We select a volume-limited sample of galaxies derived from the SDSS DR7 to study the environment of low surface brightness (LSB) galaxies at different scales, as well as several physical properties of the dark matter haloes where the LSB galaxies of the sample are embedded. To characterize the environment, we make use of a number of publicly available value-added galaxy catalogues. We find a slight preference for LSB galaxies to be found in filaments instead of clusters, with their mean distance to the nearest filament typically larger than for high surface brightness (HSB) galaxies. The fraction of isolated central LSB galaxies is higher than the same fraction for HSB ones, and the density of their local environment lower. The stellar-to-halo mass ratio using four different estimates is up to ∼20 per cent for HSB galaxies. LSB central galaxies present more recent assembly times when compared with their HSB counterparts. Regarding the λ spin parameter, using six different proxies for its estimation, we find that LSB galaxies present systematically larger values of λ than the HSB galaxy sample, and constructing a control sample with direct kinematic information drawn from ALFALFA, we confirm that the spin parameter of LSB galaxies is 1.6–2 times larger than the one estimated for their HSB counterparts.
We present a machine learning (ML) approach for the prediction of galaxies’ dark matter halo masses which achieves an improved performance over conventional methods. We train three ML algorithms (XGBoost, random forests, and neural network) to predict halo masses using a set of synthetic galaxy catalogues that are built by populating dark matter haloes in N-body simulations with galaxies and that match both the clustering and the joint distributions of properties of galaxies in the Sloan Digital Sky Survey (SDSS). We explore the correlation of different galaxy- and group-related properties with halo mass, and extract the set of nine features that contribute the most to the prediction of halo mass. We find that mass predictions from the ML algorithms are more accurate than those from halo abundance matching (HAM) or dynamical mass estimates (DYN). Since the danger of this approach is that our training data might not accurately represent the real Universe, we explore the effect of testing the model on synthetic catalogues built with different assumptions than the ones used in the training phase. We test a variety of models with different ways of populating dark matter haloes, such as adding velocity bias for satellite galaxies. We determine that, though training and testing on different data can lead to systematic errors in predicted masses, the ML approach still yields substantially better masses than either HAM or DYN. Finally, we apply the trained model to a galaxy and group catalogue from the SDSS DR7 and present the resulting halo masses.
more » « less- NSF-PAR ID:
- 10121821
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Monthly Notices of the Royal Astronomical Society
- Volume:
- 490
- Issue:
- 2
- ISSN:
- 0035-8711
- Page Range / eLocation ID:
- p. 2367-2379
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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