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Collisions between galaxy clusters provide a test of the nongravitational forces acting on dark matter. Dark matter's lack of deceleration in the \"bullet cluster\" collision constrained its self-interaction cross section σDM/m < 1.25 square centimenters per gram (cm2μ) [68% confidence limit (CL)] (σDM, self-interaction cross section; m, unit mass of dark matter) for long-ranged forces. Using the Chandra and Hubble Space Telescopes, we have now observed 72 collisions, including both major and minor mergers. Combining these measurements statistically, we detect the existence of dark mass at 7.6σ significance. The position of the dark mass has remained closely aligned within 5.8 ± 8.2 kiloparsecs of associated stars, implying a self-interaction cross section σDM/m < 0.47 cm2/g (95% CL) and disfavoring some proposed extensions to the standard model.

The alignments between galaxies, their underlying matter structures, and the cosmic web constitute vital ingredients for a comprehensive understanding of gravity, the nature of matter, and structure formation in the Universe. We provide an overview on the state of the art in the study of these alignment processes and their observational signatures, aimed at a non-specialist audience. The development of the field over the past one hundred years is briefly reviewed. We also discuss the impact of galaxy alignments on measurements of weak gravitational lensing, and discuss avenues for making theoretical and observational progress over the coming decade.

Galaxy shapes are not randomly oriented, rather they are statistically aligned in a way that can depend on formation environment, history and galaxy type. Studying the alignment of galaxies can therefore deliver important information about the physics of galaxy formation and evolution as well as the growth of structure in the Universe. In this review paper we summarise key measurements of galaxy alignments, divided by galaxy type, scale and environment. We also cover the statistics and formalism necessary to understand the observations in the literature. With the emergence of weak gravitational lensing as a precision probe of cosmology, galaxy alignments have taken on an added importance because they can mimic cosmic shear, the effect of gravitational lensing by large-scale structure on observed galaxy shapes. This makes galaxy alignments, commonly referred to as intrinsic alignments, an important systematic effect in weak lensing studies. We quantify the impact of intrinsic alignments on cosmic shear surveys and finish by reviewing practical mitigation techniques which attempt to remove contamination by intrinsic alignments.

The shapes of galaxies are not randomly oriented on the sky. During the galaxy formation and evolution process, environment has a strong influence, as tidal gravitational fields in the large-scale structure tend to align nearby galaxies. Additionally, events such as galaxy mergers affect the relative alignments of both the shapes and angular momenta of galaxies throughout their history. These “intrinsic galaxy alignments” are known to exist, but are still poorly understood. This review will offer a pedagogical introduction to the current theories that describe intrinsic galaxy alignments, including the apparent difference in intrinsic alignment between early- and late-type galaxies and the latest efforts to model them analytically. It will then describe the ongoing efforts to simulate intrinsic alignments using both N -body and hydrodynamic simulations. Due to the relative youth of this field, there is still much to be done to understand intrinsic galaxy alignments and this review summarises the current state of the field, providing a solid basis for future work.

Parasites are integral parts of ecosystem function and important drivers of evolutionary processes. Characterizing ectoparasite diversity is fundamental to studies of host–parasite interactions, evolution, and conservation, and also for understanding emerging disease threats for some vector borne pathogens. With more than 1400 species, bats represent the second most speciose mammalian clade, but their ectoparasite fauna are poorly known for most species. We sequenced mitochondrial Cytochrome Oxidase C subunit I and nuclear 18S ribosomal gene fragments, and used Bayesian phylogenetic analyses to characterize ectoparasite taxon identity and diversity for 17 species of parasitized bats sampled along the Baja California peninsula and in Northwestern Mexico. The sequence data revealed multiple novel lineages of bat bugs (Cimicidae), flies (Nycteribiidae and Streblidae), and ticks (Argasidae). Within families, the new linages showed more than 10% sequence divergence, which is consistent with separation at least at the species level. Both families of bat flies showed host specificity, particularly on Myotis species. We also identified new records for the Baja peninsula of one tick (Carios kelleyi), and of five Streblid bat fly species. One Nycteribiid bat fly haplotype from Pallid bat (Antrozous pallidus) hosts was found throughout the peninsula, suggesting potential long distance co‐dispersal with hosts. Different bat bug and tick communities were found in the north and south of the peninsula. This study is the first systematic survey of bat ectoparasites in the Baja California peninsula, revealing novel lineages that are highly genetically differentiated from other parts of North America. For some ectoparasite species, haplotype distributions may reflect patterns of bat migration. This work is a first step in characterizing ectoparasite diversity over the Baja California peninsula, and understanding how ecological and evolutionary interactions shape bat ectoparasite communities among host species in different parts of their ranges. Parasites are integral parts of the ecosystem. Using phylogenetic techniques, we found multiple novel lineages of bat bugs, flies, and ticks in Northwestern Mexico. For some ectoparasite species, haplotype distribution may reflect patterns of bat dispersal.

