understanding of the nature of these objects, but rather by the most mundane of all issues: the precision with which we can calibrate observations in physical units. We stress the need for a program to improve upon and expand the current networks of spectrophotometrically calibrated stars to provide precise calibration with an accuracy of equal to and better than 1% in the ultraviolet, visible and near-infrared portions of the spectrum, with excellent sky coverage and large dynamic range.
The traditional university science curriculum was designed to train specialists in specific disciplines. However, in universities all over the world, science students are going into increasingly diverse careers and the current model does not fit their needs. Advances in technology also make certain modes of learning obsolete. In the last 10 years, the Faculty of Science of the University of Hong Kong has undertaken major curriculum reforms. A sequence of science foundation courses required of all incoming science students are designed to teach science in an integrated manner, and to emphasize the concepts and utilities, not computational techniques, of mathematics. A number of non-discipline specific common core courses have been developed to broaden students awareness of the relevance of science to society and the interdisciplinary nature of science. By putting the emphasis on the scientific process rather than the outcome, students are taught how to identify, formulate, and solve diverse problems.
With the advances in e-Sciences and the growing complexity of scientific analyses, more and more scientists and researchers are relying on workflow systems for process coordination, derivation automation, provenance tracking, and bookkeeping. While workflow systems have been in use for decades, it is unclear whether scientific workflows can or even should build on existing workflow technologies, or they require fundamentally new approaches. In this paper, we analyze the status and challenges of scientific workflows, investigate both existing technologies and emerging languages, platforms and systems, and identify the key challenges that must be addressed by workflow systems for e-science in the 21st century.
Data volumes from multiple sky surveys have grown from gigabytes into terabytes during the past decade, and will grow from terabytes into tens (or hundreds) of petabytes in the next decade. This exponential growth of new data both enables and challenges effective astronomical research, requiring new approaches. Thus far, astronomy has tended to address these challenges in an informal and ad hoc manner, with the necessary special expertise being assigned to e-Science or survey science. However, we see an even wider scope and therefore promote a broader vision of this data-driven revolution in astronomical research. For astronomy to effectively cope with and reap the maximum scientific return from existing and future large sky surveys, facilities, and data-producing projects, we need our own information science specialists. We therefore recommend the formal creation, recognition, and support of a major new discipline, which we call Astroinformatics. Astroinformatics includes a set of naturally-related specialties including data organization, data description, astronomical classification taxonomies, astronomical concept ontologies, data mining, machine learning, visualization, and astrostatistics. By virtue of its new stature, we propose that astronomy now needs to integrate Astroinformatics as a formal sub-discipline within agency funding plans, university departments, research programs, graduate training, and undergraduate education. Now is the time for the recognition of Astroinformatics as an essential methodology of astronomical research. The future of astronomy depends on it.
Modern astronomy has been rapidly increasing our ability to see deeper into the universe, acquiring enormous samples of cosmic populations. Gaining astrophysical insights from these datasets requires a wide range of sophisticated statistical and machine learning methods. Long-standing problems in cosmology include characterization of galaxy clustering and estimation of galaxy distances from photometric colors. Bayesian inference, central to linking astronomical data to nonlinear astrophysical models, addresses problems in solar physics, properties of star clusters, and exoplanet systems. Likelihood-free methods are growing in importance. Detection of faint signals in complicated noise is needed to find periodic behaviors in stars and detect explosive gravitational wave events. Open issues concern treatment of heteroscedastic measurement errors and understanding probability distributions characterizing astrophysical systems. The field of astrostatistics needs increased collaboration with statisticians in the design and analysis stages of research projects, and to jointly develop new statistical methodologies. Together, they will draw more astrophysical insights into astronomical populations and the cosmos itself.
The modern era of scientific global-mean sea-level rise (SLR) projections began in the early 1980s. In subsequent decades, understanding of driving processes has improved, and new methodologies have been developed. Nonetheless, despite more than 70 studies, future SLR remains deeply uncertain. To facilitate understanding of the historical development of SLR projections and contextualize current projections, we have compiled a comprehensive database of 21st century global SLR projections. Although central estimates of 21st century global-mean SLR have been relatively consistent, the range of projected SLR has varied greatly over time. Among studies providing multiple estimates, the range of upper projections shrank from 1.3 to 1.8 m during the 1980s to 0.6 to 0.9 m in 2007, before expanding again to 0.5 to 2.5 m since 2013. Upper projections of SLR from individual studies are generally higher than upper projections from the Intergovernmental Panel on Climate Change, potentially due to differing percentile bounds, or a pre-disposition of consensus-based approaches toward relatively conservative outcomes.