100 research outputs found
Modeling with the Crowd: Optimizing the Human-Machine Partnership with Zooniverse
LSST and Euclid must address the daunting challenge of analyzing the
unprecedented volumes of imaging and spectroscopic data that these
next-generation instruments will generate. A promising approach to overcoming
this challenge involves rapid, automatic image processing using appropriately
trained Deep Learning (DL) algorithms. However, reliable application of DL
requires large, accurately labeled samples of training data. Galaxy Zoo Express
(GZX) is a recent experiment that simulated using Bayesian inference to
dynamically aggregate binary responses provided by citizen scientists via the
Zooniverse crowd-sourcing platform in real time. The GZX approach enables
collaboration between human and machine classifiers and provides rapidly
generated, reliably labeled datasets, thereby enabling online training of
accurate machine classifiers. We present selected results from GZX and show how
the Bayesian aggregation engine it uses can be extended to efficiently provide
object-localization and bounding-box annotations of two-dimensional data with
quantified reliability. DL algorithms that are trained using these annotations
will facilitate numerous panchromatic data modeling tasks including
morphological classification and substructure detection in direct imaging, as
well as decontamination and emission line identification for slitless
spectroscopy. Effectively combining the speed of modern computational analyses
with the human capacity to extrapolate from few examples will be critical if
the potential of forthcoming large-scale surveys is to be realized.Comment: 5 pages, 1 figure. To appear in Proceedings of the International
Astronomical Unio
VERITAS Very High Energy Observations of the Distant Blazar 1ES 0647+250
We perform an analysis of the long- and short-term variability of the very high energy (VHE; above 100 GeV) gamma-ray emission from the newly-detected distant blazar 1ES 0647+250. Both new and archival data from the VERITAS telescope were examined, and no strong evidence for integral flux variability on any timescale was found. This lack of variability is consistent with the application of current ultra-high energy cosmic ray (UHECR) models, which can produce secondary gamma-ray emission along the line of sight from the blazar; it also allows averaging over multiyear timescales without bias, aiding in the construction of spectral energy distribution plots (SEDs) for 1ES 0647+250. Because of its distance, 1ES 0647+250 is an object of interest for further study, particularly in efforts to constrain models of the extragalactic background light (EBL) and intergalactic magnetic field strength (IGMFs)
Recommendations for the Creation of a Center for Citizen Science
The explosive growth of citizen science has led to myriad independent projects in Minnesota and beyond. Here, we examine whether the field of citizen science would benefit from a center to coordinate efforts and help citizen science practitioners. We present results of a focus group–based needs assessment involving 52 practitioners active in citizen science. The main conclusions are that establishment of a center for citizen science would benefit efforts and that a statewide center should serve multiple functions. Though this process focused on Minnesota, we believe our findings and recommendations are applicable to and would benefit Extension efforts anywhere
Galaxy Zoo: Morphological Classification and Citizen Science
We provide a brief overview of the Galaxy Zoo and Zooniverse projects,
including a short discussion of the history of, and motivation for, these
projects as well as reviewing the science these innovative internet-based
citizen science projects have produced so far. We briefly describe the method
of applying en-masse human pattern recognition capabilities to complex data in
data-intensive research. We also provide a discussion of the lessons learned
from developing and running these community--based projects including thoughts
on future applications of this methodology. This review is intended to give the
reader a quick and simple introduction to the Zooniverse.Comment: 11 pages, 1 figure; to be published in Advances in Machine Learning
and Data Mining for Astronom
Citizen Science: Contributions to Astronomy Research
The contributions of everyday individuals to significant research has grown
dramatically beyond the early days of classical birdwatching and endeavors of
amateurs of the 19th century. Now people who are casually interested in science
can participate directly in research covering diverse scientific fields.
Regarding astronomy, volunteers, either as individuals or as networks of
people, are involved in a variety of types of studies. Citizen Science is
intuitive, engaging, yet necessarily robust in its adoption of sci-entific
principles and methods. Herein, we discuss Citizen Science, focusing on fully
participatory projects such as Zooniverse (by several of the au-thors CL, AS,
LF, SB), with mention of other programs. In particular, we make the case that
citizen science (CS) can be an important aspect of the scientific data analysis
pipelines provided to scientists by observatories.Comment: 12 pages, 2 figure
Galaxy Nurseries: Crowdsourced analysis of slitless spectroscopic data
We present the results of Galaxy Nurseries project, which was designed to
enable crowdsourced analysis of slitless spectroscopic data by volunteer
citizen scientists using the Zooniverse online interface. The dataset was
obtained by the WFC3 Infrared Spectroscopic Parallel (WISP) Survey
collaboration and comprises NIR grism (G102 and G141) and direct imaging.
Volunteers were instructed to evaluate indicated spectral features and decide
whether it was a genuine emission line or more likely an artifact. Galaxy
Nurseries was completed in only 40 days, gathering 414,360 classifications from
3003 volunteers for 27,333 putative emission lines. The results of Galaxy
Nurseries demonstrate the feasibility of identifying genuine emission lines in
slitless spectra by citizen scientists. Volunteer responses for each subject
were aggregated to compute , the fraction of volunteers who
classified the corresponding emission line as "Real". To evaluate the accuracy
of volunteer classifications, their aggregated responses were compared with
independent assessments provided by members of the WISP Survey Science Team
(WSST). Overall, there is a broad agreement between the WSST and volunteers'
classifications, although we recognize that robust scientific analyses
typically require samples with higher purity and completeness than raw
volunteer classifications provide. Nonetheless, choosing optimal threshold
values for allows a large fraction of spurious lines to be
vetoed, substantially reducing the timescale for subsequent professional
analysis of the remaining potential lines.Comment: Accepted for publication in Research Notes of the AA
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