Kavli Affiliate: Gregory J. Herczeg
| First 5 Authors: Swagat R Das, Saumya Gupta, Jessy Jose, Manash Samal, Gregory J. Herczeg
| Summary:
To identify member populations of IC 1396, we employ the random forest (RF)
classifier of machine learning technique. Random forest classifier is an
ensemble of individual decision trees suitable for large, high-dimensional
datasets. The training set used in this work is derived from previous
Gaia-based studies, where the member stars are younger than $sim$ 10~Myr.
However, its sensitivity is limited to $sim$ 20~mag in the $rm r_2$ band,
making it challenging to identify candidates at the fainter end. In this
analysis, in addition to magnitudes and colours, we incorporate several derived
parameters from the magnitude and colour of the sources to identify candidate
members of the star-forming complex. By employing this method, we are able to
identify promising candidate member populations of the star-forming complex. We
discuss the associated limitations and caveats in the method and for improvment
in future studies.
In this analysis, we identify 2425 high-probability low-mass stars
distributed within the entire star-forming complex, of which 1331 are new
detections. Comparison of these identified member populations shows a high
retrieval rate with Gaia-based literature sources, as well as sources detected
through methods based on optical spectroscopy, Spitzer, $rm H_{alpha}/X-ray$
emissions, optical, and 2MASS photometry. The mean age of the member
populations is $rm sim 2-4~Myr$, consistent with findings from previous
studies. Considering the identified member populations, we present preliminary
results by exploring the presence of sub-clusters within IC 1396, assessing the
possible mass limit of the member populations, and providing a brief discussion
on the star formation history of the complex.
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