Speaker
Nathaniel Starkman
(University of Toronto)
Description
Stellar streams are sensitive probes of the Galactic potential. The likelihood of a model given stream data can only be assessed using simulations. However, comparison to simulation is challenging in a noisy 6D phase space in which even the stream paths are hard to quantify. Here we present a novel application of Self-Organizing Maps and first-order Kalman Filters to reconstruct the stream 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. We can uniformly analyze and compare data with simulation.
| Do you plan to attend the symposium in-person or virtually? | undecided |
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Primary authors
Nathaniel Starkman
(University of Toronto)
Jo Bovy
(University of Toronto)
Prof.
Jeremy Webb
(University of Toronto)
Prof.
Daniela Calvetti
(Case Western Reserve University)
Prof.
Erkki Somersalo
(Case Western Reserve University)
