During the last ten years, a considerable amount of effort has been made to develop algorithms for automatic classification of variable stars. That has been primarily achieved by applying machine learning methods to photometric datasets where objects are represented as light curves. Classifiers require training sets to learn the underlying patterns that allow the separation among classes. Unfortunately, building training sets is an expensive process that demands a lot of human efforts. Every time data comes from new surveys; the only available training instances are the ones that have a cross-match with previously labelled objects, consequently generating insufficient training sets compared with the large amounts of unlabelled sources. In this work, we present an algorithm that performs unsupervised classification of variable stars, relying only on the similarity among light curves. We tackle the unsupervised classification problem by proposing an untraditional approach. Instead of trying to match classes of stars with clusters found by a clustering algorithm, we propose a query based method where astronomers can find groups of variable stars ranked by similarity. We also develop a fast similarity function specific for light curves, based on a novel data structure that allows scaling the search over the entire dataset of unlabelled objects. Experiments show that our unsupervised model achieves high accuracy in the classification of different types of variable stars and that the proposed algorithm scales up to massive amounts of light curves.

We describe a revised procedure for the numerical simulation of planetary nebulae luminosity functions (PNLF), improving on previous work (Méndez & Soffner 1997). The procedure now is based on new H-burning post-AGB evolutionary tracks (Miller Bertolami 2016). For a given stellar mass, the new central stars are more luminous and evolve faster. We have slightly changed the distribution of the [O III] 5007 intensities relative to those of Hβ and the generation of absorbing factors, while still basing their numerical modeling on empirical information extracted from studies of galactic planetary nebulae (PNs) and their central stars. We argue that the assumption of PNs being completely optically thick to H-ionizing photons leads to conflicts with observations and show that to account for optically thin PNs is necessary. We then use the new simulations to estimate a maximum final mass, clarifying its meaning, and discuss the effect of internal dust extinction as a possible way of explaining the persistent discrepancy between PNLF and surface brightness fluctuation (SBF) distances. By adjusting the range of minimum to maximum final mass, it is also possible to explain the observed variety of PNLF shapes at intermediate magnitudes. The new PN formation rates are calculated to be slightly lower than suggested by previous simulations based on older post-AGB evolutionary tracks.

We present an empirical model for the number of globular clusters (GCs) in galaxies based on recent data showing a tight relationship between dark matter halo virial masses and GC numbers. While a simple base model forming GCs in low-mass haloes reproduces this relation, we show that a second formation pathway for GCs is needed to account for observed younger GC populations. We confirm previous works that reported the observed linear correlation as being a consequence of hierarchical merging and its insensitivity to the exact GC formation processes at higher virial masses, even for a dual formation scenario. We find that the scatter of the linear relation is strongly correlated with the relative amount of smooth accretion: the more dark matter is smoothly accreted, the fewer GCs a halo has compared to other haloes of the same mass. This scatter is smaller than that introduced by halo mass measurements, indicating that the number of GCs in a galaxy is a good tracer for its dark matter mass. Smooth accretion is also the reason for a lower average dark matter mass per GC in low-mass haloes. Finally, we successfully reproduce the observed general trend of GCs being old and the tendency of more massive haloes hosting older GC systems. Including the second GC formation mechanism through gas-rich mergers leads to a more realistic variety of GC age distributions and also introduces an age inversion in the halo virial mass range log Mvir/M⊙ = 11–13.

We discuss the statistical distribution of galaxy shapes and viewing angles under the assumption of triaxiality by deprojecting observed surface brightness profiles of 56 brightest cluster galaxies (BCGs) coming from a recently published large deep-photometry sample. For the first time, we address this issue by directly measuring axis ratio profiles without limiting ourselves to a statistical analysis of average ellipticities. We show that these objects are strongly triaxial, with triaxiality parameters 0.39 ≤ T ≤ 0.72, they have average axis ratios 〈p(r)〉 = 0.84 and 〈q(r)〉 = 0.68, and they are more spherical in the central regions but flatten out at large radii. Measured shapes in the outskirts agree well with the shapes found for simulated massive galaxies and their dark matter halos from both the IllustrisTNG and the Magneticum simulations, possibly probing the nature of dark matter. In contrast, both simulations fail to reproduce the observed inner regions of BCGs, producing objects that are too flattened.

Shape measurements of galaxies and galaxy clusters are widespread in the analysis of cosmological simulations. But the limitations of those measurements have been poorly investigated. In this Letter, we explain why the quality of the shape measurement does not only depend on the numerical resolution, but also on the density gradient. In particular, this can limit the quality of measurements in the central regions of haloes. We propose a criterion to estimate the sensitivity of the measured shapes based on the density gradient of the halo and to apply it to cosmological simulations of collisionless and self-interacting dark matter. By this, we demonstrate where reliable measurements of the halo shape are possible and how cored density profiles limit their applicability.