Constraining the three-dimensional orbits of galaxies under ram pressure stripping with convolutional neural networks

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Abstract

Ram pressure stripping (RPS) of gas from disk galaxies has long been considered to play vital roles in galaxy evolution within groups and clusters. For a given density of intracluster medium (ICM) and a given velocity of a disk galaxy, RPS can be controlled by two angles (θ and ϕ) that define the angular relationship between the direction vector of the galaxy's three-dimensional (3D) motion within its host cluster and the galaxy's spin vector. We here propose a new method in which convolutional neutral networks (CNNs) are used to constrain θ and ϕ of disk galaxies under RPS. We first train a CNN by using ̃105 synthesized images of gaseous distributions of the galaxies from numerous RPS models with different θ and ϕ. We then apply the trained CNN to a new test RPS model to predict θ and ϕ. The similarity between the correct and predicted θ and ϕ is measured by cosine similarity (cos Θ) with cos Θ = 1 being perfectly accurate prediction. We show that the average cos Θ among test models is ≈0.95 (≈18° deviation), which means that θ and ϕ can be constrained by applying the CNN to the gaseous distributions. This result suggests that if the ICM is in hydrostatic equilibrium (thus not moving), the 3D orbit of a disk galaxy within its host cluster can be constrained by the spatial distribution of the gas being stripped by RPS. We discuss how this new method can be applied to H I studies of galaxies by ongoing and future large H I surveys such as the WALLABY and the SKA projects.
Original languageEnglish
Pages (from-to)1924-1937
JournalMonthly Notices of the Royal Astronomical Society
Volume485
Issue number2
DOIs
Publication statusE-pub ahead of print - 17 Aug 2018

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ram
stripping
disk galaxies
galaxies
orbits
three dimensional motion
hydrostatics
gases
gas
model test
train
spatial distribution
deviation
prediction
predictions

Cite this

@article{d509a0d57ad248fa964c32cc55330923,
title = "Constraining the three-dimensional orbits of galaxies under ram pressure stripping with convolutional neural networks",
abstract = "Ram pressure stripping (RPS) of gas from disk galaxies has long been considered to play vital roles in galaxy evolution within groups and clusters. For a given density of intracluster medium (ICM) and a given velocity of a disk galaxy, RPS can be controlled by two angles (θ and ϕ) that define the angular relationship between the direction vector of the galaxy's three-dimensional (3D) motion within its host cluster and the galaxy's spin vector. We here propose a new method in which convolutional neutral networks (CNNs) are used to constrain θ and ϕ of disk galaxies under RPS. We first train a CNN by using ̃105 synthesized images of gaseous distributions of the galaxies from numerous RPS models with different θ and ϕ. We then apply the trained CNN to a new test RPS model to predict θ and ϕ. The similarity between the correct and predicted θ and ϕ is measured by cosine similarity (cos Θ) with cos Θ = 1 being perfectly accurate prediction. We show that the average cos Θ among test models is ≈0.95 (≈18° deviation), which means that θ and ϕ can be constrained by applying the CNN to the gaseous distributions. This result suggests that if the ICM is in hydrostatic equilibrium (thus not moving), the 3D orbit of a disk galaxy within its host cluster can be constrained by the spatial distribution of the gas being stripped by RPS. We discuss how this new method can be applied to H I studies of galaxies by ongoing and future large H I surveys such as the WALLABY and the SKA projects.",
keywords = "galaxies:ISM galaxies:evolution galaxies: clusters : general Astrophysics - Astrophysics of Galaxies Astrophysics - Instrumentation and Methods for Astrophysics",
author = "Kenji Bekki",
year = "2018",
month = "8",
day = "17",
doi = "10.1093/mnras/sty2203",
language = "English",
volume = "485",
pages = "1924--1937",
journal = "Monthly Notices of the Royal Astronomical Society",
issn = "0035-8711",
publisher = "OXFORD UNIV PRESS UNITED KINGDOM",
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}

TY - JOUR

T1 - Constraining the three-dimensional orbits of galaxies under ram pressure stripping with convolutional neural networks

AU - Bekki, Kenji

PY - 2018/8/17

Y1 - 2018/8/17

N2 - Ram pressure stripping (RPS) of gas from disk galaxies has long been considered to play vital roles in galaxy evolution within groups and clusters. For a given density of intracluster medium (ICM) and a given velocity of a disk galaxy, RPS can be controlled by two angles (θ and ϕ) that define the angular relationship between the direction vector of the galaxy's three-dimensional (3D) motion within its host cluster and the galaxy's spin vector. We here propose a new method in which convolutional neutral networks (CNNs) are used to constrain θ and ϕ of disk galaxies under RPS. We first train a CNN by using ̃105 synthesized images of gaseous distributions of the galaxies from numerous RPS models with different θ and ϕ. We then apply the trained CNN to a new test RPS model to predict θ and ϕ. The similarity between the correct and predicted θ and ϕ is measured by cosine similarity (cos Θ) with cos Θ = 1 being perfectly accurate prediction. We show that the average cos Θ among test models is ≈0.95 (≈18° deviation), which means that θ and ϕ can be constrained by applying the CNN to the gaseous distributions. This result suggests that if the ICM is in hydrostatic equilibrium (thus not moving), the 3D orbit of a disk galaxy within its host cluster can be constrained by the spatial distribution of the gas being stripped by RPS. We discuss how this new method can be applied to H I studies of galaxies by ongoing and future large H I surveys such as the WALLABY and the SKA projects.

AB - Ram pressure stripping (RPS) of gas from disk galaxies has long been considered to play vital roles in galaxy evolution within groups and clusters. For a given density of intracluster medium (ICM) and a given velocity of a disk galaxy, RPS can be controlled by two angles (θ and ϕ) that define the angular relationship between the direction vector of the galaxy's three-dimensional (3D) motion within its host cluster and the galaxy's spin vector. We here propose a new method in which convolutional neutral networks (CNNs) are used to constrain θ and ϕ of disk galaxies under RPS. We first train a CNN by using ̃105 synthesized images of gaseous distributions of the galaxies from numerous RPS models with different θ and ϕ. We then apply the trained CNN to a new test RPS model to predict θ and ϕ. The similarity between the correct and predicted θ and ϕ is measured by cosine similarity (cos Θ) with cos Θ = 1 being perfectly accurate prediction. We show that the average cos Θ among test models is ≈0.95 (≈18° deviation), which means that θ and ϕ can be constrained by applying the CNN to the gaseous distributions. This result suggests that if the ICM is in hydrostatic equilibrium (thus not moving), the 3D orbit of a disk galaxy within its host cluster can be constrained by the spatial distribution of the gas being stripped by RPS. We discuss how this new method can be applied to H I studies of galaxies by ongoing and future large H I surveys such as the WALLABY and the SKA projects.

KW - galaxies:ISM galaxies:evolution galaxies: clusters : general Astrophysics - Astrophysics of Galaxies Astrophysics - Instrumentation and Methods for Astrophysics

U2 - 10.1093/mnras/sty2203

DO - 10.1093/mnras/sty2203

M3 - Article

VL - 485

SP - 1924

EP - 1937

JO - Monthly Notices of the Royal Astronomical Society

JF - Monthly Notices of the Royal Astronomical Society

SN - 0035-8711

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ER -