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IdentYS: A Python-Based Tool for Identifying Young Stars in Star-Forming Regions

E. Nikoghosyan, D. Baghdasaryan, D. Andreasyan, N. Azatyan, A. Samsonyan, A. Yeghikyan

TL;DR

The paper tackles the challenge of identifying young stellar objects (YSOs) in distant, embedded star-forming regions amid substantial field contamination. It introduces IdentYS, a Python-based pipeline that fuses near-IR and mid-IR photometry from UKIDSS GPS, VVV/VIRAC, 2MASS, Spitzer GLIMPSE/MIPSGAL, and ALLWISE, and classifies YSOs primarily as Class I or II using multiple color-color and color-magnitude diagnostics. A key contribution is the two-diagram IR-excess criterion, which markedly improves sample purity while enabling large-scale statistical studies across diverse regions. The results demonstrate robust YSO identification in a complex region (GRSMC 045.49+00.04), with detailed analyses of diagrammatic behavior, CMD positioning, and SED slopes, highlighting the method's practical utility for surveys of embedded populations.

Abstract

Research on young stellar populations is essential to understand the properties of embedded clusters and advance theories of their formation. This has driven advancements in methodologies for star detection, leading to the development of valuable databases and software. We present the scientific justification and operating principles of the IdentYS tool, which is designed to identify young stellar objects (YSOs) in star-forming regions. The tool facilitates the identification of young stars with infrared (IR) excess in remote and embedded star-forming regions, focusing primarily on Class I and II YSOs. For this purpose, near- and mid-IR photometric data and five colour-colour diagrams (J - H) vs. (H - K), K - [3.6] vs. [3.6] - [4.5], [3.6] - [4.5] vs. [5.8] - [8.0], [3.6] - [4.5] vs. [8.0] - [24], and [3.4] - [4.6] vs. [4.6] - [12] are used. The purity of the YSOs sample is enhanced by excluding field contamination from stellar and extragalactic objects. As a result, we compile a list of YSO candidates displaying the source designation, astrometric, and photometric parameters, as well as information on the evolutionary stage determined by the presence of IR excess, as indicated by certain diagrams. The application of this program can greatly streamline the statistical analysis of young stellar populations across diverse star-forming regions, including distant and deeply embedded ones, which typically require processing large volumes of initial data.

IdentYS: A Python-Based Tool for Identifying Young Stars in Star-Forming Regions

TL;DR

The paper tackles the challenge of identifying young stellar objects (YSOs) in distant, embedded star-forming regions amid substantial field contamination. It introduces IdentYS, a Python-based pipeline that fuses near-IR and mid-IR photometry from UKIDSS GPS, VVV/VIRAC, 2MASS, Spitzer GLIMPSE/MIPSGAL, and ALLWISE, and classifies YSOs primarily as Class I or II using multiple color-color and color-magnitude diagnostics. A key contribution is the two-diagram IR-excess criterion, which markedly improves sample purity while enabling large-scale statistical studies across diverse regions. The results demonstrate robust YSO identification in a complex region (GRSMC 045.49+00.04), with detailed analyses of diagrammatic behavior, CMD positioning, and SED slopes, highlighting the method's practical utility for surveys of embedded populations.

Abstract

Research on young stellar populations is essential to understand the properties of embedded clusters and advance theories of their formation. This has driven advancements in methodologies for star detection, leading to the development of valuable databases and software. We present the scientific justification and operating principles of the IdentYS tool, which is designed to identify young stellar objects (YSOs) in star-forming regions. The tool facilitates the identification of young stars with infrared (IR) excess in remote and embedded star-forming regions, focusing primarily on Class I and II YSOs. For this purpose, near- and mid-IR photometric data and five colour-colour diagrams (J - H) vs. (H - K), K - [3.6] vs. [3.6] - [4.5], [3.6] - [4.5] vs. [5.8] - [8.0], [3.6] - [4.5] vs. [8.0] - [24], and [3.4] - [4.6] vs. [4.6] - [12] are used. The purity of the YSOs sample is enhanced by excluding field contamination from stellar and extragalactic objects. As a result, we compile a list of YSO candidates displaying the source designation, astrometric, and photometric parameters, as well as information on the evolutionary stage determined by the presence of IR excess, as indicated by certain diagrams. The application of this program can greatly streamline the statistical analysis of young stellar populations across diverse star-forming regions, including distant and deeply embedded ones, which typically require processing large volumes of initial data.

Paper Structure

This paper contains 12 sections, 5 figures, 1 table.

Figures (5)

  • Figure 1: Colour–colour diagrams of YSO candidates within the GRSMC 045.49+00.04 molecular cloud. Top left panel: (J–H) vs. (H–K) diagram, with black curves showing the loci of dwarfs and giants from Bessell1988 and converted to the 2MASS photometric system Carpenter2001. The parallel lines represent the interstellar reddening vectors Rieke1985. The remaining panels are: top right - K–[3.6] vs. [3.6]–[4.5]; middle left - [3.6]–[4.5] vs. [5.8]–[8.0]; and middle right - [3.6]–[5.8] vs. [8.0]–[24], where the Class I and Class II YSO domains are adopted from Allen2007Megeath2004Muzerolle2004. The bottom panels present W1–W2 vs. W2–W3 diagrams, constructed according to the criteria of Koenig2012 (left) and Koenig2014 (right). The reddening vectors for Spitzer and WISE bands are obtained from Flaherty2007 and Xue2016, respectively. Class I YSOs are indicated by red circles, Class I/II by orange, and Class II by blue. Circle sizes vary to enhance visualization. In the bottom right panel, dark gray circles indicate YSO candidates identified by Koenig2012 and supported by additional c–c diagrams.
  • Figure 2: Schematic representation of the IdentYS code
  • Figure 3: Herschel 70 $\mu$m image of the GRSMC 045.49+00.04 molecular cloud. The dashed white circle highlights an area with a 14$^{\prime}$ centered around the coordinates 19:14:32.0, +11:10:55.0, delineating the primary study region.
  • Figure 4: Colour–magnitude diagrams of YSO candidates within the GRSMC 045.49+00.04 molecular cloud: 2MASS (top panel), Spitzer (middle panel), and WISE (bottom panel). The black curve represents the 10$^7$ years isochrone adopted from the PARSEC database. The symbols denoting YSOs of different evolutionary classes are the same as in Fig. \ref{['fig:c-c']}.
  • Figure 5: Distribution of spectral index $\alpha$. Left panel: YSO candidates, for which an IR excess was detected (beije) and not detected (green) in the MIR1 ([3.6] - [4.5] vs. [5.8] - [8.0]) diagram. Right panel: stellar objects with IR excess detected in only one diagram (dark grey) and those with no IR excess detected (light grey).