ABSTRACT In this work, we describe a framework for solving spherical inverse imaging problems using posterior sampling for full uncertainty quantification. Inverse imaging problems defined on the sphere arise in many fields, including seismology and cosmology, where images are defined on the globe and the cosmic sphere, and are generally high-dimensional and computationally expensive. As a result, sampling the posterior distribution of spherical imaging problems is a challenging task. Our framework leverages a proximal Markov chain Monte Carlo (MCMC) algorithm to efficiently sample the high-dimensional space of spherical images with a sparsity-promoting wavelet prior. We detail the modifications needed for the algorithm to be applied to spherical problems, and give special consideration to the crucial forward modelling step, which contains computationally expensive spherical harmonic transforms. By sampling the posterior, our framework allows for full and flexible uncertainty quantification, something which is not possible with other methods based on, for example, convex optimization. We demonstrate our framework in practice on full-sky cosmological mass-mapping and to the construction of phase velocity maps in global seismic tomography. We find that our approach is potentially useful at moderate resolutions, such as those of interest in seismology. However, at high resolutions, such as those required for astrophysical applications, the poor scaling of the complexity of spherical harmonic transforms severely limits our method, which may be resolved with future GPU implementations. A new Python package, pxmcmc, containing the proximal MCMC sampler, measurement operators, wavelet transforms, and sparse priors are made publicly available.

Issue Title: Galaxy Alignments

The GRavitational lEnsing Accuracy Testing 2008 (GREAT08) Challenge focuses on a problem that is of crucial importance for future observations in cosmology. The shapes of distant galaxies can be used to determine the properties of dark energy and the nature of gravity, because light from those galaxies is bent by gravity from the intervening dark matter. The observed galaxy images appear distorted, although only slightly, and their shapes must be precisely disentangled from the effects of pixelisation, convolution and noise. The worldwide gravitational lensing community has made significant progress in techniques to measure these distortions via the Shear TEsting Program (STEP). Via STEP, we have run challenges within our own community, and come to recognise that this particular image analysis problem is ideally matched to experts in statistical inference, inverse problems and computational learning. Thus, in order to continue the progress seen in recent years, we are seeking an infusion of new ideas from these communities. This document details the GREAT08 Challenge for potential participants. Please visit www.great08challenge.info for the latest information.

By organizing Kaggle competitions, astrophysicist Thomas Kitching can focus on asking the right questions.

GRavitational lEnsing Accuracy Testing 2010 (GREAT10) is a public image analysis challenge aimed at the development of algorithms to analyze astronomical images. Specifically, the challenge is to measure varying image distortions in the presence of a variable convolution kernel, pixelization and noise. This is the second in a series of challenges set to the astronomy, computer science and statistics communities, providing a structured environment in which methods can be improved and tested in preparation for planned astronomical surveys. GREAT10 extends upon previous work by introducing variable fields into the challenge. The \"Galaxy Challenge\" involves the precise measurement of galaxy shape distortions, quantified locally by two parameters called shear, in the presence of a known convolution kernel. Crucially, the convolution kernel and the simulated gravitational lensing shape distortion both now vary as a function of position within the images, as is the case for real data. In addition, we introduce the \"Star Challenge\" that concerns the reconstruction of a variable convolution kernel, similar to that in a typical astronomical observation. This document details the GREAT10 Challenge for potential participants. Continually updated information is also available from www.greatchallenges.info.