J/A+A/693/A306 SPLUS DR4 VAC - Predicted Parameters (Ferriera Lopes+, 2025)
Stellar atmospheric parameters and chemical abundances of ∼5 million stars
from S-PLUS multi-band photometry.
Ferriera Lopes C.E., Gutierrez-Soto L.A., Ferreira Alberice V.S.,
Monsalves N., Hazarika D., Catelan M., Placco V.M., Limberg G.,
Almeida-Fernandes F., Perottoni H.D., Smith Castelli A.V., Akras S.,
Alonso-Garcia J., Cordeiro V., Jaque Arancibia M., Daflon S., Dias B.,
Goncalves D.R., Machado-Pereira E., Lopes A.R., Bom C.R.,
Thom de Souza R.C., de Isidio N.G., Alvarez-Candal A., De Rossi M.E.,
Bonatto C.J., Cubillos Palma B., Borges Fernandes M., Humire P.K.,
Oliveira Schwarz G.B., Schoenell W., Kanaan A., Mendes de Oliveira C.
<Astron. Astrophys. 693, A306 (2025)>
=2025A&A...693A.306F 2025A&A...693A.306F (SIMBAD/NED BibCode)
ADC_Keywords: Surveys ; Photometry, SDSS ; Optical ; Abundances ;
Effective temperatures
Keywords: catalogs - stars: abundances - Galaxy: abundances
Abstract:
The APOGEE, GALAH, and LAMOST spectroscopic surveys have substantially
contributed to our understanding of the Milky Way by providing a wide
range of stellar parameters and chemical abundances. Complementing
these efforts, photometric surveys that include narrow/medium-band
filters, such as the Southern Photometric Local Universe Survey
(S-PLUS), provide a unique opportunity to estimate atmospheric
parameters and elemental abundances for a much larger number of
sources compared to spectroscopic surveys.
Establish methodologies for extracting stellar atmospheric parameters
and selected chemical abundances from S-PLUS photometric data, which
cover approximately 3000 square degrees, by applying seven narrowband
and five broad-band filters.
We used all 66 S-PLUS colors to estimate parameters based on three
different training samples from the LAMOST, APOGEE, and GALAH surveys,
applying Cost-Sensitive Neural Network (NN) and Random Forest (RF)
algorithms. We kept stellar abundances that lacked corresponding
absorption features in the S-PLUS filters to test for spurious
correlations in our method. Furthermore, we evaluated the
effectiveness of the NN and RF algorithms by using estimated Teff and
log g as input features to determine other stellar parameters and
abundances. The NN approach consistently outperforms the RF technique
on all parameters tested. Moreover, incorporating Teff and log g leads
to an improvement in the estimation accuracy by approximately 3%. We
kept only parameters with a goodness-of-fit higher than 50%.
Our methodology allowed reliable estimates for fundamental stellar
parameters (Teff , log g, and [Fe/H]) and elemental abundance ratios
such as [alpha/Fe], [Al/Fe], [C/Fe], [Li/Fe], and [Mg/Fe] for
approximately 5 million stars across the Milky Way, with
goodness-of-fit above 60%. We also obtained additional abundance
ratios, including [Cu/Fe], [O/Fe], and [Si/Fe]. However, these ratios
should be used cautiously due to their low accuracy or lack of a clear
relationship with the S-PLUS filters. Validation of our estimations
and methods was performed using star clusters, TESS (Transiting
Exoplanet Survey Satellite) data, and J-PLUS (Javalambre Photometric
Local Universe Survey) photometry, further demonstrating the
robustness and accuracy of our approach.
By leveraging S-PLUS photometric data and advanced machine-learning
techniques, we have established a robust framework for extracting
fundamental stellar parameters and chemical abundances from medium-
and narrowband photometric observations. This approach offers a
cost-effective alternative to high-resolution spectroscopy, and the
estimated parameters hold significant potential for future studies,
particularly in classifying objects within our Milky Way or gaining
insights into its various stellar populations.
