The stellar host in star-forming low-mass galaxies: Evidence for two classes

The morphological evolution of star-forming galaxies provides important clues to understand their physical properties, as well as the triggering and quenching mechanisms of star formation. We aim at connecting morphology and star-formation properties of low-mass galaxies (median stellar mass $\sim$ 10$^{8.5}$ M$_{\odot}$) at low redshift ($z<0.36$). We use a sample of medium-band selected star-forming galaxies from the GOODS-North field. H$\alpha$ images for the sample are created combining both spectral energy distribution fits and HST data. Using them, we mask the star forming regions to obtain an unbiased two-dimensional model of the light distribution of the host galaxies. For this purpose we use $\texttt{PHI}$, a new Bayesian photometric decomposition code. We apply it independently to 7 HST bands assuming a S\'ersic surface brightness model. Star-forming galaxy hosts show low S\'ersic index (with median $n$ $\sim$ 0.9), as well as small sizes (median $R_e$ $\sim$ 1.6 kpc), and negligible change of the parameters with wavelength (except for the axis ratio, which grows with wavelength). Using a clustering algorithm, we find two different classes of star-forming galaxies: A more compact, redder, and high-$n$ (class A) and a more extended, bluer and lower-$n$ one (class B). We also find evidence that the first class is more spheroidal-like. In addition, we find that 48% of the analyzed galaxies present negative color gradients (only 5% are positive). The host component of low-mass star-forming galaxies at $z<0.36$ separates into two different classes, similar to what has been found for their higher mass counterparts. The results are consistent with an evolution from class B to class A. Several mechanisms from the literature, like minor and major mergers, and violent disk instability, can explain the physical process behind the likely transition between the classes. [abridged]Comment: Accepted for publication in Astronomy & Astrophysics. 13 pages, 11 figure

Mining the UKIDSS GPS: star formation and embedded clusters

Data mining techniques must be developed and applied to analyse the large public data bases containing hundreds to thousands of millions entries. The aim of this study is to develop methods for locating previously unknown stellar clusters from the UKIDSS Galactic Plane Survey catalogue data. The cluster candidates are computationally searched from pre-filtered catalogue data using a method that fits a mixture model of Gaussian densities and background noise using the Expectation Maximization algorithm. The catalogue data contains a significant number of false sources clustered around bright stars. A large fraction of these artefacts were automatically filtered out before or during the cluster search. The UKIDSS data reduction pipeline tends to classify marginally resolved stellar pairs and objects seen against variable surface brightness as extended objects (or "galaxies" in the archive parlance). 10% or 66 x 10^6 of the sources in the UKIDSS GPS catalogue brighter than 17 magnitudes in the K band are classified as "galaxies". Young embedded clusters create variable NIR surface brightness because the gas/dust clouds in which they were formed scatters the light from the cluster members. Such clusters appear therefore as clusters of "galaxies" in the catalogue and can be found using only a subset of the catalogue data. The detected "galaxy clusters" were finally screened visually to eliminate the remaining false detections due to data artefacts. Besides the embedded clusters the search also located locations of non clustered embedded star formation. The search covered an area of 1302 square degrees and 137 previously unknown cluster candidates and 30 previously unknown sites of star formation were found

Multivariate Approaches to Classification in Extragalactic Astronomy

Clustering objects into synthetic groups is a natural activity of any science. Astrophysics is not an exception and is now facing a deluge of data. For galaxies, the one-century old Hubble classification and the Hubble tuning fork are still largely in use, together with numerous mono-or bivariate classifications most often made by eye. However, a classification must be driven by the data, and sophisticated multivariate statistical tools are used more and more often. In this paper we review these different approaches in order to situate them in the general context of unsupervised and supervised learning. We insist on the astrophysical outcomes of these studies to show that multivariate analyses provide an obvious path toward a renewal of our classification of galaxies and are invaluable tools to investigate the physics and evolution of galaxies.Comment: Open Access paper. http://www.frontiersin.org/milky\_way\_and\_galaxies/10.3389/fspas.2015.00003/abstract\&gt;. \&lt;10.3389/fspas.2015.00003 \&g

Constant Approximation for kk-Median and kk-Means with Outliers via Iterative Rounding

