Using Visual Analytics to Develop Situation Awareness in Astrophysics

Abstract

We present a novel collaborative visual analytics application for cognitively overloaded users in the astrophysics domain. The system was developed for scientists who need to analyze heterogeneous, complex data under time pressure, and make predictions and time-critical decisions rapidly and correctly under a constant influx of changing data. The Sunfall Data Taking system utilizes several novel visualization and analysis techniques to enable a team of geographically distributed domain specialists to effectively and remotely maneuver a custom-built instrument under challenging operational conditions. Sunfall Data Taking has been in production use for 2 years by a major international astrophysics collaboration (the largest data volume supernova search currently in operation), and has substantially improved the operational efficiency of its users. We describe the system design process by an interdisciplinary team, the system architecture and the results of an informal usability evaluation of the production system by domain experts in the context of Endsley's three levels of situation awareness.

Experiences with a Flexible User Research Process to Build Data Change Tools

“…User research practices in commercial software development span a wide range of approaches and commonly include usability evaluations of systems to identify problems [13,14], as well as ethnographic inquiries employing interviews and observations to better understand the people and problems of interest [5,15]. Some scientific software projects have incorporated these processes to evaluate the products they are producing [1,6,9,11]. There has been limited discussion and reflection about the incorporation of user research and design as an integral element of scientific software projects [10].…”

“…The Deduce (Distributed Dynamic Data Analytics Infrastructure for Collaborative Environments) 1 project is exploring the challenges and methodologies for enabling usable and efficient scientific data change management. The project uses a mix of techniques to address the data change problem space.…”

Data management and simulation support accelerating carbon capture through computing

“…User Experience design techniques were utilized during the project to solicit input on priorities and processes from the industry participants and to improve usability of CCSI Toolset modules [3], [4]. One of the results of our early interaction with the industry participants was the recognition that they already have extensive experience and libraries of models in existing process simulation packages including AspenPlus, Aspen Custom Modeller (ACM), Simulink, gProms, and custom simulation packages including some that are Excel-based.…”

Performance and Cost Analysis of the Supernova Factory on the Amazon AWS Cloud

“…Software identified candidates in subtractions using a variety of quality cuts, and later, machine learning algorithms to identify real astrophysical transients and reject image/subtraction artifacts. Humans performed the final quality assessment step, saving and vetting candidates using historical and context data using a custom scientific workflow tool, SNwarehouse [7]. The entire process, from the start of data collection to human identification of candidates, took approximately 18 h.…”