Abstract
This paper describes the design and implementation of Stingray, a library in Python built to perform time series analysis and related tasks on astronomical light curves. Its core functionality comprises a range of Fourier analysis techniques commonly used in spectral-timing analysis, as well as extensions for analyzing pulsar data, simulating data sets, and statistical modeling. Its modular build allows for easy extensions and incorporation of its methods into data analysis workflows and pipelines. We aim for the library to be a platform for the implementation of future spectral-timing techniques. Here, we describe the overall vision and framework, core functionality, extensions, and connections to high-level command-line and graphical interfaces. The code is well-tested, with a test coverage of currently 95%, and is accompanied by extensive API documentation and a set of step-by-step tutorials.
Highlights
Many celestial objects vary in brightness on timescales of milliseconds to centuries. These “light curves”–variations of brightness of an object as a function of time–often encode interesting physical processes that can help us learn about the nature of the celestial bodies that produced them
In stars like our sun, typical time scales tell us about stellar rotation, starspots and internal physics like convection
In remnants of stellar explosions like neutron stars, we can use time series to learn about the densest matter known in the universe
Summary
These “light curves”–variations of brightness of an object as a function of time–often encode interesting physical processes that can help us learn about the nature of the celestial bodies that produced them. In remnants of stellar explosions like neutron stars, we can use time series to learn about the densest matter known in the universe.
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