J/MNRAS/511/5578 Vetting asteroseismic Δν measurements (Reyes+, 2022)
Vetting asteroseismic Δν measurements using neural networks.
Reyes C., Stello D., Hon M., Zinn J.C.
<Mon. Not. R. Astron. Soc. 511, 5578-5596 (2022)>
=2022MNRAS.511.5578R 2022MNRAS.511.5578R (SIMBAD/NED BibCode)
ADC_Keywords: Asteroseismology
Keywords: asteroseismology - stars: oscillations -
stars: fundamental parameters - methods: data analysis
Abstract:
Precise asteroseismic parameters allow one to quickly estimate radius
and mass distributions for large samples of stars. A number of
automated methods are available to calculate the frequency of maximum
acoustic power (νmax) and the frequency separation between overtone
modes (Δν) from the power spectra of red giants. However,
filtering through the results requires either manual vetting,
elaborate averaging across multiple methods, or sharp cuts in certain
parameters to ensure robust samples of stars free of outliers. Given
the importance of ensemble studies for Galactic archaeology and the
surge in data availability, faster methods for obtaining reliable
asteroseismic parameters are desirable. We present a neural network
classifier that vets Δν by combining multiple features from
the visual Δν vetting process. Our classifier is able to
analyse large numbers of stars determining whether their measured
Δν are reliable thus delivering clean samples of oscillating
stars with minimal effort. Our classifier is independent of the method
used to obtain νmax and Δν, and therefore can be applied
as a final step to any such method. Tests of our classifier's
performance on manually vetted Δν measurements reach an
accuracy of 95%. We apply the method to giants observed by K2 Galactic
Archaeology Program and find that our results retain stars with
astrophysical oscillation parameters consistent with the parameter
distributions already defined by well-characterised Kepler red giants.
Description:
The SYD pipeline is run on all the K2 power spectra from Campaigns
1-8 and 10-18 corresponding to 47683 time series (from 45132
unique targets) that were deemed to potentially show oscillations by
the neural network detection algorithm from Hon et al.
(2018ApJ...859...64H 2018ApJ...859...64H). No significance testing or other form of
vetting was performed on the resulting νmax and Δν
results from this SYD run. Hence, by construction, we expect a large
fraction of Δν values to be incorrect. Fewer than 20000
stars are known to actually show oscillations with reliable seismic
results for both νmax and Δν in the K2 GAP sample (Zinn et
al. 2021, in prep., arXiv:2108.05455). The vetting method that we now
implement as part of the SYD pipeline is therefore our neural network
classifier, and the resulting vetted SYD values are listed in
Table G1.
Our vetted results and the probabilities given by the network after
running it on original samples from the five pipelines are listed in
Table G2, broken down by campaign.
File Summary:
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FileName Lrecl Records Explanations
--------------------------------------------------------------------------------
ReadMe 80 . This file
tableg1.dat 50 20708 Neural network outputs from SYD results
tableg2.dat 67 66854 Neural network vetted results from five pipelines
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See also:
IV/34 : K2 Ecliptic Plane Input Catalog (EPIC) (Huber+, 2017)
Byte-by-byte Description of file: tableg1.dat
--------------------------------------------------------------------------------
Bytes Format Units Label Explanations
--------------------------------------------------------------------------------
1- 9 I9 --- EPIC K2 object identifier
(K2 ECLIPTIC PLANE INPUT CATALOG)
11- 12 I2 --- camp [1/18] K2 campaign number
14- 20 F7.3 uHz numax [3.36/271.26] Numax delivered by the SYD
Pipeline
22- 28 F7.3 uHz e_numax Numax uncertainty as given by the SYD pipeline
30- 35 F6.3 uHz dnu [0.59/21.8] Deltanu delivered by the SYD
Pipeline
37- 42 F6.3 uHz e_dnu Deltanu uncertainty as given by the SYD
pipeline
44- 48 F5.3 --- dnuProb [0.5/1.0] Probability assigned by the neural
network classifier from this work to the
raw deltanu value
50 I1 --- RCRGB [0/1] Evolutionary phase (1)
--------------------------------------------------------------------------------
Note (1): Evolutionary phase assigned to the star by the machine learning method
from Hon et al. (2018MNRAS.476.3233H 2018MNRAS.476.3233H) for raw values of numax and deltanu
given by the SYD pipeline as follows:
0 = Red Giant Branch
1 = Red Clump
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Byte-by-byte Description of file: tableg2.dat
--------------------------------------------------------------------------------
Bytes Format Units Label Explanations
--------------------------------------------------------------------------------
1- 3 A3 --- Pipeline Name of the pipeline which delivered the
values of numax, e_numax, dnu and e_dnu
5- 13 I9 --- EPIC K2 object identifier
(K2 ECLIPTIC PLANE INPUT CATALOG)
15- 16 I2 --- camp [1/18] K2 campaign number
18- 24 F7.3 muHz numax [2.81/274.5] Numax delivered by the
corresponding pipeline
26- 31 F6.3 muHz e_numax Numax uncertainty as given by the
corresponding pipeline
33- 38 F6.3 muHz dnu [0.59/23.71] Deltanu delivered by the
corresponding pipeline
40- 45 F6.3 muHz e_dnu ? Deltanu uncertainty as given by the
corresponding pipeline
47- 53 A7 --- EVensemble ? Evolutionary phase for ensemble-corrected
values of numax and deltanu (1)
55- 61 A7 --- EV ? Evolutionary phase for raw values of numax
and deltanu given by the corresponding
pipeline (1)
63- 67 F5.3 --- dnuProb [0.5/1.0] Probability assigned by the neural
network classifier from this work to the
deltanu value
--------------------------------------------------------------------------------
Note (1): assigned to the star by the machine learning method from
Hon et al. (2018MNRAS.476.3233H 2018MNRAS.476.3233H.)
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Acknowledgements:
Claudia Reyes, c.reyes_saez(at)unsw.edu.au
(End) Patricia Vannier [CDS] 24-Mar-2022