I found a new PN Candidate (32.035994 49.233615), by using synthetic fotometry based on Lamost spectra of objects catalog as emission lines objects. See the notebook.
- Papers:
@ARTICLE{2022RAA....22f5015L,
author = {{Lu}, Yan and {Luo}, A. -Li and {Wang}, Li-Li and {Wang}, You-Fen and {Li}, Yin-Bi and {Han}, Jin-Shu and {Qin}, Li and {Tang}, Yan-Ke and {Qiu}, Bo and {Zhang}, Shuo and {Zhang}, Jian-Nan and {Zhao}, Yong-Heng},
title = "{106 New Emission-line Galaxies and 29 New Galactic H II Regions are Identified with Spectra in the Unknown Data Set of LAMOST DR7}",
journal = {Research in Astronomy and Astrophysics},
keywords = {techniques: spectroscopic, Galaxies, (ISM:) HII regions},
year = 2022,
month = jun,
volume = {22},
number = {6},
eid = {065015},
pages = {065015},
doi = {10.1088/1674-4527/ac693b},
adsurl = {https://ui.adsabs.harvard.edu/abs/2022RAA....22f5015L},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}}
@ARTICLE{2022ApJS..260...35W,
author = {{Wang}, Luqian and {Li}, Jiao and {Wu}, You and {Gies}, Douglas R. and {Liu}, Jin Zhong and {Liu}, Chao and {Guo}, Yanjun and {Chen}, Xuefei and {Han}, Zhanwen},
title = "{Identification of New Classical Be Stars from the LAMOST Medium Resolution Survey}",
journal = {\apjs},
keywords = {Early-type stars, Be stars, Surveys, 430, 142, 1671, Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Astrophysics of Galaxies},
year = 2022,
month = jun,
volume = {260},
number = {2},
eid = {35},
pages = {35},
doi = {10.3847/1538-4365/ac617a},
archivePrefix = {arXiv},
eprint = {2203.15289},
primaryClass = {astro-ph.SR},
adsurl = {https://ui.adsabs.harvard.edu/abs/2022ApJS..260...35W},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}}@ARTICLE{2022ApJS..259...38Z,
author = {{Zhang}, Yun-Jin and {Hou}, Wen and {Luo}, A. -Li and {Li}, Shuo and {Qin}, Li and {Lu}, Yan and {Li}, Yin-Bi and {Chen}, Jian-Jun and {Zhao}, Yong-Heng},
title = "{A Catalog of Early-type H{\ensuremath{\alpha}} Emission-line Stars and 62 Newly Confirmed Herbig Ae/Be Stars from LAMOST Data Release 7}",
journal = {\apjs},
keywords = {Ae stars, Pre-main sequence, Be stars, Herbig Ae/Be stars, 20, 1289, 142, 723, Astrophysics - Solar and Stellar Astrophysics},
year = 2022,
month = apr,
volume = {259},
number = {2},
eid = {38},
pages = {38},
doi = {10.3847/1538-4365/ac4964},
archivePrefix = {arXiv},
eprint = {2107.00402},
primaryClass = {astro-ph.SR},
adsurl = {https://ui.adsabs.harvard.edu/abs/2022ApJS..259...38Z},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}}@ARTICLE{2021RAA....21..288S,
author = {{Shridharan}, Baskaran and {Mathew}, Blesson and {Nidhi}, Sabu and {Anusha}, Ravikumar and {Arun}, Roy and {Kartha}, Sreeja S. and {Kumar}, Yerra Bharat},
title = "{Discovery of 2716 hot emission-line stars from LAMOST DR5}",
journal = {Research in Astronomy and Astrophysics},
keywords = {stars: early-type, methods: data analysis, techniques: photometric, astronomical databases: catalogs, Astrophysics - Solar and Stellar Astrophysics},
year = 2021,
month = dec,
volume = {21},
number = {11},
eid = {288},
pages = {288},
doi = {10.1088/1674-4527/21/11/288},
archivePrefix = {arXiv},
eprint = {2108.08025},
primaryClass = {astro-ph.SR},
adsurl = {https://ui.adsabs.harvard.edu/abs/2021RAA....21..288S},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}}@ARTICLE{2021PASP..133l4501Y,
author = {{Yang}, Yujie and {Jiang}, Bin},
title = "{Searching for Galactic H II Regions from the LAMOST Spectroscopic Database}",
journal = {\pasp},
keywords = {1671, 1858, 694},
year = 2021,
month = dec,
