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Results for “Zooniverse platform”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS2019

K2-288Bb: A Small Temperate Planet in a Low-Mass Binary System Discovered by Citizen Scientists

Observations from the Kepler and K2 missions have provided the astronomical community with unprecedented amounts of data to search for transiting exoplanets and other astrophysical phenomena. Here, we present K2-288, a low-mass binary system (M2.0 ± 1.0; M3.0 ± 1.0) hosting a small (Rp=1.9 R), temperate (Teq=226K) planet observed in K2 Campaign 4. The candidate was first identified by citizen scientists using Exoplanet Explorers hosted on the Zooniverse platform. Follow-up observations and detailed analysesvalidate the planet and indicate that it likely orbits the secondary star on a 31.39-day period. This orbit places K2-288Bb in or near the habitable zone of its low-mass host star. K2-288Bb resides in a system with a uniquearchitecture, as it orbits at >0.1 au from one component in a moderate separation binary (a(proj)~55 au), andfurther follow-up may provide insight into its formation and evolution. Additionally, its estimated size straddlesthe observed gap in the planet radius distribution. Planets of this size occur less frequently and may be in atransient phase of radius evolution. K2-288 is the third transiting planet system identified by the ExoplanetExplorers program and its discovery exemplifies the value of citizen science in the era of Kepler, K2, and theTransiting Exoplanet Survey Satellite.

Kepler and K2 missions↗
NASA NTRS2019

Machine Learning for the Zwicky Transient Facility

The Zwicky Transient Facility is a large optical survey in multiple filters producing hundreds of thousands of transient alerts per night. We describe here various machine learning (ML) implementations and plans to make the maximal use of the large data set by taking advantage of the temporal nature of the data, and further combining it with other data sets. We start with the initial steps of separating bogus candidates from real ones, separating stars and galaxies, and go on to the classification of real objects into various classes. Besides the usual methods (e.g., based on features extracted from light curves) we also describe early plans for alternate methods including the use of domain adaptation, and deep learning. In a similar fashion we describe efforts to detect fast moving asteroids. We also describe the use of the Zooniverse platform for helping with classifications through the creation of training samples, and active learning. Finally we mention the synergistic aspects of ZTF and LSST from the ML perspective.

Ashish Mahabal↗
NASA NTRSDate not supplied

NASA GLOBE CLOUD GAZE: Creating Data Quality Flags for Citizen Science Cloud Observations Matched to NASA Satellite Data

The GLOBE Program, NASA’s largest and longest lasting citizen science program about the Earth, has been collecting cloud observations matched to multiple satellite data daily. The program’s cloud protocol is historically the most popular protocol as your eyes are the only instruments you need to collect observations of the sky. This dataset includes over 3,300,000 cloud observations with variables like total cloud cover, cloud type and opacity that are collocated to the nearest overpass times of geostationary satellites (GOES-15, GOES-16, GOES-17, Meteosat-8, Meteosat-11, or Himawari-8), or to Clouds and the Earth’s Radiant Energy System (CERES) instruments onboard Aqua and Terra, or the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite. In order to increase the usability of this dataset, the Community science project Leveraging Online and User Data through GLOBE And Zooniverse Engagement (CLOUD GAZE) has been developed to generate data quality flags of these ground-up and top-down perspectives of sky and clouds. Recently funded through NASA’s Citizen Science for Earth Systems Program, CLOUD GAZE has partnered with the Zooniverse online platform to obtain reference data and image tagging of sky photographs collected through The GLOBE Program’s clouds protocol. This paper will present the GLOBE Clouds dataset matched to NASA satellite data, integration of CLOUD GAZE to develop data quality flags, and research applications of the dataset (includes ground-up and top-down perspective comparisons, ground observations of dust storms and smoke plumes, and cloud observations in polar regions). The paper will also present on techniques and recommendations for classroom use and for community engagement, particularly for those looking to online resources.

Marilé Colón Robles↗