Subaru Hyper-Supreme Cam observations of IC 1396

Source catalogue, member population, and sub-clusters of the complex

¹ Departamento de Astronomia, Universidad de Chile, Las Condes, 7591245 Santiago, Chile
² Department of Physics, Indian Institute of Science Education and Research Tirupati, Yerpedu, Tirupati 517619, Andhra Pradesh, India
³ Astronomy Unit, School of Physical and Chemical Sciences, Queen Mary University of London, Mile end Road, London E1 4NS, UK
⁴ Physical Research Laboratory (PRL), Navrangpura, Ahmedabad 380 009, Gujarat, India
⁵ Kavli Institute for Astronomy and Astrophysics, Peking University, Yi He Yuan Lu 5, Haidian Qu, Beijing 100871, China
⁶ Instituto de Física y Astronomía, Universidad de Valparaíso, ave. Gran Bretaña, 1111, Casilla 5030, Valparaíso, Chile
⁷ Millennium Institute of Astrophysics, Nuncio Monseñor Sotero Sanz 100, Of. 104, Providencia, Santiago, Chile
⁸ Centre for Astrophysics Research, University of Hertfordshire, Hatfield AL10 9AB, UK
⁹ Inter University Centre for Astronomy and Astrophysics, Ganeshkhind, Pune 411007, India
¹⁰ Indian Institute of Technology Hyderabad, Kandi, Sangareddy, Telangana, India

^★ Corresponding authors: swagat@das.uchile.cl; dasswagat77@gmail.com; jessyvjose1@gmail.com

Received: 17 April 2024
Accepted: 19 March 2025

Abstract

Context. Identifying members of star-forming regions is an initial step to analyse the properties of a molecular cloud complex. In such a membership analysis, the sensitivity of a dataset plays a significant role in detecting stellar mass up to a specific limit, which is crucial for understanding various stellar properties, such as disc evolution and planet formation across different environments.

Aims. IC 1396 is a nearby classical H II region dominated by feedback-driven star formation activity. In this work, we aim to identify the low-mass member populations of the complex using deep optical multi-band imaging with Subaru-Hyper Suprime Cam (HSC) over ∼7.1 deg² in r₂, i₂, and Y bands. The optical dataset is complemented by UKIDSS near-infrared data in the J, H, and K bands. Through this work, we evaluate the strengths and limitations of machine learning techniques when applied to such astronomical datasets.

Methods. To identify member populations of IC 1396, we employed the random forest (RF) classifier of machine learning technique. The RF 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, in which the member stars are younger than ∼10 Myr. However, its sensitivity is limited to ∼20 mag in the r₂ band, making it challenging to identify candidates at the fainter end. In this analysis, in addition to magnitudes and colours, we incorporated 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 were able to identify promising candidate member populations of the star-forming complex. We discuss the associated limitations and caveats in the method and improvements for future studies.

Results. In this analysis, we identify 2425 high-probability low-mass stars distributed within the entire star-forming complex, of which 1331 are new detections. A 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, H_α/X – ray emissions, optical photometry, and 2MASS photometry. The mean age of the member populations is ∼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.

Conclusions. The primary aim of this work is to develop a method of identifying candidate member populations from a deep, sensitive dataset such as Subaru-HSC by employing machine learning techniques. Although we overcome some limitations in this study, the method requires further improvements to address the caveats associated with such a membership analysis.