CGC: a scalable Python package for co- and tri-clustering of geodata cubes

Clustering Geo-Data Cubes (CGC) is a Python package to perform clustering analysis for multidimensional geospatial data. The included tools allow the user to efficiently run tasks in parallel on local and distributed systems

Fast and Credible Likelihood-Free Cosmology with Truncated Marginal Neural Ratio Estimation

Sampling-based inference techniques are central to modern cosmological data analysis; these methods, however, scale poorly with dimensionality and typically require approximate or intractable likelihoods. In this paper we describe how Truncated Marginal Neural Ratio Estimation (TMNRE) (a new approach in so-called simulation-based inference) naturally evades these issues, improving the $(i)$ efficiency, $(ii)$ scalability, and $(iii)$ trustworthiness of the inferred posteriors. Using measurements of the Cosmic Microwave Background (CMB), we show that TMNRE can achieve converged posteriors using orders of magnitude fewer simulator calls than conventional Markov Chain Monte Carlo (MCMC) methods. Remarkably, the required number of samples is effectively independent of the number of nuisance parameters. In addition, a property called \emph{local amortization} allows the performance of rigorous statistical consistency checks that are not accessible to sampling-based methods. TMNRE promises to become a powerful tool for cosmological data analysis, particularly in the context of extended cosmologies, where the timescale required for conventional sampling-based inference methods to converge can greatly exceed that of simple cosmological models such as $\Lambda$CDM. To perform these computations, we use an implementation of TMNRE via the open-source code \texttt{swyft}.Comment: v2: accepted journal version. v1: 37 pages, 13 figures. \texttt{swyft} is available at https://github.com/undark-lab/swyft, and demonstration code for cosmological examples is available at https://github.com/acole1221/swyft-CM

Galaxy And Mass Assembly (GAMA): Self-Organizing Map Application on Nearby Galaxies

Galaxy populations show bimodality in a variety of properties: stellar mass, colour, specific star-formation rate, size, and S\'ersic index. These parameters are our feature space. We use an existing sample of 7556 galaxies from the Galaxy and Mass Assembly (GAMA) survey, represented using five features and the K-means clustering technique, showed that the bimodalities are the manifestation of a more complex population structure, represented by between 2 and 6 clusters. Here we use Self Organizing Maps (SOM), an unsupervised learning technique which can be used to visualize similarity in a higher dimensional space using a 2D representation, to map these five-dimensional clusters in the feature space onto two-dimensional projections. To further analyze these clusters, using the SOM information, we agree with previous results that the sub-populations found in the feature space can be reasonably mapped onto three or five clusters. We explore where the "green valley" galaxies are mapped onto the SOM, indicating multiple interstitial populations within the green valley population. Finally, we use the projection of the SOM to verify whether morphological information provided by GalaxyZoo users, for example, if features are visible, can be mapped onto the SOM-generated map. Voting on whether galaxies are smooth, likely ellipticals, or "featured" can reasonably be separated but smaller morphological features (bar, spiral arms) can not. SOMs promise to be a useful tool to map and identify instructive sub-populations in multidimensional galaxy survey feature space, provided they are large enough.Comment: 14 pages, 14 figures, accepted by MNRA

Galaxy and mass assembly (GAMA): Self-Organizing Map application on nearby galaxies

Galaxy populations show bimodality in a variety of properties: stellar mass, colour, specific star-formation rate, size, and Sérsic index. These parameters are our feature space. We use an existing sample of 7556 galaxies from the Galaxy and Mass Assembly (GAMA) survey, represented using five features and the K-means clustering technique, showed that the bimodalities are the manifestation of a more complex population structure, represented by between two and six clusters. Here we use Self-Organizing Maps (SOM), an unsupervised learning technique that can be used to visualize similarity in a higher dimensional space using a 2D representation, to map these 5D clusters in the feature space on to 2D projections. To further analyse these clusters, using the SOM information, we agree with previous results that the sub-populations found in the feature space can be reasonably mapped on to three or five clusters. We explore where the ‘green valley’ galaxies are mapped on to the SOM, indicating multiple interstitial populations within the green valley population. Finally, we use the projection of the SOM to verify whether morphological information provided by GalaxyZoo users, for example, if features are visible, can be mapped on to the SOM-generated map. Voting on whether galaxies are smooth, likely ellipticals, or ‘featured’ can reasonably be separated but smaller morphological features (bar, spiral arms) can not. SOMs promise to be a useful tool to map and identify instructive sub-populations in multidimensional galaxy survey feature space, provided they are large enough

Galaxy and Mass Assembly (GAMA): formation and growth of elliptical galaxies in the group environment