volume = {133},
number = {1030},
eid = {124501},
pages = {124501},
doi = {10.1088/1538-3873/ac193a},
adsurl = {https://ui.adsabs.harvard.edu/abs/2021PASP..133l4501Y},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}}@ARTICLE{2020yCatp040001605H,
author = {{Hou}, W. and {Luo}, A. -L. and {Hu}, J. -Y. and {Yang}, H. -F. and {Du}, C. -D. and {Liu}, C. and {Lee}, C. -D. and {Lin}, C. -C. and {Wang}, Y. -F. and {Zhang}, Y. and {Cao}, Z. -H. and {Hou}, Y. -H.},
title = "{VizieR Online Data Catalog: LAMOST catalog of early-type emission-line stars (Hou+, 2016)}",
journal = {VizieR Online Data Catalog (other)},
keywords = {Stars: emission, Stars: early-type},
year = 2020,
month = aug,
volume = {0400},
eid = {J/other/RAA/16},
pages = {J/other/RAA/16},
adsurl = {https://ui.adsabs.harvard.edu/abs/2020yCatp040001605H},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}}@ARTICLE{2020RAA....20...97Z,
author = {{Zhang}, Meng and {Chen}, Bing-Qiu and {Huo}, Zhi-Ying and {Zhang}, Hua-Wei and {Xiang}, Mao-Sheng and {Yuan}, Hai-Bo and {Huang}, Yang and {Wang}, Chun and {Liu}, Xiao-Wei},
title = "{A catalogue of H{\ensuremath{\alpha}} emission-line point sources in the vicinity fields of M 31 and M 33 from the LAMOST survey}",
journal = {Research in Astronomy and Astrophysics},
keywords = {stars: emission-line, planetary nebulae: general, galaxies: individual (M 31, M 33), 694, Astrophysics - Astrophysics of Galaxies},
year = 2020,
month = jun,
volume = {20},
number = {6},
eid = {097},
pages = {097},
doi = {10.1088/1674-4527/20/6/97},
archivePrefix = {arXiv},
eprint = {2001.11681},
primaryClass = {astro-ph.GA},
adsurl = {https://ui.adsabs.harvard.edu/abs/2020RAA....20...97Z},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}}@ARTICLE{2020AJ....159...43H,
author = {{Hou}, Wen and {Luo}, A. -li and {Li}, Yin-Bi and {Qin}, Li},
title = "{Spectroscopically Identified Cataclysmic Variables from the LAMOST Survey. I. The Sample}",
journal = {\aj},
keywords = {Cataclysmic variable stars, Dwarf novae, Nova-like variable stars, Catalogs, Astronomy data analysis, 203, 418, 1126, 205, 1858, Astrophysics - Solar and Stellar Astrophysics},
year = 2020,
month = feb,
volume = {159},
number = {2},
eid = {43},
pages = {43},
doi = {10.3847/1538-3881/ab5962},
archivePrefix = {arXiv},
eprint = {1911.08338},
primaryClass = {astro-ph.SR},
adsurl = {https://ui.adsabs.harvard.edu/abs/2020AJ....159...43H},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}}@ARTICLE{2020A&A...643A.122S,
author = {{{\v{S}}koda}, P. and {Podsztavek}, O. and {Tvrd{\'\i}k}, P.},
title = "{Active deep learning method for the discovery of objects of interest in large spectroscopic surveys{\ensuremath{\star}}}",
journal = {\aap},
keywords = {surveys, virtual observatory tools, methods: statistical, techniques: spectroscopic, stars: emission-line, Be, line: profiles, Astrophysics - Instrumentation and Methods for Astrophysics, Computer Science - Machine Learning},
year = 2020,
month = nov,
volume = {643},
eid = {A122},
pages = {A122},
doi = {10.1051/0004-6361/201936090},
archivePrefix = {arXiv},
eprint = {2009.03219},
primaryClass = {astro-ph.IM},
adsurl = {https://ui.adsabs.harvard.edu/abs/2020A&A...643A.122S},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}}- Central stars of PNe in GAIA -> González-Santamaría, I. et. al.
- HII regions -> Anderson, L. D. et. al..
- SySt -> Akras et. al
- Near stars GAIA -> Smart, R. L. et al..
- The STAR- MELT PYTHON package for emission-line analysis of YSOs, Campbell-White, Justyn et. al. 2021.
- PyNeb.
