On the Fast Track: Rapid construction of stellar stream paths

TL;DR

This paper introduces a novel, model-independent method using Self-Organizing Maps and Kalman Filters to rapidly reconstruct stellar stream paths, accounting for measurement errors and data sparsity, facilitating comparison with simulations.

Contribution

The paper presents a new non-parametric technique for reconstructing stellar streams that handles phase-wrapped streams and propagates uncertainties, implemented in an accessible Python package.

Findings

Enables uniform analysis of stellar streams and simulations.

Works on phase-wrapped streams and propagates measurement errors.

Implemented in the public Python package TrackStream.

Abstract

Stellar streams are sensitive probes of the Galactic potential. The likelihood of a stream model given stream data is often assessed using simulations. However, comparing to simulations is challenging when even the stream paths can be hard to quantify. Here we present a novel application of Self-Organizing Maps and first-order Kalman Filters to reconstruct a stream's path, propagating measurement errors and data sparsity into the stream path uncertainty. The technique is Galactic-model independent, non-parametric, and works on phase-wrapped streams. With this technique, we can uniformly analyze and compare data with simulations, enabling both comparison of simulation techniques and ensemble analysis with stream tracks of many stellar streams. Our method is implemented in the public Python package TrackStream, available at https://github.com/nstarman/trackstream.