The Cluster Science Archive: from Time Period to Physics Based Search

Abstract:

Since 2000, the Cluster spacecraft relay the most detailed information on how the solar wind affects our geospace in three dimensions. Science output from Cluster is a leap forward in our knowledge of space plasma physics: the science behind space weather. It has been key in improving the modeling of the magnetosphere and understanding its various physical processes. Cluster data have enabled the publication of more than 2000 refereed papers and counting.

This substantial scientific return is often attributed to the online availability of the Cluster data archive, now called the Cluster Science Archive (CSA). It is being developed by the ESAC Science Data Center (ESDC) team and maintained alongside other science ESA archives at ESAC (ESA Space Astronomy Center, Madrid, Spain).

CSA is a public archive, which contains the entire set of Cluster high-resolution data, and other related products in a standard format and with a complete set of metadata. Since May 2015, it also contains data from the CNSA/ESA Double Star mission (2003-2008), a mission operated in conjunction with Cluster. The total amount of data format now exceeds 100 TB. Accessing CSA requires to be registered to enable user profiles and CSA accounts more than 1,500 users.

CSA provides unique tools for visualizing its data including

- on-demand particle distribution functions visualization

- fast data browsing with more than 15TB of pre-generated plots

- inventory plots

It also offers command line capabilities (e.g. data access via Matlab or IDL softwares, data streaming). Despite its reliability, users can only request data for a specific time period while scientists often focus on specific regions or data signatures.

For these reasons, a data-mining tool is being developed to do just that. It offers an interface to select data based not only on a time period but on various criteria including: key physical parameters, regions of space and spacecraft constellation geometry. The output of this tool is a list of time periods that fits the criteria imposed by the user. Such a list enables to download any bunch of datasets for all these time periods in one go. We propose to present the state of development of this tool and interact with the scientific community to better fit its needs.