- Specutils.
df_new = pd.read_csv("cans-new-gaiadr3.csv")df_pn_all = pd.read_csv("Luis_hash-pn-gaia.csv")
mask = df_pn_all["PNstat"] == "T"
df_pn = df_pn_all[mask]import numpy as np
import json
import matplotlib.pyplot as plt
from astropy.table import Table
import pandas as pd
import seaborn as sns
from scipy.stats import gaussian_kde
<<read-gaia-cans-new>>
<<read-gaia-pn>>
lgd_kws = {'frameon': True, 'fancybox': True, 'shadow': True}
# removing inf or nan values
col = ["parallax", "phot_g_mean_mag", "bp_rp"]
df_new = df_new[col]
df_new1 = df_new.dropna()
# G-mag
Gmag = np.array(df_new1["phot_g_mean_mag"])
# Color
#cbp_rpmag = df["phot_bp_mean_mag"] - df["phot_rp_mean_mag"]
bp_rpmag = np.array(df_new1["bp_rp"])
# Calculate the point density
xy = np.vstack([bp_rpmag, Gmag])
z = gaussian_kde(xy)(xy)
# Sort the points by density, so that the densest points are plotted last
idx = z.argsort()
x, y, z = bp_rpmag[idx], Gmag[idx], z[idx]
# PNe
Gmag_pn = df_pn["phot_g_mean_mag"]
bp_rpmag_pn = df_pn["bp_rp"]
colors = ["cerulean",]
colors = sns.xkcd_palette(colors)
# # known PNe
# Gmag_cv = df_cv["phot_g_mean_mag"]
# bp_rpmag_cv = df_cv["bp_rp"]
# colors1 = ["pale yellow"]
# colors1 = sns.xkcd_palette(colors1)<<gaia-match>>
pltfile = 'color-mag-gaia-true.pdf'
sns.set_style('ticks')
fig = plt.figure(figsize=(6, 7))
ax = fig.add_subplot(111)
ax.scatter(x, y, c=z, s=50, zorder = 10,edgecolor=['none'])
ax.scatter(bp_rpmag_pn, Gmag_pn, c = colors, edgecolor=['black'], alpha = 0.4, s = 50)
#plt.scatter(bp_rpmag, Gmag, alpha=0.8)
plt.xlabel(r'$G_{BP} - G_{RP}$')
plt.ylabel(r'$G$')
#ax.set_xlim(-30.0, 390.0)
#ax.set_ylim(-90.0, 90.0)
ax.legend(prop={'family': 'monospace', 'size': 'x-small'}, **lgd_kws)
plt.gca().invert_yaxis()
fig.savefig(pltfile) <<gaia-match>>
# Absolute magnitude
# G-mag
Gmag_abs = np.array(df_new1["phot_g_mean_mag"]) + 5*np.log10(np.array(df_new1["parallax"] / 1000.)) + 5
# Color
bp_rpmag = np.array(df_new1["bp_rp"])
# Creating new pandas table
data = {'G_abs':Gmag_abs,
'bp_rp':bp_rpmag}
df_result = pd.DataFrame(data)
df_result_new = df_result.dropna()
G_abs = np.array(df_result_new["G_abs"])
bp_rp = np.array(df_result_new["bp_rp"])
# Calculate the point density
xy = np.vstack([bp_rp, G_abs])
z = gaussian_kde(xy)(xy)
# Sort the points by density, so that the densest points are plotted last
idx = z.argsort()
x, y, z = bp_rp[idx], G_abs[idx], z[idx]
# PN
Gmag_pn_abs = df_pn["phot_g_mean_mag"] + 5*np.log10(np.array(df_pn["parallax"] / 1000.)) + 5
bp_rpmag_pn = df_pn["bp_rp"]
colors = ["cerulean",]
colors = sns.xkcd_palette(colors)
# # CV
# Gmag_cv_abs = df_cv["phot_g_mean_mag"] + 5*np.log10(np.array(df_cv["parallax"] / 1000.)) + 5
# bp_rpmag_cv = df_cv["bp_rp"]
# colors1 = ["pale yellow",]
# colors1 = sns.xkcd_palette(colors1)
pltfile = 'color-mag_abs-gaia.pdf'
sns.set_style('ticks')
fig = plt.figure(figsize=(6, 7))
ax = fig.add_subplot(111)
ax.scatter(x, y, c=z, s=50, edgecolor=['none'])
ax.scatter(bp_rpmag_pn, Gmag_pn_abs, c = colors, edgecolor=['black'], alpha = 0.4,
s = 50, label= "PN")
#ax.scatter(bp_rpmag_cv, Gmag_cv_abs, c = colors1, edgecolor=['black'], alpha = 0.9, s = 50)
#plt.scatter(bp_rpmag, Gmag_abs, alpha=0.8)
plt.xlabel(r'$G_{BP} - G_{RP}$')
plt.ylabel(r'$M_G + A_G$')
#ax.set_xlim(-30.0, 390.0)
#ax.set_ylim(-90.0, 90.0)
ax.legend(prop={'family': 'monospace', 'size': 'x-small'}, **lgd_kws)
plt.gca().invert_yaxis()
fig.savefig(pltfile) <<gaia-match>>
