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Silkscreen Direct Measurements of Galaxy Distances from Survey Image Cutouts

TB Miller, I Pasha, A Polzin, P van Dokkum - arXiv preprint arXiv …, 2024 - arxiv.org
Computer Science paper astro-ph.GA Suggest

… In this work we turn to the rapidly growing field of simulation based inference to infer distances, and other physical properties, of dwarf galaxies directly from multi-band …

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BibTeX

@article{2407.04091v2,
Author        = {Tim B. Miller and Imad Pasha and Ava Polzin and Pieter van Dokkum},
Title         = {Silkscreen: Direct Measurements of Galaxy Distances from Survey Image
  Cutouts},
Eprint        = {2407.04091v2},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.GA},
Abstract      = {With upcoming wide field surveys from the ground and space the number of
known dwarf galaxies at $\lesssim 25$ Mpc is expected to dramatically increase.
Insight into their nature and analyses of these systems' intrinsic properties
will rely on reliable distance estimates. Currently employed techniques are
limited in their widespread applicability, especially in the semi-resolved
regime. In this work we turn to the rapidly growing field of simulation based
inference to infer distances, and other physical properties, of dwarf galaxies
directly from multi-band images. We introduce silkscreen: a code leveraging
neural posterior estimation to infer the posterior distribution of parameters
while simultaneously training a convolutional neural network such that
inference is performed directly on the images. Utilizing this combination of
machine learning and Bayesian inference, we demonstrate the method's ability to
recover accurate distances from ground-based survey images for a set of nearby
galaxies ($2 < D ({\rm Mpc)} < 12$) with measured SBF or TRGB distances. We
discuss caveats of the current implementation along with future prospects,
focusing on the goal of applying silkscreen to large upcoming surveys, like
LSST. While the current implementation performs simulations and training on a
per-galaxy basis, future implementations will aim to provide a broadly-trained
model that can facilitate inference for new dwarf galaxies in a matter of
seconds using only broadband cutouts. We focus here on dwarf galaxies, we note
that this method can be generalized to more luminous systems as well.},
Year          = {2024},
Month         = {Jul},
Url           = {http://arxiv.org/abs/2407.04091v2},
File          = {2407.04091v2.pdf}
}

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