Description:
Stellar atmospheric parameters and chemical abundances of ∼5 million
stars are presented. We used the SPLUS DR4 photometric data observed
using T80S telescope at CTIO, combined with the spectroscopic catalog
data from GALAH, APOGEE and LAMOST to calculate the parameters.
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
giantdr4.dat 438 148477 SPLUS DR4 VAC - Predicted Parameters for Giants
dwarfdr4.dat 443 4825823 SPLUS DR4 VAC - Predicted Parameters for Dwarfs
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See also:
II/246 : 2MASS All-Sky Catalog of Point Sources (Cutri+ 2003)
Byte-by-byte Description of file: giantdr4.dat
--------------------------------------------------------------------------------
Bytes Format Units Label Explanations
--------------------------------------------------------------------------------
1- 28 A28 --- ID Unique identifier for each celestial
source (ID)
30- 40 F11.7 deg RAdeg Right Ascension coordinate of the
source (J2000) (RA)
42- 52 F11.7 deg DEdeg Declination coordinate of the
source (J2000) (DEC)
54- 60 F7.4 [-] [Fe/H]APO Abundance ratio of Iron to Hydrogen,
calculated using APOGEE training set
(FeH_APO)
62- 64 I3 % f_[Fe/H]APO [33/100] Indicating % of features
within the training set range, with
100 for all features within range
(FeHAPOFL)
66- 72 F7.4 [-] [Fe/H]GAL Abundance ratio of Iron to Hydrogen,
calculated using GALAH training set
(FeH_GAL)
74- 76 I3 % f_[Fe/H]GAL [7/100] Indicating % of features within
the training set range, with 100 for
all features within range (FeHGALFL)
78- 84 F7.4 [-] [alpha/Fe]MAPO Abundance ratio of Alpha elements to
Iron, calculated using APOGEE training
set (AlphaM_APO)
86- 88 I3 % f_[alpha/Fe]MAPO [33/100] Indicating % of features
within the training set range, with
100 for all features within range
(AlphaMAPOFL)
90- 96 F7.4 [-] [Al/Fe]APO Abundance ratio of Aluminium to Iron,
calculated using APOGEE training set
(AlFe_APO)
98-100 I3 % f_[Al/Fe]APO [32/100] Indicating % of features
within the training set range, with
100 for all features within range
(AlFeAPOFL)
102-108 F7.4 [-] [C/Fe]APO Abundance ratio of Carbon to Iron,
calculated using APOGEE training set
(CFe_APO)
110-112 I3 % f_[C/Fe]APO [33/100] Indicating % of features
within the training set range, with
100 for all features within range
(CFeAPOFL)
114-120 F7.4 [-] [Li/Fe]GAL Abundance ratio of Lithium to Iron,
calculated using GALAH training set
(LiFe_GAL)
122-124 I3 % f_[Li/Fe]GAL [5/100] Indicating % of features within
the training set range, with 100 for
all features within range
(LiFeGALFL)
126-132 F7.4 [-] [Mg/Fe]APO Abundance ratio of Magnesium to Iron,
calculated using APOGEE training set
(MgFe_APO)
134-136 I3 % f_[Mg/Fe]APO [33/100] Indicating % of features
within the training set range, with
100 for all features within range
(MgFeAPOFL)
138-144 F7.4 [-] [Cu/Fe]GAL Abundance ratio of Copper to Iron,
calculated using GALAH training set
(CuFe_GAL)
146-148 I3 % f_[Cu/Fe]GAL [7/100] Indicating % of features
within the training set range, with
100 for all features within range
(CuFeGALFL)
150-156 F7.4 [-] [O/Fe]APO Abundance ratio of Oxygen to Iron,
calculated using APOGEE training set
(OFe_APO)
158-160 I3 % f_[O/Fe]APO [33/100] Indicating % of features
within the training set range, with
100 for all features within range
(OFeAPOFL)
162-168 F7.4 [-] [Si/Fe]APO Abundance ratio of Silicon to Iron,
calculated using APOGEE training set
(SiFe_APO)
