Issue |
A&A
Volume 647, March 2021
|
|
---|---|---|
Article Number | A14 | |
Number of page(s) | 23 | |
Section | Numerical methods and codes | |
DOI | https://doi.org/10.1051/0004-6361/202038123 | |
Published online | 26 February 2021 |
S2D2: Small-scale Significant substructure DBSCAN Detection
I. NESTs detection in 2D star-forming regions
1
Univ. Grenoble Alpes, CNRS, IPAG, 38000 Grenoble, France
e-mail: marta.gonzalez-garcia@univ-grenoble-alpes.fr
2
University of Exeter, Stocker Road, Exeter EX4 4PY, UK
3
School of Physics and Astronomy, Cardiff University, The Parade CF24 3AA, UK
4
School of Physics and Astronomy, University of Leeds, Leeds LS2 9JT, UK
5
Quasar Science Resources, S.L. Camino de las Ceudas 2, 28232 Las Rozas de Madrid, Madrid, Spain
6
Leiden Observatory, Niels Bohrweg 2, 2333 CA Leiden, The Netherlands
7
Universidade de Vigo, Campus Universitario Lagoas-Marcosende, 36210 Vigo, Spain
Received:
8
April
2020
Accepted:
17
December
2020
Context. The spatial and dynamical structure of star-forming regions can offer insights into stellar formation patterns. The amount of data from current and upcoming surveys calls for robust and objective procedures for detecting structures in order to statistically analyse the various regions and compare them.
Aims. We aim to provide the community with a tool capable of detecting, above random expectations, the small-scale significant structure in star-forming regions that could serve as an imprint of the stellar formation process. The tool makes use of the one-point correlation function to determine an appropriate length scale for ϵ and uses nearest-neighbour statistics to determine a minimum number of points Nmin for the DBSCAN algorithm in the neighbourhood of ϵ.
Methods. We implemented the procedure and applied it to synthetic star-forming regions of different nature and characteristics to obtain its applicability range. We also applied the method to observed star-forming regions to demonstrate its performance in realistic circumstances and to analyse its results.
Results. The procedure successfully detects significant small-scale substructures in heterogeneous regions, fulfilling the goals it was designed for and providing very reliable structures. The analysis of regions close to complete spatial randomness (Q ∈ [0.7, 0.87]) shows that even when some structure is present and recovered, it is hardly distinguishable from spurious detection in homogeneous regions due to projection effects. Thus, any interpretation should be done with care. For concentrated regions, we detect a main structure surrounded by smaller ones, corresponding to the core plus some Poisson fluctuations around it. We argue that these structures do not correspond to the small compact regions we are looking for. In some realistic cases, a more complete hierarchical, multi-scale analysis would be needed to capture the complexity of the region.
Conclusions. We carried out implementations of our procedure and devised a catalogue of the Nested Elementary STructures (NESTs) detected as a result in four star-forming regions (Taurus, IC 348, Upper Scorpius, and Carina). This catalogue is being made publicly available to the community. Implementations of the 3D versionsof the procedure, as well as up to 6D versions, including proper movements, are in progress and will be provided in a future work.
Key words: methods: data analysis / methods: statistical / Galaxy: structure / galaxies: star formation
© M. González et al. 2021
Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.