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Towards characterizing dark matter subhalo perturbations in stellar streams with graph neural networks

PX Ma, KK Rogers, TS Li, R Hložek, J Webb… - arXiv preprint arXiv …, 2025 - arxiv.org
Astrophysics paper astro-ph.GA Suggest

… 3 a schematic summary of the full pipeline from input simulations (§ 2.1) through the two different encoders that we consider to the final simulation-based inference step (§ …

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BibTeX

@article{2502.03522v2,
Author        = {Peter Xiangyuan Ma and Keir K. Rogers and Ting S. Li and Renée Hložek and Jeremy Webb and Ruth Huang and Julian Meunier},
Title         = {Towards characterizing dark matter subhalo perturbations in stellar
  streams with graph neural networks},
Eprint        = {2502.03522v2},
DOI           = {10.3847/1538-4357/add698},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.GA},
Abstract      = {The phase space of stellar streams is proposed to detect dark substructure in
the Milky Way through the perturbations created by passing subhalos - and thus
is a powerful test of the cold dark matter paradigm and its alternatives. Using
graph convolutional neural network (GCNN) data compression and simulation-based
inference (SBI) on a simulated GD-1-like stream, we improve the constraint on
the mass of a [$10^8$, $10^7$, $10^6$] $M_\odot$ perturbing subhalo by factors
of [11, 7, 3] with respect to the current state-of-the-art density power
spectrum analysis. We find that the GCNN produces posteriors that are more
accurate (better calibrated) than the power spectrum. We simulate the positions
and velocities of stars in a GD-1-like stream and perturb the stream with
subhalos of varying mass and velocity. Leveraging the feature encoding of the
GCNN to compress the input phase space data, we then use SBI to estimate the
joint posterior of the subhalo mass and velocity. We investigate how our
results scale with the size of the GCNN, the coordinate system of the input and
the effect of incomplete observations. Our results suggest that a survey with
$10 \times$ fewer stars (300 stars) with complete 6-D phase space data performs
about as well as a deeper survey (3000 stars) with only 3-D data (photometry,
spectroscopy). The stronger constraining power and more accurate posterior
estimation motivate further development of GCNNs in combining future
photometric, spectroscopic and astrometric stream observations.},
Year          = {2025},
Month         = {Feb},
Note          = {ApJ, 987, 96 (2025)},
Url           = {http://arxiv.org/abs/2502.03522v2},
File          = {2502.03522v2.pdf}
}

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