Arvidsson K.,Adler Planetarium |
Kerton C.R.,Iowa State University
Astronomical Journal | Year: 2011
We use SCUBA 850 μm and CO observations to analyze the surroundings of three Galactic ring-like H II regions, KR7, KR 81, and KR 120 (Sh 2-124, Sh 2-165, and Sh 2-187), with the aim of finding sites of triggered star formation. We find one prominent submillimeter (sub-mm) source for each region, located at the interface between the H II region and its neutral surroundings. Using Two Micron All Sky Survey photometry, we find that the prominent sub-mm source for KR 120 probably contains an embedded cluster of young stellar objects (YSOs), making it a likely site for triggered star formation. The KR 7 sub-mm source could possibly contain embedded YSOs, while the KR 81 sub-mm source likely does not. The mass column densities for these dominant sub-mm sources fall in the ∼0.1-0.6gcm-2 range. The mass of the cold, dense material (clumps) seen as the three dominant sub-mm sources falls around ∼100 M ⊙. We use the SCUBA Legacy catalog to characterize the populations of sub-mm sources around the H II regions, and compare them to the sources found around a previously studied similar ring-like H II region (KR 140) and near a massive star-forming region (W3). Finally, we estimate the IR luminosities of the prominent newly detected sub-mm sources and find that they are correlated with the clump mass, consistent with a previously known luminosity-mass relationship which this study shows to be valid over four orders of magnitude in mass. © 2011. The American Astronomical Society. All rights reserved. Source
Agency: NSF | Branch: Standard Grant | Program: | Phase: IUSE | Award Amount: 137.51K | Year: 2015
This project will build upon the infrastructure of Zooniverse.org to create authentic research experiences for introductory astronomy students. Education research indicates that including authentic research in science classes improves attitudes towards science and scientists in a diverse cross-section of students. The curriculum materials will be tested and refined at a broad spectrum of institutional settings before dissemination.
Introductory astronomy generally provides students little insight into the realities of being a scientist. This project will address this deficiency by introducing an authentic research experience for students into the astronomy for non-majors curriculum. It will utilize the classification and meta-data exploration capabilities of the Zooniverse platform. The proposed course curriculum will support students in building foundational research skills and practices through a series of in-class activities and a semester-long group research project. These activities will employ a state-of-the-art online platform to explore data collection, manipulation, and interpretation within the core topics in the curriculum. The project team will assess student learning and attitudinal gains through traditional in-class testing and conceptual questioning that is embedded within the Zooniverse online environment, as well as student interviews. This includes assessing the impact of the research experience on students understanding of the nature of science, conceptual astronomy learning gains (e.g. the Zooniverse Astronomical Concept Survey, Prather et al, 2013), and interest in pursuing a STEM major. The team will also assess the impact of different implementations of the online platform as well as the ease of implementation of the new curricular materials in a variety of institutional settings, course structures, and content focus. They will use the insight gained to develop the most effective curricular and training materials. All curricular materials, instructional guides, online Zooniverse tools, and underlying code will be widely disseminated.
Agency: NSF | Branch: Standard Grant | Program: | Phase: SOCIAL-COMPUTATIONAL SYSTEMS | Award Amount: 395.90K | Year: 2012
The goal of this project is to develop a next-generation socio-computational citizen science platform that combines the efforts of human classifiers with those of computational systems to maximize the efficiency with which human attention can be used. Dealing with the flood of digital data that confronts researchers is the fundamental challenge of twenty-first century research. New techniques, tools and strategies for dealing with massive data sets, whether they consist of vast numbers of base-pair DNA sequences or terabytes of data from all-sky astronomical surveys, present an opportunity to establish a new paradigm of scientific discovery, but the task is not easy. In many areas of research, the relentless growth of data sets has led to the adoption of increasingly automated and unsupervised methods of classification. In many cases, this has led to degradation in classification quality, with machine learning and computer vision unable to replicate the successes of human pattern recognition. The growth of citizen science on the web has provided a temporary solution to this problem, demonstrating that it is possible to recruit hundreds of thousands of volunteers to make an authentic contribution to results, boosting human analysis through the collective wisdom of a crowd of classifiers. However, human classifiers alone will not be able to cope with expected flood of data from future scientific instruments.
This research will be carried out by a partnership between computer and social scientists, addressing research problems both in automated data analysis and social science through systems implementation, alongside field research and experiments with project participants. The intellectual merit of this project lies in its contribution to advancing knowledge and understanding in multiple domains of science. First, the work will contribute to developing new methods of computational data analysis, initially with analysis of astronomical images, and later extending to additional fields. Second, the project includes social science research to test and apply theories of human motivation and learning in an online context, which can then be applied to a broad range of social-computational problems. By mixing human and computational elements, the planned system has the potential to transform the application of citizen science and its approach to data analysis.
