Asteroseismic determination of fundamental parameters of Sun-like stars using multilayered neural networks

Kuldeep Verma, Shravan Hanasoge, Jishnu Bhattacharya, H. M. Antia, Ganapathy Krishnamurthi

Research output: Contribution to journalArticle

Abstract

The advent of space-based observatories such as Convection, Rotation and planetary Transits (CoRoT) and Kepler has enabled the testing of our understanding of stellar evolution on thousands of stars. Evolutionary models typically require five input parameters, the mass, initial helium abundance, initial metallicity, mixing length (assumed to be constant over time), and the age to which the star must be evolved. Some of these parameters are also very useful in characterizing the associated planets and in studying Galactic archaeology. How to obtain these parameters from observations rapidly and accurately, specifically in the context of surveys of thousands of stars, is an outstanding question, one that has eluded straightforward resolution. For a given star, we typically measure the effective temperature and surface metallicity spectroscopically and low-degree oscillation frequencies through space observatories. Here we demonstrate that statistical learning, using artificial neural networks, is successful in determining the evolutionary parameters based on spectroscopic and seismic measurements. Our trained networks show robustness over a broad range of parameter space, and critically, are entirely computationally inexpensive and fully automated. We analyse the observations of a few stars using this method and the results compare well to inferences obtained using other techniques. This method is both computationally cheap and inferentially accurate, paving the way for analysing the vast quantities of stellar observations from past, current, and future missions.

Original languageEnglish (US)
Pages (from-to)4206-4214
Number of pages9
JournalMonthly Notices of the Royal Astronomical Society
Volume461
Issue number4
DOIs
StatePublished - Oct 1 2016

Fingerprint

sun
stars
metallicity
observatories
observatory
archaeology
stellar evolution
transit
inference
artificial neural network
helium
learning
planets
convection
planet
oscillation
parameter
oscillations
temperature
method

Keywords

  • Fundamental parameters-stars
  • Interiors-stars
  • Low-mass-stars
  • Oscillations-stars
  • Solar-type
  • Stars

ASJC Scopus subject areas

  • Astronomy and Astrophysics
  • Space and Planetary Science

Cite this

Asteroseismic determination of fundamental parameters of Sun-like stars using multilayered neural networks. / Verma, Kuldeep; Hanasoge, Shravan; Bhattacharya, Jishnu; Antia, H. M.; Krishnamurthi, Ganapathy.

In: Monthly Notices of the Royal Astronomical Society, Vol. 461, No. 4, 01.10.2016, p. 4206-4214.

Research output: Contribution to journalArticle

Verma, Kuldeep ; Hanasoge, Shravan ; Bhattacharya, Jishnu ; Antia, H. M. ; Krishnamurthi, Ganapathy. / Asteroseismic determination of fundamental parameters of Sun-like stars using multilayered neural networks. In: Monthly Notices of the Royal Astronomical Society. 2016 ; Vol. 461, No. 4. pp. 4206-4214.
@article{732c800a43fa4de1b3ac17ec87c2ba55,
title = "Asteroseismic determination of fundamental parameters of Sun-like stars using multilayered neural networks",
abstract = "The advent of space-based observatories such as Convection, Rotation and planetary Transits (CoRoT) and Kepler has enabled the testing of our understanding of stellar evolution on thousands of stars. Evolutionary models typically require five input parameters, the mass, initial helium abundance, initial metallicity, mixing length (assumed to be constant over time), and the age to which the star must be evolved. Some of these parameters are also very useful in characterizing the associated planets and in studying Galactic archaeology. How to obtain these parameters from observations rapidly and accurately, specifically in the context of surveys of thousands of stars, is an outstanding question, one that has eluded straightforward resolution. For a given star, we typically measure the effective temperature and surface metallicity spectroscopically and low-degree oscillation frequencies through space observatories. Here we demonstrate that statistical learning, using artificial neural networks, is successful in determining the evolutionary parameters based on spectroscopic and seismic measurements. Our trained networks show robustness over a broad range of parameter space, and critically, are entirely computationally inexpensive and fully automated. We analyse the observations of a few stars using this method and the results compare well to inferences obtained using other techniques. This method is both computationally cheap and inferentially accurate, paving the way for analysing the vast quantities of stellar observations from past, current, and future missions.",
keywords = "Fundamental parameters-stars, Interiors-stars, Low-mass-stars, Oscillations-stars, Solar-type, Stars",
author = "Kuldeep Verma and Shravan Hanasoge and Jishnu Bhattacharya and Antia, {H. M.} and Ganapathy Krishnamurthi",
year = "2016",
month = "10",
day = "1",
doi = "10.1093/mnras/stw1621",
language = "English (US)",
volume = "461",
pages = "4206--4214",
journal = "Monthly Notices of the Royal Astronomical Society",
issn = "0035-8711",
publisher = "Oxford University Press",
number = "4",

