Eyes on the future: optimizing science output for next generation surveys with joint crowdsourced and automated classification techniques

着眼未来：利用联合众包和自动分类技术优化下一代调查的科学产出

• 批准号: 1716602
• 负责人: Claudia Scarlata
• 金额: $ 65.47万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1716602&HistoricalAwards=false
• 关键词: Eyes; future; optimizing; science; output

The successful use of citizen science to carry out "crowd-sourced" galaxy classifications from imaging data has shown clearly the availability of a vast resource of volunteer labor. Future surveys will also produce spectra, breaking down light into its wavelengths to reveal the physical state of the galaxies. The present, ambitious, project will extend the involvement of volunteers into these spectroscopic data, providing a richer experience for participants and addressing research in galaxy formation impossible to accomplish any other way. If the scalability problem of handling massive amounts of spectral data can also be solved this way, the impact for the community and for the scientifically literate public will be enormous.While crowdsourced galaxy classifications have proven their worth with a decade of images from the Sloan Digital Sky Survey (SDSS), several next generation surveys will be spectroscopic, which substantially increases the complexity of the data sets, while providing a much larger space for discovery. Although having spectra for tens of millions of objects will allow a wide range of science, new tools are needed to maximize the scientific return. Automatic algorithms alone will either struggle in identifying difficult features or produce highly contaminated samples to try to maximize completeness. The volume of data from forthcoming surveys renders human scrutiny impractical. This project will meet these challenges, extending to spectroscopic data the successful crowdsourcing approach used for imaging by the suite of Galaxy Zoo projects, and expanding previous NSF-supported work by this team. There will be two components: (1) Galaxy Nurseries will build an emission line catalog through crowdsourced classifications of spectroscopic data; and (2) Clump Scout will identify clumpy galaxies in the SDSS. Investigations with these catalogs will constrain galaxy formation models by (1) quantifying the fraction of galaxies with giant star-forming regions; (2) characterizing the internal variation of physical properties of such regions; and (3) comparing the gas metallicity of galaxies with and without these regions.As with the previous image work, the high-level catalogs to be produced, and the new classification algorithms to be used, are to be released publicly, and will be a valuable resource for the community. The study also continues the work to implement Galaxy Zoo in undergraduate astronomy courses, involving both graduate and undergraduate students.

公民科学的成功运用，从成像数据中进行“众包”星系分类，清楚地表明了志愿劳动的巨大资源的可用性。 未来的调查还将产生光谱，将光分解成其波长，以揭示星系的物理状态。 目前，雄心勃勃的项目将扩大志愿者对这些光谱数据的参与，为参与者提供更丰富的经验，并解决不可能以其他方式完成的星系形成研究。 如果处理大量光谱数据的可扩展性问题也能以这种方式解决，那么对社区和科学素养的公众的影响将是巨大的。虽然众包星系分类已经通过斯隆数字巡天（SDSS）十年的图像证明了它们的价值，但几个下一代巡天将是光谱的，这大大增加了数据集的复杂性，同时提供更大的发现空间。 虽然拥有数千万个物体的光谱将允许广泛的科学，但需要新的工具来最大限度地提高科学回报。 自动算法本身要么在识别困难的特征方面遇到困难，要么产生高度污染的样本，以试图最大限度地提高完整性。 即将进行的调查所获得的大量数据使人的监督变得不切实际。 该项目将应对这些挑战，将Galaxy Zoo项目套件用于成像的成功众包方法扩展到光谱数据，并扩展该团队以前的NSF支持工作。 将有两个组成部分：（1）星系苗圃将通过众包光谱数据分类建立一个发射线目录;（2）Clump Scout将在SDSS中识别团簇星系。 利用这些星表的研究将通过（1）量化具有巨大恒星形成区的星系比例，（2）表征这些区域物理性质的内部变化，（3）研究星系的形成模型。和（3）比较有和没有这些区域的星系的气体金属丰度。与以前的图像工作一样，将要产生的高级目录和将要使用的新分类算法，将公开发布，并将成为社区的宝贵资源。 该研究还继续在本科天文学课程中实施银河动物园的工作，涉及研究生和本科生。

项目成果

Investigating Clumpy Galaxies in the Sloan Digital Sky Survey Stripe 82 Using the Galaxy Zoo

• DOI: 10.3847/1538-4357/abed5b
• 发表时间: 2020-11
• 期刊: The Astrophysical Journal
• 作者: V. Mehta;C. Scarlata;L. Fortson;H. Dickinson;Dominic Adams;J. Chevallard;S. Charlot;Melanie Beck;S. Kruk;B. Simmons

Galaxy Zoo: Morphological Classification of Galaxy Images from the Illustris Simulation

Galaxy Zoo：Illustris 模拟中的星系图像的形态分类

• DOI: 10.3847/1538-4357/aaa250
• 发表时间: 2018
• 期刊: The Astrophysical Journal
• 作者: Dickinson, Hugh;Fortson, Lucy;Lintott, Chris;Scarlata, Claudia;Willett, Kyle;Bamford, Steven;Beck, Melanie;Cardamone, Carolin;Galloway, Melanie;Simmons, Brooke
• 通讯作者: Simmons, Brooke