J/AJ/167/12 Open cluster parameters with ANN (Cavallo+, 2024)
Parameter Estimation for Open Clusters using an Artificial Neural Network with a
QuadTree-based Feature Extractor.
Cavallo L., Spina L., Carraro G., Magrini L., Poggio E., Cantat-Gaudin T.,
Pasquato M., Lucatello S., Ortolani S., Schiappacasse-Ulloa J.
<Astron. J. 167, 12 (2024)>
=2024AJ....167...12C 2024AJ....167...12C (SIMBAD/NED BibCode)
ADC_Keywords: Milky Way ; Clusters, open ; Positional data ; Models
Keywords: open star clusters - Gaia - neural networks - Milky Way disk -
Fundamental parameters of stars
Abstract:
With the unprecedented increase of known star clusters, quick and
modern tools are needed for their analysis. In this work, we develop
an artificial neural network trained on synthetic clusters to estimate
the age, metallicity, extinction, and distance of Gaia open clusters.
We implement a novel technique to extract features from the
colour-magnitude diagram of clusters by means of the QuadTree tool and
we adopt a multi-band approach. We obtain reliable parameters for
∼5400 clusters. We demonstrate the effectiveness of our methodology in
accurately determining crucial parameters of Gaia open clusters by
performing a comprehensive scientific validation. In particular, with
our analysis we have been able to reproduce the Galactic metallicity
gradient as it is observed by high-resolution spectroscopic surveys.
This demonstrates that our method reliably extracts information on
metallicity from colour-magnitude diagrams (CMDs) of stellar clusters.
For the sample of clusters studied, we find an intriguing systematic
older age compared to previous analyses present in the literature.
This work introduces a novel approach to feature extraction using a
QuadTree algorithm, effectively tracing sequences in CMDs despite
photometric errors and outliers. The adoption of ANNs, rather than
Convolutional Neural Networks, maintains the full positional
information and improves performance, while also demonstrating the
potential for deriving clusters' parameters from simultaneous analysis
of multiple photometric bands, beneficial for upcoming telescopes like
the Vera Rubin Observatory. The implementation of ANN tools with
robust isochrone fit techniques could provide further improvements in
the quest for open clusters' parameters.
Description:
We present one table containing the catalogue of clusters analysed by
our ANNs.
File Summary:
--------------------------------------------------------------------------------
FileName Lrecl Records Explanations
--------------------------------------------------------------------------------
ReadMe 80 . This file
catalog.dat 226 6413 Clusters analysed by our ANNs
(update version, 18/10/2024)
--------------------------------------------------------------------------------
See also:
J/A+A/673/A114 : Improving the open cluster census. II. (Hunt+, 2023)
Byte-by-byte Description of file: catalog.dat
--------------------------------------------------------------------------------
Bytes Format Units Label Explanations
--------------------------------------------------------------------------------
1- 20 A20 --- Cluster Cluster name
22- 41 F20.16 deg RAdeg Mean right ascension (ICRS) at Ep=2016.0
of cluster's members
43- 62 F20.16 deg DEdeg Mean declination (ICRS) at Ep=2016.0)
of cluster's members
64 A1 --- kind [omg] Type of object from Hunt & Reffert
(2023A&A...673A.114H 2023A&A...673A.114H, Cat. J/A+A/673/A114) (1)
66- 71 F6.3 mas plx Mean parallax of cluster's members
73- 77 F5.3 mas e_plx Error on the parallax
79- 82 F4.2 --- CMDclass [0/1] 50th percentile of CMD class from
Hunt & Reffert (2023A&A...673A.114H 2023A&A...673A.114H,
Cat. J/A+A/673/A114)
84 I1 --- Quality [0/3] Quality of the predictions (2)
86- 89 F4.2 [yr] logAge16 16th percentile of logAge predictions
of the ANNs
91- 95 F5.2 [yr] logAge50 50th percentile of logAge predictions
of the ANNs
97-101 F5.2 [yr] logAge84 84th percentile of logAge predictions
of the ANNs
103-107 F5.2 [-] [Fe/H]16 16th percentile of [Fe/H] predictions
of the ANNs
109-113 F5.2 [-] [Fe/H]50 50th percentile of [Fe/H] predictions
of the ANNs
115-119 F5.2 [-] [Fe/H]84 84th percentile of [Fe/H] predictions
of the ANNs
121-124 F4.2 mag AV16 16th percentile of V-band extinction
predictions of the ANNs
126-129 F4.2 mag AV50 50th percentile of V-band extinction
predictions of the ANNs
131-134 F4.2 mag AV84 84th percentile of V-band extinction
predictions of the ANNs
136-140 F5.2 mag dMod16 16th percentile of distance module
predictions of the ANNs
142-146 F5.2 mag dMod50 50th percentile of distance module
predictions of the ANNs
148-152 F5.2 mag dMod84 84th percentile of distance module
predictions of the ANNs
154-173 F20.16 kpc X X coordinate in galactocentric galactic
coordinates (as predicted by the ANNs)
175-196 E22.15 kpc Y Y coordinate in galactocentric galactic
coordinates (as predicted by the ANNs)
198-219 E22.15 kpc Z Z coordinate in galactocentric galactic
coordinates (as predicted by the ANNs)
221-226 F6.3 kpc Rgc Distance from Galactic centre (as predicted by
the ANNs and assuming Sun Rgc=8.122kpc)
--------------------------------------------------------------------------------
Note (1): Type of object as follows:
o = open cluster
m = moving group
g = globular cluster
Note (2): Quality of the predictions as follows:
0 = gold sample
1 = silver sample
2 = bronze sample
3 = wood sample
--------------------------------------------------------------------------------
Acknowledgements:
Lorenzo Cavallo, lorenzo.cavallo(at)phd.unipd
History:
07-Dec-2023: on-line version
18-Oct-2024: updated version
(End) Patricia Vannier [CDS] 03-Nov-2023