# removing inf or nan values
col = ["parallax"]
df_new = df_new[col]
df_new1 = df_new.dropna()
d = 1 / df_new1["parallax"]
# QSOs from simbad
df_pn_new = df_pn[col]
df_pn_new1 = df_pn_new.dropna()
d_pn = 1 / df_pn_new1["parallax"]
pltfile = 'dist-distance-gaia.pdf'
with sns.axes_style("ticks"):
# Bar diagram
fig, ax1 = plt.subplots(1, 1, figsize=(10, 6), sharex=True)
plt.xlabel(r"$D(Kpc)$", fontsize=33)
plt.ylabel(r"Density", fontsize=33)
plt.tick_params(axis='x', labelsize=33)
plt.tick_params(axis='y', labelsize=33)
d = [x for x in d]
sns.distplot(d,norm_hist=True, kde=False, ax=ax1,
bins=2000, color='r')
d_pn = [x for x in d_pn]
sns.distplot(d_pn, norm_hist=True, kde=False, ax=ax1,
bins=2000, color='b', label = "PN")
ax1.set(xlim=[-50, 70])
ax1.legend(loc='upper left')
ymax = ax1.get_ybound()[1]
sns.despine()
plt.tight_layout()
plt.savefig(pltfile)- The comobination,
g, r, i:python ../programs/rgb_image-ps1.py cutout_rings_v3_skycell_2294_031_stk_i_unconv cutout_rings_v3_skycell_2294_031_stk_r_unconv cutout_rings_v3_skycell_2294_031_stk_g_unconv --vmin_r -100.2 --vmax_r 7890.0 --vmin_g -100 --vmax_g 3000 --vmin_b -100.4 --vmax_b 3000.0 --debug- WISE and Panstarrs coloured images were made.
I put all the HASH and Halo and new discovery together in the l versus b diagram.
J/ApJS/92/125 Post-AGB evolution (Vassiliadis+, 1994)
================================================================================
Post-asymptotic giant branch evolution of low- to intermediate-mass stars
VASSILIADIS E., WOOD P.R.
<Astrophys. J. Suppl. Ser. 92, 125 (1994)>
=1994ApJS...92..125V (SIMBAD/NED Reference)
================================================================================
ADC_Keywords: Models, evolutionary; Mass loss; Nebulae, planetary
Keywords: Magellanic Clouds - planetary nebulae: general - stars: evolution -
stars: interiors
Abstract:
In this paper, we present the results for the post-AGB phases of
stellar evolutionary sequences, complete from the main-sequence phase,
through the AGB phase, and on into the planetary nebula and white
dwarf regimes. Mass loss has been included using an empirical
formalism derived from observed mass-loss rates of planetary nebula
nuclei available in the literature and from radiation-pressure-driven
stellar wind theory. Models are calculated for initial masses 0.89,
0.95, 1.0, 1.5, 2.0, 2.5, 3.5, and 5.0M_{sun}_, and metallicities
0.016, 0.008, 0.004, and 0.001. These abundance and mass values were
chosen to allow comparison with Galactic, and Magellanic Cloud planetary
nebulae and their nuclei. The post-AGB evolutionary sequences fall
into two distinct groups depending on when the planetary nebula nuclei
leave the AGB: one group where helium-shell burning is dominant, and
the other group where hydrogen-shell burning is dominant. Of the 27
computed sequences: 17 are hydrogen-burners, and 10 are helium-burners.
In only five cases was any effort made to control the phase of departure
from the AGB. Lower mass models are more likely to leave the AGB burning
helium, as the preceding AGB evolution has a mass-loss rate which is
greatest immediately prior to a helium-shell flash. The calculations
are compared with the large observational database that has developed
over recent years for the Large Magellanic Cloud. These calculations
will be useful for determining the planetary nebula luminosity function,
and for the study of the ultraviolet excess observed in elliptical
galaxies.
