We are excited to announce that the Gravity Spy team was awarded a three-year research grant by the U.S. National Science Foundation (NSF) to build the next volunteer-enabled gravitational-wave search phase. The project will investigate approaches to use auxiliary channel data, the same data used by LIGO scientists when isolating other noise signals from the glitches in the main channel. Existing volunteer efforts engaging in advanced work (e.g., reading and interpreting information in alogs and providing insights into the root causes of glitches) typically performed by scientists at the detector sites inspired the research team to consider additional technical support to assist volunteers. A recent publication by the research team (Soni et al., 2021) demonstrated that volunteers are pretty good at making discoveries in large data sets even without such technical support.
The new project is complex, which is why we have built a team of researchers from across the U.S., including LIGO researchers within CIERA, LIGO researchers at Christopher Newport University, machine learning researchers at Northwestern University, and crowd-sourced science researchers at Syracuse University and the University of Wisconsin-Madison.
One goal of the project is to create novel interfaces for Gravity Spy that visualize the auxiliary channel data and provide statistical summaries about correlations between glitches, activities performed by LIGO scientists when they investigate the source of glitches. The team plans to create Beginner, Intermediate, and Advanced workflows that support different modes of interaction at each stage. In the Beginner mode (see Figure below for an interface mockup), volunteers will be able to compare a glitch that occurred in the gravitational-wave channel with the spectrogram of correlated auxiliary channels to assess whether the correlation seems natural or coincidental. In Intermediate mode, volunteers will be able to examine and refine collections created by beginners and identify correlations between glitches and the auxiliary channel over some timeframe. Finally, work in the Advanced mode will enable volunteers to discover the root cause of glitches by examining patterns and making inferences among large sets of auxiliary channel data.
By building tools to support similar activities in Gravity Spy 2.0, volunteers will view new data and learn to make statistical inferences about noise signals among the thousands of auxiliary channels. Over the next year, the research team hopes to understand what background knowledge of the auxiliary data is helpful for volunteers, and what system tools and expertise will help volunteers identify connections among data sources. Through this, the research team hopes volunteers will isolate which relationships are causal vs. spurious. The research team will engage existing Gravity Spy volunteers throughout the project to obtain feedback and insights about the new interfaces (a mockup interface is shown below). We hope everyone is as excited about this project as we are.
We look forward to continued collaboration with volunteers as Gravity Spy sets the standard for the next generation of people-powered science projects.
The Gravity Spy Team