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Chutipong Suwannajak. Detection of stellar substructures in the m82 halo using density-based spatial clustering (dbscan). (). King Mongkut's University of Technology North Bangkok. Central Library. : , 2026.
Detection of stellar substructures in the m82 halo using density-based spatial clustering (dbscan)
Name: Wachira Norasing
Organization :
Chiang Mai University. Department of Computer Science
Email :
wachira_n@cmu.ac.th
Organization :
Chiang Mai University. Department of Computer Science
Email :
areerat.t@cmu.ac.th
Name: Jakarin Chawachat
Organization :
Chiang Mai University. Department of Computer Science
Email :
jakarin.c@cmu.ac.th
Name: Chutipong Suwannajak
Organization :
National Astronomical Research Institute of Thailand (Public Organization).
Email :
chutipong@narit.or.th
keyword:
Hubble Space Telescope
LCSH:
Galaxies
LCSH:
Cluster analysis
-- Data processing
Abstract:
The gravitational interaction between M81 and M82 has generated a complex web of tidal debris, including potential Tidal Dwarf Galaxies (TDGs) in the M82 halo. Identifying these faint, diffuse substructures is observational challenging. In this study, we present an automated detection pipeline using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm applied to high-resolution Hubble space telescope (HST) Advanced Camera for Surveys (ACS) photometry from the ACS Nearby Galaxy Survey Treasury (ANGST). We evaluate the performance of two clustering strategies: a 2D spatial method using the Haversine metric and a 3D physio-spatial method incorporating stellar color. Our analysis demonstrates that the 2D spatial clustering (eps = 2.0 arcsec) is the optimal strategy for recovering the diffuse morphology of a tidal dwarf, successfully retrieving the known candidate DF-E1 with a spatial extent of approximately 600 parsecs. In contrast, the 3D approach leads to the fragmentation of extended systems due to internal color gradients. Cross-verification with the star cluster catalog reveals that clusters detected by our algorithm may spatially coincide with known star clusters, although they are morphological distinct. We conclude that physical size serves as a robust discriminator, establishing DBSCAN as a powerful tool for systematic searches of low-surface-brightness substructures in a nearby galaxy.
King Mongkut's University of Technology North Bangkok. Central Library
Address:
BANGKOK
Email:
library@kmutnb.ac.th
Created:
2026
Modified:
2026-08-05
Issued:
2026-08-05
บทความ/Article
application/pdf
BibliograpyCitation :
In ECTI Association and Burapha University. Faculty of Engineering. 2026 23rd International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON 2026) (pp.163-168). Piscataway, NJ : IEEE
eng
©copyrights King Mongkut's University of Technology North Bangkok
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ใช้เวลา
0.028379 วินาที
Wachira Norasing
| Title | Contributor | Type |
|---|---|---|
| Detection of stellar substructures in the m82 halo using density-based spatial clustering (dbscan)
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Wachira Norasing;Areerat Trongratsameethong;Jakarin Chawachat;Chutipong Suwannajak | บทความ/Article |
Areerat Trongratsameethong
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| Detection of stellar substructures in the m82 halo using density-based spatial clustering (dbscan)
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Wachira Norasing;Areerat Trongratsameethong;Jakarin Chawachat;Chutipong Suwannajak | บทความ/Article |
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| Detection of stellar substructures in the m82 halo using density-based spatial clustering (dbscan)
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Wachira Norasing;Areerat Trongratsameethong;Jakarin Chawachat;Chutipong Suwannajak | บทความ/Article |
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| Detection of stellar substructures in the m82 halo using density-based spatial clustering (dbscan)
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Wachira Norasing;Areerat Trongratsameethong;Jakarin Chawachat;Chutipong Suwannajak | บทความ/Article |