170-172 I3 % f_[Si/Fe]APO [33/100] Indicating % of features
within the training set range, with
100 for all features within range
(SiFeAPOFL)
174-182 F9.4 K TeffGAL Effective temperature of the source,
calculated using GALAH training set
(Teff_GAL)
184-186 I3 % f_TeffGAL [6/100] Indicating % of features within
the training set range, with 100 for
all features within range
(TeffGALFL)
188-196 F9.4 K TeffAPO Effective temperature of the source,
calculated using APOGEE training set
(Teff_APO)
198-200 I3 % f_TeffAPO [31/100] Indicating % of features
within the training set range, with
100 for all features within range
(TeffAPOFL)
202-207 F6.4 [cm/s2] loggGAL Logarithm of the surface gravity,
calculated using GALAH training set
(logg_GAL)
209-211 I3 --- f_loggGAL [6/100] Indicating % of features within
the training set range, with 100 for
all features within range
(loggGALFL)
213-218 F6.4 [cm/s2] loggAPO Logarithm of the surface gravity,
calculated using APOGEE training set
(logg_APO)
220-222 I3 --- f_loggAPO [31/100] Indicating % of features
within the training set range, with
100 for all features within range
(loggAPOFL)
224-232 F9.4 K TeffGaia Effective temperature of the source
from GAIA DR3 catalog (Teff_GAIA)
234-239 F6.4 [cm/s2] loggGaia Logarithm of the surface gravity from
GAIA DR3 catalog (logg_GAIA)
241-247 F7.4 [-] [Fe/H]Gaia Abundance ratio of Iron to Hydrogen
from GAIA DR3 catalog (FeH_GAIA)
249-258 F10.4 pc DistGaia Distance from GAIA DR3 catalog
(Dist_GAIA)
260-266 F7.4 mag umag Photometric measurement in the
uJAVA filter, corresponding to the
u-band (U)
268-274 F7.4 mag e_umag Error or uncertainty associated with
the uJAVA filter measurement (eU)
276-282 F7.4 mag J378 Photometric measurement in the
J0378 filter (J378)
284-290 F7.4 mag e_J378 Error or uncertainty associated with
the J0378 filter measurement (eJ378)
292-298 F7.4 mag J395 Photometric measurement in the
J0395 filter (J395)
300-306 F7.4 mag e_J395 Error or uncertainty associated with
the J0395 filter measurement (eJ395)
308-314 F7.4 mag J410 Photometric measurement in the
J0410 filter (J410)
316-322 F7.4 mag e_J410 Error or uncertainty associated with
the J0410 filter measurement (eJ410)
324-330 F7.4 mag J430 Photometric measurement in the
J0430 filter (J430)
332-338 F7.4 mag e_J430 Error or uncertainty associated with
the J0430 filter measurement (eJ430)
340-346 F7.4 mag gmag Photometric measurement in the
gSDSS filter, corresponding to the
g-band (G)
348-354 F7.4 mag e_gmag Error or uncertainty associated with
the gSDSS filter measurement (eG)
356-361 F6.4 mag J515 Photometric measurement in the
J0515 filter (J515)
363-368 F6.4 mag e_J515 Error or uncertainty associated with
the J0515 filter measurement (eJ515)
370-375 F6.4 mag rmag Photometric measurement in the
rSDSS filter, corresponding to
the r-band (R)
377-382 F6.4 mag e_rmag Error or uncertainty associated with
the rSDSS filter measurement (eR)
384-389 F6.4 mag J660 Photometric measurement in the
J0660 filter (J660)
391-396 F6.4 mag e_J660 Error or uncertainty associated with
the J0660 filter measurement (eJ660)
398-403 F6.4 mag imag Photometric measurement in the iSDSS
filter, corresponding to the
i-band (I)
405-410 F6.4 mag e_imag Error or uncertainty associated with
the iSDSS filter measurement (eI)
412-417 F6.4 mag J861 Photometric measurement in the
J0861 filter (J861)
419-424 F6.4 mag e_J861 Error or uncertainty associated with
the J0861 filter measurement (eJ861)