There are many proposed mechanisms driving the morphological transformation of disc galaxies to elliptical galaxies. In this paper, we determine if the observed transformation in low-mass groups can be explained by the merger histories of galaxies. We measured the group mass– morphology relation for groups from the Galaxy and Mass Assembly group catalogue with masses from 1011 to 1015 M. Contrary to previous studies, the fraction of elliptical galaxies in our more complete group sample increases significantly with group mass across the full range of group mass. The elliptical fraction increases at a rate of 0.163 ± 0.012 per dex of group mass for groups more massive than 1012.5 M. If we allow for uncertainties in the observed group masses, our results are consistent with a continuous increase in elliptical fraction from group masses as low as 1011 M. We tested if this observed relation is consistent with the merger activity using a GADGET-2 dark matter simulation of the galaxy groups. We specified that a simulated galaxy would be transformed to an elliptical morphology either if it experienced a major merger or if its cumulative mass gained from minor mergers exceeded 30 per cent of its final mass. We then calculated a group mass–morphology relation for the simulations. The position and slope of the simulated relation were consistent with the observational relation, with a gradient of 0.184 ± 0.010 per dex of group mass. These results demonstrate a strong correlation between the frequency of merger events and disc-to-elliptical galaxy transformation in galaxy group environments.This research was conducted by the Australian Research Council Centre of Excellence for All-sky Astrophysics (CAASTRO), through project number CE110001020. SB acknowledges funding support from the Australian Research Council through a Future Fellowship (FT140101166). GAMA is a joint European-Australasian project based around a spectroscopic campaign using the AAT. The GAMA input catalogue is based on data taken from the SDSS and the UKIRT Infrared Deep Sky Survey. Complementary imaging of the GAMA regions is being obtained by a number of independent survey programmes including GALEX MIS, VST KIDS, VISTA VIKING, WISE, Herschel-ATLAS, GMRT and ASKAP providing ultraviolet to radio coverage. GAMA is funded by the STFC (UK), the ARC (Australia), the AAO and the participating institutions. The GAMA web site is http://www.gama-survey.org/

Galaxy And Mass Assembly (GAMA): stellar mass growth of spiral galaxies in the cosmic web

We look for correlated changes in stellar mass and star formation rate (SFR) along filaments in the cosmic web by examining the stellar masses and UV-derived SFRs of 1799 ungrouped and unpaired spiral galaxies that reside in filaments. We devise multiple distance metrics to characterize the complex geometry of filaments, and find that galaxies closer to the cylindrical centre of a filament have higher stellar masses than their counterparts near the periphery of filaments, on the edges of voids. In addition, these peripheral spiral galaxies have higher SFRs at a given mass. Complementing our sample of filament spiral galaxies with spiral galaxies in tendrils and voids, we find that the average SFR of these objects in different large-scale environments are similar to each other with the primary discriminant in SFR being stellar mass, in line with previous works. However, the distributions of SFRs are found to vary with large-scale environment. Our results thus suggest a model in which in addition to stellar mass as the primary discriminant, the large-scale environment is imprinted in the SFR as a second-order effect. Furthermore, our detailed results for filament galaxies suggest a model in which gas accretion from voids on to filaments is primarily in an orthogonal direction. Overall, we find our results to be in line with theoretical expectations of the thermodynamic properties of the intergalactic medium in different large-scale environments

Galaxy And Mass Assembly (GAMA) : trends in galaxy colours, morphology, and stellar populations with large-scale structure, group, and pair environments

We explore trends in galaxy properties with Mpc-scale structures using catalogues of environment and large-scale structure from the Galaxy And Mass Assembly (GAMA) survey. Existing GAMA catalogues of large-scale structure, group, and pair membership allow us to construct galaxy stellar mass functions for different environmental types. To avoid simply extracting the known underlying correlations between galaxy properties and stellar mass, we create a mass matched sample of galaxies with stellar masses within 9.5 ≤ log M*/h−2 M⊙ ≤ 11 for each environmental population. Using these samples, we show that mass normalized galaxies in different large-scale environments have similar energy outputs, u − r colours, luminosities, and morphologies. Extending our analysis to group and pair environments, we show that galaxies that are not in groups or pairs exhibit similar characteristics to each other regardless of broader environment. For our mass controlled sample, we fail to see a strong dependence of Sérsic index or galaxy luminosity on halo mass, but do find that it correlates very strongly with colour. Repeating our analysis for galaxies that have not been mass controlled introduces and amplifies trends in the properties of galaxies in pairs, groups, and large-scale structure, indicating that stellar mass is the most important predictor of the galaxy properties we examine, as opposed to environmental classifications.Publisher PDFPeer reviewe

Galaxy And Mass Assembly (GAMA): trends in galaxy colours, morphology, and stellar populations with large-scale structure, group, and pair environments

We explore trends in galaxy properties with Mpc-scale structures using catalogues of environment and large-scale structure from the Galaxy And Mass Assembly (GAMA) survey. Existing GAMA catalogues of large-scale structure, group, and pair membership allow us to construct galaxy stellar mass functions for different environmental types. To avoid simply extracting the known underlying correlations between galaxy properties and stellar mass, we create a mass matched sample of galaxies with stellar masses within 9.5 ≤ log M*/h−2 M⊙ ≤ 11 for each environmental population. Using these samples, we show that mass normalized galaxies in different large-scale environments have similar energy outputs, u − r colours, luminosities, and morphologies. Extending our analysis to group and pair environments, we show that galaxies that are not in groups or pairs exhibit similar characteristics to each other regardless of broader environment. For our mass controlled sample, we fail to see a strong dependence of Sérsic index or galaxy luminosity on halo mass, but do find that it correlates very strongly with colour. Repeating our analysis for galaxies that have not been mass controlled introduces and amplifies trends in the properties of galaxies in pairs, groups, and large-scale structure, indicating that stellar mass is the most important predictor of the galaxy properties we examine, as opposed to environmental classifications