JOURNAL ARTICLE
Stellar contents and star formation in IRAS 18456-0223.
Published In: Journal of Astrophysics & Astronomy, 2026, v. 47, n. 1. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Pandey, Nilesh; Kamath, U. S. 3 of 3
Abstract
We use various analytical techniques to study Young Stellar Objects (YSOs) in an area of approximately 10 ′ × 10 ′ in the IRAS 18456-0223 star-forming region. We use archival optical (Gaia DR3) and infrared (2MASS, UKIDSS, Spitzer, WISE, and Herschel) data and our optical spectroscopy of three bright stars for this purpose. We identify 89 YSOs (80 class II and 9 class I), based on their infrared properties. Our multiwavelength SED fits of select YSOs show that they have masses ∼ 0.1 –7.2 M ⊙ and are upto 4 Myr old. Our Minimum Spanning Tree (MST) analysis shows that these YSOs, situated at around 600 pc, form clusters with radial extent of order 0.5 pc and mean surface densities ∼ 60 pc - 2 . We compare UKIDSS and 2MASS data of the YSOs and find that some of them show variability. We constructed maps based on Herschel data which reveal multiple column density peaks ( N H 2 ∼ 10 22 cm - 2 ) embedded in cold ( T d ∼ 10 - 13 K) filaments. Our near-infrared extinction map exhibits several high- A V peaks, some of which coincide with the sub-mm column density maxima. Using our optical spectra of three bright sources, we show that they are of A-K type. One star shows the Li I 6707 Å line, indicating its youth. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Journal of Astrophysics & Astronomy. 2026/04, Vol. 47, Issue 1, p1
- Document Type:Article
- Subject Area:Astronomy and Astrophysics
- Publication Date:2026
- ISSN:0250-6335
- DOI:10.1007/s12036-026-10148-1
- Accession Number:192962934
- Copyright Statement:Copyright of Journal of Astrophysics & Astronomy is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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