In this paper, we present a new iterative rounding framework for many clustering problems. Using this, we obtain an $(\alpha_1 + \epsilon \leq 7.081 + \epsilon)$-approximation algorithm for $k$-median with outliers, greatly improving upon the large implicit constant approximation ratio of Chen [Chen, SODA 2018]. For $k$-means with outliers, we give an $(\alpha_2+\epsilon \leq 53.002 + \epsilon)$-approximation, which is the first $O(1)$-approximation for this problem. The iterative algorithm framework is very versatile; we show how it can be used to give $\alpha_1$- and $(\alpha_1 + \epsilon)$-approximation algorithms for matroid and knapsack median problems respectively, improving upon the previous best approximations ratios of $8$ [Swamy, ACM Trans. Algorithms] and $17.46$ [Byrka et al, ESA 2015]. The natural LP relaxation for the $k$-median/$k$-means with outliers problem has an unbounded integrality gap. In spite of this negative result, our iterative rounding framework shows that we can round an LP solution to an almost-integral solution of small cost, in which we have at most two fractionally open facilities. Thus, the LP integrality gap arises due to the gap between almost-integral and fully-integral solutions. Then, using a pre-processing procedure, we show how to convert an almost-integral solution to a fully-integral solution losing only a constant-factor in the approximation ratio. By further using a sparsification technique, the additive factor loss incurred by the conversion can be reduced to any $\epsilon > 0$

The Phoenix Deep Survey: Extremely Red Galaxies and Cluster Candidates

We present the results of a study of a sample of 375 Extremely Red Galaxies (ERGs) in the Phoenix Deep Survey, 273 of which constitute a subsample which is 80% complete to K_s = 18.5 over an area of 1160 arcmin^2. The angular correlation function for ERGs is estimated, and the association of ERGs with faint radio sources explored. We find tentative evidence that ERGs and faint radio sources are associated at z > 0.5. A new overdensity-mapping algorithm has been used to characterize the ERG distribution, and identify a number of cluster candidates, including a likely cluster containing ERGs at 0.5 < z < 1. Our algorithm is also used in an attempt to probe the environments in which faint radio sources and ERGs are associated. We find limited evidence that the I - K_s > 4 criterion is more efficient than R - K_s > 5 at selecting dusty star-forming galaxies, rather than passively evolving ERGs.Comment: 14 emulateapj pages, 15 figures, 1 table, accepted for publication in Astronomical Journal. A version with full resolution figures is available at http://www.physics.usyd.edu.au/~asmith/research/ERGpaper.pd

Seedless clustering in all-sky searches for gravitational-wave transients

The problem of searching for unmodeled gravitational-wave bursts can be thought of as a pattern recognition problem: how to find statistically significant clusters in spectrograms of strain power when the precise signal morphology is unknown. In a previous publication, we showed how "seedless clustering" can be used to dramatically improve the sensitivity of searches for long-lived gravitational-wave transients. In order to manage the computational costs, this initial analysis focused on externally triggered searches where the source location and emission time are both known to some degree of precision. In this paper, we show how the principle of seedless clustering can be extended to facilitate computationally-feasible, all-sky searches where the direction and emission time of the source are entirely unknown. We further demonstrate that it is possible to achieve a considerable reduction in computation time by using graphical processor units (GPUs), thereby facilitating more sensitive searches.Comment: 9 pages, 2 figure

Extended Object Tracking: Introduction, Overview and Applications

This article provides an elaborate overview of current research in extended object tracking. We provide a clear definition of the extended object tracking problem and discuss its delimitation to other types of object tracking. Next, different aspects of extended object modelling are extensively discussed. Subsequently, we give a tutorial introduction to two basic and well used extended object tracking approaches - the random matrix approach and the Kalman filter-based approach for star-convex shapes. The next part treats the tracking of multiple extended objects and elaborates how the large number of feasible association hypotheses can be tackled using both Random Finite Set (RFS) and Non-RFS multi-object trackers. The article concludes with a summary of current applications, where four example applications involving camera, X-band radar, light detection and ranging (lidar), red-green-blue-depth (RGB-D) sensors are highlighted.Comment: 30 pages, 19 figure