File Summary:
--------------------------------------------------------------------------------
FileName Lrecl Records Explanations
--------------------------------------------------------------------------------
ReadMe 80 . This file
table.tex 87 1014 LaTeX version of the tables
table3 41 720 H-Burning PNN Evolutionary Models
table4 41 401 He-Burning PNN Evolutionary Models
table5 41 45 H-Like He-Burning PNN Evolutionary Model
J/A+A/588/A25 Post-AGB and CSPNe evolutionary models (Miller Bertolami, 2016)
================================================================================
New models for the evolution of post-asymptotic giant branch stars and central
stars of planetary nebulae.
Miller Bertolami M.M.
<Astron. Astrophys. 588, A25 (2016)>
=2016A&A...588A..25M (SIMBAD/NED BibCode)
================================================================================
ADC_Keywords: Models, evolutionary ; Stars, giant ; Planetary nebulae
Keywords: stars: AGB and post-AGB - stars: low-mass - stars: evolution -
planetary nebulae: general
Abstract:
The post-asymptotic giant branch (AGB) phase is arguably one of the
least understood phases of the evolution of low- and intermediate-
mass stars. The two grids of models presently available are based on
outdated micro- and macrophysics and do not agree with each other.
Studies of the central stars of planetary nebulae (CSPNe) and post-AGB
stars in different stellar populations point to significant
discrepancies with the theoretical predictions of post-AGB models.
We study the timescales of post-AGB and CSPNe in the context of our
present understanding of the micro- and macrophysics of stars. We want
to assess whether new post-AGB models, based on the latter
improvements in TP-AGB modeling, can help us to understand the
discrepancies between observation and theory and within theory itself.
In addition, we aim to understand the impact of the previous AGB
evolution for post-AGB phases.
We computed a grid of post-AGB full evolutionary sequences that
include all previous evolutionary stages from the zero age main
sequence to the white dwarf phase. We computed models for initial
masses between 0.8 and 4M_{sun}_ and for a wide range of initial
metallicities (Z_0_= 0.02, 0.01, 0.001, 0.0001). This allowed us to
provide post-AGB timescales and properties for H-burning post-AGB
objects with masses in the relevant range for the formation of
planetary nebulae (~0,5-0,8M_{sun}_). We included an updated treatment
of the constitutive microphysics and included an updated description
of the mixing processes and winds that play a key role during the
thermal pulses (TP) on the AGB phase.
Description:
We compute a grid of post-AGB full evolutionary sequences that are
derived from full evolutionary models which include all previous
evolutionary stages from the Zero Age Main Sequence to the White Dwarf
phase. Models are computed for initial masses between 0.8 and
4M_{sun}_ and for a wide range of initial metallicities. Two grids of
post-AGB models are provided. The main grid of 24 H-burning post-AGB
sequences corresponds to the sequences presented in table 3 of the
article. Each file contains all the sequences with a given initial
metallicity and are named as 0200_T03.dat, 0100_T03.dat 0010_T03.dat
and 0001_T03.dat (corresponding to metallicities Z=0.02, 0.01, 0.001
and 0.0001 in table 3).
The second grid corresponds to the sequences presented in Appendix B,
table B.2 (originally presented in Miller Bertolami, M. M. 2015, in
ASPCS, Vol. 493, 83) and are named as 0100_TB2.dat and 0010_TB2.dat
(corresponding to metallicities Z=0.01 and 0.001 in table B2)
All sequences are presented at equivalent evolutionary points to allow
easy interpolation:
Sequences are presented until their luminosity drops to 1 solar
luminosity or until a late helium flash develops.
File Summary:
--------------------------------------------------------------------------------
FileName Lrecl Records Explanations
--------------------------------------------------------------------------------
ReadMe 80 . This file
list.dat 102 6 List of models
models/* . 6 *Individual files
--------------------------------------------------------------------------------
Note on models/*: Inside each file each sequence is started with a short header
(7 lines) indicating the nature of the model (post-AGB, H-burner),
composition and the initial and final masses of the original simulation.
Different evolutionary sequences are separated by 2 empty lines.
