On the fast track: Rapid construction of stellar stream paths

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.

Constraining the Gravitational Potential from the Projected Morphology of Extragalactic Tidal Streams

“…In Nibauer et al (2022) an unsupervised stream track-fitting algorithm is developed, though the algorithm utilizes velocity information that we are unlikely to measure in external galaxies. Starkman et al (2023) developed another approach for fitting stream tracks, utilizing self-organizing maps and Kalman Filters. Both methods operate in the context of the Milky Way, and we leave the task of extending them to photometry of external galaxies to future work.…”

“…This is the approach we take in Section 5, where we analyze a real tidal feature surrounding the galaxy NGC 5907. In a future work, we will consider more rigorous and automated techniques for fitting flexible and differentiable curves to the observed population of tidal debris (e.g., Starkman et al 2023).…”