426-431 F6.4 mag zmag Photometric measurement in the
zSDSS filter, corresponding to the
z-band (Z)
433-438 F6.4 mag e_zmag Error or uncertainty associated
with the zSDSS filter measurement (eZ)
--------------------------------------------------------------------------------
Byte-by-byte Description of file: dwarfdr4.dat
--------------------------------------------------------------------------------
Bytes Format Units Label Explanations
--------------------------------------------------------------------------------
1- 28 A28 --- ID Unique identifier for each celestial
source (ID)
30- 40 F11.7 deg RAdeg Right Ascension coordinate of the
source (J2000) (RA)
42- 52 F11.7 deg DEdeg Declination coordinate of the source
(J2000) (DEC)
54- 60 F7.4 [-] [Fe/H]APO Abundance ratio of Iron to Hydrogen,
calculated using APOGEE training set
(FeH_APO)
62- 64 I3 % f_[Fe/H]APO [2/100] Indicating % of features within
the training set range, with 100 for
all features within range (FeHAPOFL)
66- 72 F7.4 [-] [Fe/H]GAL Abundance ratio of Iron to Hydrogen,
calculated using GALAH training set
(FeH_GAL)
74- 76 I3 % f_[Fe/H]GAL [1/100] Indicating % of features within
the training set range, with 100 for
all features within range (FeHGALFL)
78- 84 F7.4 [-] [Fe/H]LMR Abundance ratio of Iron to Hydrogen,
calculated using LAMOST training set
(FeH_LMR)
86- 88 I3 % f_[Fe/H]LMR [1/100] Indicating % of features within
the training set range, with 100 for
all features within range (FeHLMRFL)
90- 96 F7.4 [-] [alpha/Fe]MAPO Abundance ratio of Alpha elements
to Iron, calculated using APOGEE
training set (AlphaM_APO)
98-100 I3 % f_[alpha/Fe]MAPO [2/100] Indicating % of features within
the training set range, with 100 for
all features within range
(AlphaMAPOFL)
102-108 F7.4 [-] [alpha/Fe]MLMR Abundance ratio of Alpha elements to
Iron, calculated using LAMOST training
set (AlphaM_LMR)
110-112 I3 % f_[alpha/Fe]MLMR [1/100] Indicating % of features
within the training set range, with
100 for all features within range
(AlphaMLMRFL)
114-120 F7.4 [-] [Li/Fe]GAL Abundance ratio of Lithium to Iron,
calculated using GALAH training set
(LiFe_GAL)
122-124 I3 % f_[Li/Fe]GAL [1/100] Indicating % of features
within the training set range, with
100 for all features within range
(LiFeGALFL)
126-132 F7.4 [-] [Mg/Fe]APO Abundance ratio of Magnesium to
Iron, calculated using APOGEE training
set (MgFe_APO)
134-136 I3 % f_[Mg/Fe]APO [2/100] Indicating % of features
within the training set range, with
100 for all features within range
(MgFeAPOFL)
138-144 F7.4 [-] [Si/Fe]APO Abundance ratio of Silicon to
Iron, calculated using APOGEE training
set (SiFe_APO)
146-148 I3 % f_[Si/Fe]APO [2/100] Indicating % of features
within the training set range, with
100 for all features within range
(SiFeAPOFL)
150-159 F10.4 K TeffGAL Effective temperature of the source,
calculated using GALAH training set
(Teff_GAL)
161-163 I3 % f_TeffGAL [1/100] Indicating % of features within
the training set range, with 100 for
all features within range
(TeffGALFL)
165-174 F10.4 K TeffAPO Effective temperature of the source,
calculated using APOGEE training set
(Teff_APO)
176-178 I3 % f_TeffAPO [1/100] Indicating % of features within
the training set range, with 100 for
all features within range
(TeffAPOFL)
180-189 F10.4 K TeffLMR Effective temperature of the source,
calculated using LAMOST training set
(Teff_LMR)
191-193 I3 % f_TeffLMR Indicating % of features within the
training set range, with 100 for all