}

TY - JOUR

T1 - Asteroseismic determination of fundamental parameters of Sun-like stars using multilayered neural networks

AU - Verma, Kuldeep

AU - Hanasoge, Shravan

AU - Bhattacharya, Jishnu

AU - Antia, H. M.

AU - Krishnamurthi, Ganapathy

PY - 2016/10/1

Y1 - 2016/10/1

N2 - The advent of space-based observatories such as Convection, Rotation and planetary Transits (CoRoT) and Kepler has enabled the testing of our understanding of stellar evolution on thousands of stars. Evolutionary models typically require five input parameters, the mass, initial helium abundance, initial metallicity, mixing length (assumed to be constant over time), and the age to which the star must be evolved. Some of these parameters are also very useful in characterizing the associated planets and in studying Galactic archaeology. How to obtain these parameters from observations rapidly and accurately, specifically in the context of surveys of thousands of stars, is an outstanding question, one that has eluded straightforward resolution. For a given star, we typically measure the effective temperature and surface metallicity spectroscopically and low-degree oscillation frequencies through space observatories. Here we demonstrate that statistical learning, using artificial neural networks, is successful in determining the evolutionary parameters based on spectroscopic and seismic measurements. Our trained networks show robustness over a broad range of parameter space, and critically, are entirely computationally inexpensive and fully automated. We analyse the observations of a few stars using this method and the results compare well to inferences obtained using other techniques. This method is both computationally cheap and inferentially accurate, paving the way for analysing the vast quantities of stellar observations from past, current, and future missions.

AB - The advent of space-based observatories such as Convection, Rotation and planetary Transits (CoRoT) and Kepler has enabled the testing of our understanding of stellar evolution on thousands of stars. Evolutionary models typically require five input parameters, the mass, initial helium abundance, initial metallicity, mixing length (assumed to be constant over time), and the age to which the star must be evolved. Some of these parameters are also very useful in characterizing the associated planets and in studying Galactic archaeology. How to obtain these parameters from observations rapidly and accurately, specifically in the context of surveys of thousands of stars, is an outstanding question, one that has eluded straightforward resolution. For a given star, we typically measure the effective temperature and surface metallicity spectroscopically and low-degree oscillation frequencies through space observatories. Here we demonstrate that statistical learning, using artificial neural networks, is successful in determining the evolutionary parameters based on spectroscopic and seismic measurements. Our trained networks show robustness over a broad range of parameter space, and critically, are entirely computationally inexpensive and fully automated. We analyse the observations of a few stars using this method and the results compare well to inferences obtained using other techniques. This method is both computationally cheap and inferentially accurate, paving the way for analysing the vast quantities of stellar observations from past, current, and future missions.

KW - Fundamental parameters-stars

KW - Interiors-stars

KW - Low-mass-stars

KW - Oscillations-stars

KW - Solar-type

KW - Stars

UR - http://www.scopus.com/inward/record.url?scp=84988564389&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84988564389&partnerID=8YFLogxK

U2 - 10.1093/mnras/stw1621

DO - 10.1093/mnras/stw1621

M3 - Article

VL - 461

SP - 4206

EP - 4214

JO - Monthly Notices of the Royal Astronomical Society

JF - Monthly Notices of the Royal Astronomical Society

SN - 0035-8711

IS - 4

ER -