J/MNRAS/505/1607  Tidal tails in the open cluster NGC 752  (Bhattacharya+, 2021)

Tidal tails in the disintegrating open cluster NGC 752. Bhattacharya S., Agarwal M., Rao K.K., Vaidya K. <Mon. Not. R. Astron. Soc., 505, 1607-1613 (2021)> =2021MNRAS.505.1607B 2021MNRAS.505.1607B (SIMBAD/NED BibCode)
ADC_Keywords: Clusters, open ; Milky Way ; Parallaxes, trigonometric ; Photometry, G band ; Optical ; Infrared ; Ultraviolet ; Proper motions ; Radial velocities Keywords: methods: data analysis - open clusters and associations: individual: NGC 752 Abstract: We utilize the robust membership determination algorithm, ML-MOC, on the precise astrometric and deep photometric data from Gaia Early Data Release 3 within a region of radius 5 degrees around the centre of the intermediate-age Galactic open cluster NGC 752 to identify its member stars. We report the discovery of the tidal tails of NGC 752, extending out to ∼35 pc on either side of its denser central region and following the cluster orbit. From comparison with PARSEC stellar isochrones, we obtain the mass function of the cluster with a slope, Χ = -1.26 ± 0.07. The high negative value of Χ is indicative of a disintegrating cluster undergoing mass segregation. Χ is more negative in the intra-tidal regions as compared to the outskirts of NGC 752. We estimate a present day mass of the cluster, MC = 297 ± 10 M. Through mass-loss due to stellar evolution and tidal interactions, we further estimate that NGC 752 has lost nearly 95.2-98.5 per cent of its initial mass, Mi = 0.64-2 * 104M. Description: Agarwal et al. (2021MNRAS.502.2582A 2021MNRAS.502.2582A) using Gaia DR2 (2018A&A...616A...1G 2018A&A...616A...1G, Cat. I/345) data with their novel machine learning membership determination algorithm found hints of tidal tails in NGC 752. Also, Hu et al. (2021ApJ...912....5H 2021ApJ...912....5H, Cat. J/ApJ/912/5) also identify the elongated morphology of the peripheral regions of NGC 752 from the Gaia DR2 data using the members identified by Cantat-Gaudin et al. (2018A&A...618A..93C 2018A&A...618A..93C, Cat. J/A+A/618/A93). As the first step, we do a membership selection in NGC 752 from the Gaia EDR3 (2021A&A...649A...1G 2021A&A...649A...1G, Cat. I/350) data set is carried out in a region of radius 5 degrees around its centre. This sample is termed All sources. We use ML-MOC (Agarwal et al. 2021MNRAS.502.2582A 2021MNRAS.502.2582A) to identify cluster members using the proper motion and parallax information. It identifies cluster members in the PM-ω parameter space, independent of the spatial density of the cluster, thereby allowing for the identification of faint extended spatial structures such as tidal tails.Their spatial, PM, and ω distributions are shown as Sample sample (see Section 2 Fig. 1). For the second step, a three-dimensional Gaussian mixture model (GMM; Mclachlan & Peel 2000, Wiley Series in Probability and Statistics, 2000, Finite Mixture Models, Vol. 44, Wiley, New York) is used in the PM-ω parameter space of the Sample sources sample to distinguish between the cluster and field members, also assigning a membership probability (pmemb). Those sources having pmemb ≥ 0.6 are considered as high probability members, also including all the radial velocity members. A small number of stars having 0.2 ≤ pmemb ≤ 0.6 are also considered as members if their values lie within the range of values specified by the members that have pmemb ≥ 0.8. This results in the identification of 282 members in NGC 752 whose spatial, PM, and ω distributions are also shown (see Section 2 Fig. 1). The identified members with their IDs, astrometry, photometry, radial velocity, and pmemb have been tabulated in table1.dat. File Summary: -------------------------------------------------------------------------------- FileName Lrecl Records Explanations -------------------------------------------------------------------------------- ReadMe 80 . This file table1.dat 107 282 *Gaia EDR3 members identified in NGC 752 using ML-MOC -------------------------------------------------------------------------------- Note on table1.dat: ML-MOC means Machine Learning based Membership determination for Open Clusters. Except pmemb values computed in this work, all data come from Gaia EDR3 (2021A&A...649A...1G 2021A&A...649A...1G, Cat. I/350). -------------------------------------------------------------------------------- See also: I/345 : Gaia DR2 (Gaia Collaboration, 2018) I/350 : Gaia EDR3 (Gaia Collaboration, 2020) J/ApJ/912/5 : Parameters of 265 open clusters (Hu+, 2021) J/A+A/618/A93 : Gaia DR2 open clusters in the Milky Way (Cantat-Gaudin+, 2018) J/ApJ/862/33 : Improved & expanded membership catalog for NGC752 (Agueros+, 2018) Byte-by-byte Description of file: table1.dat -------------------------------------------------------------------------------- Bytes Format Units Label Explanations -------------------------------------------------------------------------------- 1- 18 I18 --- GaiaEDR3 Gaia EDR3 source identifier (GaiaID) 20- 37 F18.15 deg RAdeg Barycentric right ascension of the source (ICRS) at Ep=2016.0 (RA) 39- 56 F18.15 deg DEdeg Barycentric declination of the source (ICRS) at Ep=2016.0 (Dec) 58- 62 F5.3 mas plx Absolute stellar parallax of the source at the Ep=2016.0 (omega) 64- 69 F6.3 mas/yr pmRA Proper motion in right ascension pmRA*cosDE of the source in ICRS at Ep=2016.0 (mualpha) 71- 77 F7.3 mas/yr pmDE Proper motion in declination direction (mudelta) 79- 83 F5.2 mag Gmag G-band mean magnitude (Vega) (G) 85- 89 F5.2 mag BPmag Integrated BP mean magnitude (Vega) (BP) 91- 95 F5.2 mag RPmag Integrated RP mean magnitude (Vega) (RP) 97-102 F6.2 km/s RV ? Radial velocity from Gaia EDR3 copied from Gaia DR2 (RV) 104-107 F4.2 --- pmemb Computed NGC 752 membership probability (pmemb) -------------------------------------------------------------------------------- History: From electronic version of the journal
(End) Luc Trabelsi [CDS] 28-May-2024
The document above follows the rules of the Standard Description for Astronomical Catalogues; from this documentation it is possible to generate f77 program to load files into arrays or line by line