--------------------------------------------------------------------------------
Byte-by-byte Description of file: list.dat
--------------------------------------------------------------------------------
Bytes Format Units Label Explanations
--------------------------------------------------------------------------------
1- 12 A12 --- FileName Name of the table in subdirectory models
14-102 A89 --- Title Title of the table
--------------------------------------------------------------------------------
Byte-by-byte Description of file (#): models/*
--------------------------------------------------------------------------------
Bytes Format Units Label Explanations
--------------------------------------------------------------------------------
1- 5 I5 --- N Track point number
7- 15 F9.6 [Lsun] logL logarithm of the stellar luminosity
17- 25 F9.6 [K] logTeff logarithm of the effective temperature
27- 35 F9.6 [cm/s2] logg logarithm of the surface gravity
40- 51 F12.4 yr t Age since the point at LogTeff=3.85
53- 61 F9.6 --- Menv Fractional mass of the envelope
63- 71 F9.6 Msun Mstar Total mass of the star
73- 82 F10.6 [Msun/yr] log(-dM/dt) Logarithm of the Mass Loss Rate,
log(-dMstar/dt)
--------------------------------------------------------------------------------
Size of the images dowloaded from WISE:
- PNPRTM1 => 70arcsec
Distance in parsec, Estimating distances from parallaxes (1467744818 sources) ( Bailer-Jones C.A.L., Rybizki J., Fouesneau M., Demleitner M., Andrae R .)
rgeo=2313.00952
rpgeo=2324.19312
Considering the referee report. I worked on the project to add more scientific insights to the paper. I contributed to the paper by supplementing it with additional LAMOST spectra.
- True PNe:
- Duplicate objects
Duplicate Objects: Object spec-56396-HD125932N280356M01_sp04-145.fits and Object spec-56769-VB194N28V1_sp04-145.fits
Duplicate Objects: Object spec-56396-HD125932N280356M01_sp04-145.fits and Object spec-56396-HD125932N280356F01_sp04-145.fits
Duplicate Objects: Object spec-56249-HD084529N184902M01_sp08-197.fits and Object spec-56249-HD084529N184902B01_sp08-197.fits
Duplicate Objects: Object spec-56249-HD084529N184902M01_sp08-197.fits and Object spec-58460-KII084529N184902B02_sp08-197.fits
Duplicate Objects: Object spec-58549-HD163804N393437V01_sp08-159.fits and Object spec-56018-B5601808_sp04-177.fits
Duplicate Objects: Object spec-55960-GAC_071N47_V1_sp05-062.fits and Object spec-56701-GAC071N47V1_sp05-062.fits
Duplicate Objects: Object spec-55960-GAC_071N47_V1_sp05-062.fits and Object spec-56996-HD043544N451252V01_sp12-022.fits
Duplicate Objects: Object spec-56966-HD213605N150455V01_sp01-008.fits and Object spec-57325-EG214033N103644V01_sp16-228.fits
Duplicate Objects: Object spec-58493-KII062035N253629B02_sp16-084.fits and Object spec-58096-KII062035N253629B01_sp16-084.fits
Duplicate Objects: Object spec-56701-GAC071N47V1_sp05-062.fits and Object spec-56996-HD043544N451252V01_sp12-022.fits
Duplicate Objects: Object spec-58257-KP192102N424113V05_sp13-110.fits and Object spec-56436-kepler02_1_sp13-139.fits
Duplicate Objects: Object spec-56769-VB194N28V1_sp04-145.fits and Object spec-56396-HD125932N280356F01_sp04-145.fits
Duplicate Objects: Object spec-56249-HD084529N184902B01_sp08-197.fits and Object spec-58460-KII084529N184902B02_sp08-197.fits
Duplicate Objects: Object spec-56326-GAC069N36V2_sp08-030.fits and Object spec-56570-GAC069N36V2_sp08-030.fits
Duplicate Objects: Object spec-58253-HD175745N291452V02_sp05-212.fits and Object spec-56767-HD175745N291452V01_sp05-212.fits
- Likely and probable PNe:
- Duplicate objects
uplicate Objects: Object spec-58042-HD070023N151717V01_sp05-172.fits and Object spec-56258-GAC105N15V1_sp05-035.fits
Duplicate Objects: Object spec-57019-HD083217N291909M01_sp16-242.fits and Object spec-57725-HD083217N291909M02_sp16-242.fits
Duplicate Objects: Object spec-55903-M31_021N30_B1_sp13-190.fits and Object spec-55907-M31_021N30_B1_sp13-190.fits
Duplicate Objects: Object spec-55903-M31_021N30_B1_sp13-190.fits and Object spec-55931-M31_025N30_B2_sp13-176.fits
Duplicate Objects: Object spec-55907-M31_021N30_B1_sp13-190.fits and Object spec-55931-M31_025N30_B2_sp13-176.fits