PLANET HUNTERS: ASSESSING THE<i>KEPLER</i>INVENTORY OF SHORT-PERIOD PLANETS

Schwamb, Megan E.; Lintott, Chris; Fischer, Debra A.; Giguere, Matthew J.; Lynn, Stuart; Smith, Arfon M.; Brewer, John M.; Parrish, Michael; Schawinski, Kevin; Simpson, Robert J.

doi:10.1088/0004-637x/754/2/129

Cited by 76 publications

(69 citation statements)

References 52 publications

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“…) or artificially generated (eg Planet Hunters; Schwamb et al . ) vouchers into voucher sets given to volunteers for classification in order to evaluate ongoing volunteer performance.…”

Section: Techniques For Producing High‐quality Ecological Citizen‐scimentioning

confidence: 99%

Assessing data quality in citizen science

Kosmala

Wiggins

Swanson

et al. 2016

Frontiers in Ecol & Environ

645

561

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Ecological and environmental citizen‐science projects have enormous potential to advance scientific knowledge, influence policy, and guide resource management by producing datasets that would otherwise be infeasible to generate. However, this potential can only be realized if the datasets are of high quality. While scientists are often skeptical of the ability of unpaid volunteers to produce accurate datasets, a growing body of publications clearly shows that diverse types of citizen‐science projects can produce data with accuracy equal to or surpassing that of professionals. Successful projects rely on a suite of methods to boost data accuracy and account for bias, including iterative project development, volunteer training and testing, expert validation, replication across volunteers, and statistical modeling of systematic error. Each citizen‐science dataset should therefore be judged individually, according to project design and application, and not assumed to be substandard simply because volunteers generated it.

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“…) or artificially generated (eg Planet Hunters; Schwamb et al . ) vouchers into voucher sets given to volunteers for classification in order to evaluate ongoing volunteer performance.…”

Section: Techniques For Producing High‐quality Ecological Citizen‐scimentioning

confidence: 99%

Assessing data quality in citizen science

Kosmala

Wiggins

Swanson

et al. 2016

Frontiers in Ecol & Environ

645

561

View full text Add to dashboard Cite

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“…In this section we investigate whether weighting individual volunteers based on the quality of their classifications can improve the cluster sample. User weighting has been applied in several other citizen science projects (Lintott et al 2008;Schwamb et al 2012) and seems naturally applicable to our AP data. In line with these previous implementations, we calculate weightings based on the level of agreement between a participant's classifications and the consensus opinion of all the volunteers.…”

Section: User Weightingmentioning

confidence: 99%

“…In this paper, we present a final, survey-wide cluster catalog created through a crowd-sourced, visual search of the data. The contribution of citizen scientists to astronomical research is not novel: projects such as Galaxy Zoo (Lintott et al 2008;Willett et al 2013), the Milky Way Project (Simpson et al 2012), and Planet Hunters (Schwamb et al 2012) have previously made use of crowd-sourcing. In this work we analyze image classifications collected from the Andromeda Project, a website established explicitly for the identification of star clusters in the PHAT dataset.…”

Section: Introductionmentioning

confidence: 99%

Phat Stellar Cluster Survey. Ii. Andromeda Project Cluster Catalog

Johnson

Seth

Dalcanton

et al. 2015

ApJ

Self Cite

132

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We construct a stellar cluster catalog for the Panchromatic Hubble Andromeda Treasury (PHAT) survey using image classifications collected from the Andromeda Project citizen science website. We identify 2,753 clusters and 2,270 background galaxies within ∼0.5 deg 2 of PHAT imaging searched, or ∼400 kpc 2 in deprojected area at the distance of the Andromeda galaxy (M31). These identifications result from 1.82 million classifications of ∼20,000 individual images (totaling ∼7 gigapixels) by tens of thousands of volunteers. We show that our crowd-sourced approach, which collects >80 classifications per image, provides a robust, repeatable method of cluster identification. The high spatial resolution Hubble Space Telescope images resolve individual stars in each cluster and are instrumental in the factor of ∼6 increase in the number of clusters known within the survey footprint. We measure integrated photometry in six filter passbands, ranging from the near-UV to the near-IR. PHAT clusters span a range of ∼8 magnitudes in F475W (g-band) luminosity, equivalent to ∼4 decades in cluster mass. We perform catalog completeness analysis using >3000 synthetic cluster simulations to determine robust detection limits and demonstrate that the catalog is 50% complete down to ∼500 M for ages <100 Myr. We include catalogs of clusters, background galaxies, remaining unselected candidates, and synthetic cluster simulations, making all information publicly available to the community. The catalog published here serves as the definitive base data product for PHAT cluster science, providing a census of star clusters in an L spiral galaxy with unmatched sensitivity and quality.

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“…Others, including the more than forty candidates identified by Wang et al (2013) and Schmitt et al (2014), may have been expected to be recovered by more conventional searches. Planet Hunters, therefore, is acting as an independent test of the Kepler pipeline's efficiency (Schwamb et al 2012) and has inspired improvements in subsequent analysis (Batalha et al 2013). A recent redesign of the project, launched in September 2014, aims to provide a "first-look" at data from the Kepler extended mission, emphasizing rapid analysis through a system that quickly identifies potential transits and then asks experienced volunteers to review them.…”

Section: Time-domain Astronomy: Supernova Zoo and Planetmentioning

confidence: 99%

Ideas for Citizen Science in Astronomy

Marshall

Lintott

Fletcher

2015

Annu. Rev. Astron. Astrophys.

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We review the relatively new, internet-enabled, and rapidly-evolving field of citizen science, focusing on research projects in stellar, extragalactic and solar system astronomy that have benefited from the participation of members of the public, often in large numbers. We find these volunteers making contributions to astronomy in a variety of ways: making and analyzing new observations, visually classifying features in images and light curves, exploring models constrained by astronomical datasets, and initiating new scientific enquiries. The most productive citizen astronomy projects involve close collaboration between the professionals and amateurs involved, and occupy scientific niches not easily filled by great observatories or machine learning methods: citizen astronomers are most strongly motivated by being of service to science. In the coming years we expect participation and productivity in citizen astronomy to increase, as survey datasets get larger and citizen science platforms become more efficient. Opportunities include engaging the public in ever more advanced analyses, and facilitating citizen-led enquiry by designing professional user interfaces and analysis tools with citizens in mind.Comment: In progress. The most up to date PDF file should be downloaded from http://tinyurl.com/CitizenAstronomyReview . We invite feedback via github issues at http://tinyurl.com/CitizenAstronomyReviewFeedback, and aim to submit to ARAA on September 2

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PLANET HUNTERS: ASSESSING THEKEPLERINVENTORY OF SHORT-PERIOD PLANETS

Cited by 76 publications

References 52 publications

Assessing data quality in citizen science

Assessing data quality in citizen science

Phat Stellar Cluster Survey. Ii. Andromeda Project Cluster Catalog

Ideas for Citizen Science in Astronomy

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