features within range (TeffLMRFL)
195-200 F6.4 [cm/s2] loggGAL Logarithm of the surface gravity,
calculated using GALAH training set
(logg_GAL)
202-204 I3 % f_loggGAL Indicating % of features within the
training set range, with 100 for all
features within range (loggGALFL)
206-211 F6.4 [cm/s2] loggAPO Logarithm of the surface gravity,
calculated using APOGEE training set
(logg_APO)
213-215 I3 % f_loggAPO Indicating % of features within the
training set range, with 100 for all
features within range (loggAPOFL)
217-222 F6.4 [cm/s2] loggLMR Logarithm of the surface gravity,
calculated using LAMOST training set
(logg_LMR)
224-226 I3 % f_loggLMR Indicating % of features within the
training set range, with 100 for all
features within range (loggLMRFL)
228-237 F10.4 K TeffGaia ? Effective temperature of the source
from GAIA DR3 catalog (Teff_GAIA)
239-244 F6.4 [cm/s2] loggGaia ? Logarithm of the surface gravity
from GAIA DR3 catalog (logg_GAIA)
246-252 F7.4 [-] [Fe/H]Gaia ? Abundance ratio of Iron to Hydrogen
from GAIA DR3 catalog (FeH_GAIA)
254-263 F10.4 pc DistGaia ? Distance from GAIA DR3 catalog
(Dist_GAIA)
265-271 F7.4 mag umag Photometric measurement in the uJAVA
filter, corresponding to the u-band
(U)
273-279 F7.4 mag e_umag Error or uncertainty associated with
the uJAVA filter measurement (eU)
281-287 F7.4 mag J378 Photometric measurement in the J0378
filter (J378)
289-295 F7.4 mag e_J378 Error or uncertainty associated with
the J0378 filter measurement (eJ378)
297-303 F7.4 mag J395 Photometric measurement in the
J0395 filter (J395)
305-311 F7.4 mag e_J395 Error or uncertainty associated with
the J0395 filter measurement (eJ395)
313-319 F7.4 mag J410 Photometric measurement in the
J0410 filter (J410)
321-327 F7.4 mag e_J410 Error or uncertainty associated with
the J0410 filter measurement (eJ410)
329-335 F7.4 mag J430 Photometric measurement in the
J0430 filter (J430)
337-343 F7.4 mag e_J430 Error or uncertainty associated with
the J0430 filter measurement (eJ430)
345-351 F7.4 mag gmag Photometric measurement in the
gSDSS filter, corresponding to the
g-band (G)
353-359 F7.4 mag e_gmag Error or uncertainty associated with
the gSDSS filter measurement (eG)
361-366 F6.4 mag J515 Photometric measurement in the
J0515 filter (J515)
368-373 F6.4 mag e_J515 Error or uncertainty associated with
the J0515 filter measurement (eJ515)
375-380 F6.4 mag rmag Photometric measurement in the rSDSS
filter, corresponding to the r-band
(R)
382-387 F6.4 mag e_rmag Error or uncertainty associated with
the rSDSS filter measurement (eR)
389-394 F6.4 mag J660 Photometric measurement in the
J0660 filter (J660)
396-401 F6.4 mag e_J660 Error or uncertainty associated with
the J0660 filter measurement (eJ660)
403-408 F6.4 mag imag Photometric measurement in the iSDSS
filter, corresponding to the i-band
(I)
410-415 F6.4 mag e_imag Error or uncertainty associated with
the iSDSS filter measurement (eI)
417-422 F6.4 mag J861 Photometric measurement in the
J0861 filter (J861)
424-429 F6.4 mag e_J861 Error or uncertainty associated with
the J0861 filter measurement (eJ861)
431-436 F6.4 mag zmag Photometric measurement in the zSDSS
filter, corresponding to the z-band
(Z)
438-443 F6.4 mag e_zmag Error or uncertainty associated with
the zSDSS filter measurement (eZ)
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Acknowledgements:
Carlos E. Ferriera Lopes, ferreiralopes1011(at)gmail.com
(End) Patricia Vannier [CDS] 28-Nov-2024