This project will advance science while promoting teaching, training and learning. One of the most significant broader impacts for its citizen science activities is enabling a community of hundreds of thousands of volunteers to participate in research, a powerful and rapidly developing form of informal science education. By choosing the relatively generic topic of image classification, beginning with astronomy but not limited to that field of science, the techniques developed under this grant will be of significant value to future investigations in similar research areas, thus enhancing the infrastructure for research and education.
Agency: NSF | Branch: Continuing grant | Program: | Phase: Particle Astrophysics/Cosmic P | Award Amount: 474.63K | Year: 2012
Intellectual Merit VERITAS is currently the most sensitive VHE gamma-ray observatory in the world. Operating at the basecamp of the Fred Lawrence Whipple Observatory in Arizona, the VERITAS project expands the Imaging Atmospheric Cherenkov Telescope technique pioneered by its precursor, the Whipple 10m Telescope, to include an array of four 12m telescopes with a multitelescope trigger for an order-of-magnitude improved sensitivity over the previous generation. VERITAS probes the extreme physics of sources such as the jets of Active Galactic Nuclei, Supernovae Remnants, and microquasars and provides an important complement to the Fermi Gamma-ray Space Telescope. This award will provide support for the VERITAS science and collaboration service efforts of the Adler Planetarium & Astronomy Museum researchers. The Adler group will lead the development and optimization of advanced gamma-ray analysis methods to increase the performance of VERITAS and play a leading role in calibration efforts. The Adlers science efforts will utilize the improved array performance in the study of extended supernova remnants and pulsar wind nebulae focusing on the case of the Crab Nebula, particularly in light of the recent discovery of the rapid bright synchrotron flares detected by Fermi.
Broader Impact The Adler serves as the lead institution for VERITAS Education and Outreach efforts. Beyond the management aspects of the VERITAS EPO effort, the Adler VERITAS scientists will engage in a variety of efforts including hosting undergraduate interns from collaborating VERITAS institutions, participating in the daily Astronomy Conversations held at the Adler Planetariums Space Visualization Laboratory, investigating the feasibility of a Citizen Science project to distinguish gamma rays from cosmic rays, and providing planetarium expertise to the VERITAS project office to develop a VERITAS-themed show.
Agency: NSF | Branch: Continuing grant | Program: | Phase: Cyber-Human Systems (CHS) | Award Amount: 134.86K | Year: 2016
This research aims to improve the efficiency, accuracy, and usability of online systems supporting citizen science, in which communities organized around serious scientific research projects combine the contributions of amateurs and professionals. In order to respond most efficiently to the increasing data deluge across multiple domains, citizen science platforms need to be more dynamic and complex - incorporating intelligent task assignment and machine learning strategies. Systems that make use of both human and machine intelligence are of interest to scientists from a wide range of disciplines. Whether viewed as social machines or as active learning systems in which progressive input from humans improves machine learning, these hybrid systems exhibit complex behavior which needs to be understood for effective system design. For example, machine learning researchers have concentrated on using the large training sets produced by citizen science projects in order to train algorithms that are later applied to a full dataset. Yet this serial processing may not be the most efficient use of the human or machine effort. The main research goal of this project is to investigate how the overall efficiency of the combined human-machine system is impacted by the separate components and their related properties and what the implications are for either human or machine classifiers or both. This process will test the hypothesis that improved overall efficiency will actually reduce the load on expert human classifiers instead of, as currently required, needing larger expert training sets for machines.
This project will investigate the dynamic combination of human and machine classifiers, gaining for the first time knowledge of how load can be optimally shared in a real, flexible citizen science platform. This research effort will be supported by building and deploying software modules on the existing Zooniverse infrastructure, the world-leading platform for online citizen science. It will (1) carry out efficient and dynamic task assignment, distinguishing in near-real time between experienced and inexperienced, and between skilled and less skilled classifiers; and (2) combine human and machine classifications dynamically, periodically training automatic classification routines on the increasing volume of training data produced by volunteers. This new software will then be utilized in a novel cascade filtering mode that reduces complex classification problems into a series of single binary tasks. The software developed in this project will provide domain scientists and social machine researchers who wish to exploit the new infrastructure with a fully flexible suite of functions appropriate to the needs defined by their specific problems.