Disaster, Infrastructure and Participatory Knowledge:The Planetary Response Network

There are many challenges involved in online participatory humanitarian response. We evaluate the Planetary Response Network (PRN), a collaboration between researchers, humanitarian organizations, and the online citizen science platform Zooniverse. The PRN uses satellite and aerial image analysis to provide stakeholders with high-level situational awareness during and after humanitarian crises. During past deployments, thousands of online volunteers have compared pre- and post-event satellite images to identify damage to infrastructure and buildings, access blockages, and signs of people in distress. In addition to collectively producing aggregated “heat maps” of features that are shared with responders and decision makers, individual volunteers may also flag novel features directly using integrated community discussion software. The online infrastructure facilitates worldwide participation even for geographically focused disasters; this widespread public participation means that high-value information can be delivered rapidly and uniformly even for large-scale crises. We discuss lessons learned from deployments, place the PRN’s distributed online approach in the context of more localized efforts, and identify future needs for the PRN and similar online crisis-mapping projects. The successes of the PRN demonstrate that effective online crisis mapping is possible on a generalized citizen science platform such as the Zooniverse

A transient search using combined human and machine classifications

Large modern surveys require efficient review of data in order to find transient sources such as supernovae, and to distinguish such sources from artefacts and noise. Much effort has been put into the development of automatic algorithms, but surveys still rely on human review of targets. This paper presents an integrated system for the identification of supernovae in data from Pan-STARRS1, combining classifications from volunteers participating in a citizen science project with those from a convolutional neural network. The unique aspect of this work is the deployment, in combination, of both human and machine classifications for near real-time discovery in an astronomical project. We show that the combination of the two methods outperforms either one used individually. This result has important implications for the future development of transient searches, especially in the era of Large Synoptic Survey Telescope and other large-throughput surveys

The K2-138 system:a near-resonant chain of five sub-neptune planets discovered by citizen scientists

K2-138 is a moderately bright (V = 12.2, K = 10.3) main-sequence K star observed in Campaign 12 of the NASA K2 mission. It hosts five small (1.6–3.3 ${R}_{\oplus }$) transiting planets in a compact architecture. The periods of the five planets are 2.35, 3.56, 5.40, 8.26, and 12.76 days, forming an unbroken chain of near 3:2 resonances. Although we do not detect the predicted 2–5 minute transit timing variations (TTVs) with the K2 timing precision, they may be observable by higher-cadence observations with, for example, Spitzer or CHEOPS. The planets are amenable to mass measurement by precision radial velocity measurements, and therefore K2-138 could represent a new benchmark system for comparing radial velocity and TTV masses. K2-138 is the first exoplanet discovery by citizen scientists participating in the Exoplanet Explorers project on the Zooniverse platform