J/A+A/692/A103 New planetary nebula candidates (Li+, 2024)
First discovery and confirmation of planetary nebula candidates from AI and
deep learning techniques applied to VPHAS+ survey data.
Li Y., Parker Q., Jia P.
<Astron. Astrophys. 692, A103 (2024)>
=2024A&A...692A.103L 2024A&A...692A.103L (SIMBAD/NED BibCode)
ADC_Keywords: Planetary nebulae ; Morphology ; Photometry, H-alpha
Keywords: methods: data analysis - techniques: spectroscopic -
planetary nebulae: general
Abstract:
We have developed tools based on deep learning and artificial
intelligence (AI) to search extant narrow-band wide-field Hα
surveys of the Galactic Plane for elusive planetary nebulae (PNe)
hidden in dense star fields towards the Galactic centre. They are
faint, low-surface-brightness, usually resolved sources, which had not
discovered by previous automatic searches that depend on photometric
data for point-like sources. These sources are very challenging to
locate by traditional visual inspection in such crowded fields and
many have been missed. We have successfully adopted a novel
"Swin-Transformer" AI algorithm, which we describe in detail in the
preceding Techniques paper (Paper I, Liu et al., arXiv:2103.14030).
Here, we present preliminary results from our first spectroscopic
follow-up run for 31 top-quality PN candidates found by the algorithm
from the high-resolution Hα survey VPHAS+. This survey has not
yet undergone extensive manual, systematic searching.
Our candidate PNe were observed with the SpUpNIC spectrograph on the
1.9m telescope at the South African Astronomical Observatory (SAAO) in
June 2023. We performed standard IRAF spectroscopic reduction,
followed by our normal HASH PN identification and classification
procedures.
Our reduced spectra confirmed that these candidates include 22 true,
likely, and possible PNe (70.97%), 3 emission-line galaxies, 2
emission-line stars, 2 late-type star contaminants, and 2 other
Hα sources including a newly identified detached fragment of
supernova remnants (SNRs) RCW 84. We present the imaging and spectral
data of these candidates and a preliminary analysis of their
properties. These data provide strong input for evaluating and
refining the behaviour of the AI algorithm when searching for PNe in
wide-field Hα surveys.
Description:
We present the first discovery and confirmation of PNe candidates
found from deep learning techniques applied to high-resolution VPHAS+
survey data. We have proven our techniques are able to independently
and automatically uncover faint, resolved PNe in very dense star
fields near the Galactic centre using the high-resolution Hα
survey VPHAS+ (Cat. II/386).
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
table1.dat 76 34 Parameters of the observed PN candidates and any
associated nebulosity or outflows
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See also:
II/386 : VPHAS+ DR3 survey (Drew+, 2025)
Byte-by-byte Description of file: table1.dat
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Bytes Format Units Label Explanations
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1- 12 A12 --- Name Common name
14- 18 I5 --- HASH HASH identification number
20- 21 I2 h RAh Right ascension (J2000)
23- 24 I2 min RAm Right ascension (J2000)
26- 29 F4.1 s RAs Right ascension (J2000)
31 A1 --- DE- Declination sign (J2000)
32- 33 I2 deg DEd Declination (J2000)
35- 36 I2 arcmin DEm Declination (J2000)
38- 39 I2 arcsec DEs Declination (J2000)
41- 46 F6.2 deg GLON Galactic longitude
48- 52 F5.2 deg GLAT Galactic latitude
54- 68 A15 --- Status Object status
70- 74 F5.1 arcsec Diam Angular size
76 A1 --- Morph PN morphology (1)
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Note (1): PN morphology as follows:
R = rounded
E = elliptical
B = bipolar
I = irregular
S = quasi-stellar
A = asymmetric
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History:
From electronic version of the journal
(End) Patricia Vannier [CDS] 15-Apr-2025