Róbert Beck

Leadership and path characteristics during walks are linked to dominance order and individual traits in dogs.

Movement interactions and the underlying social structure in groups have relevance across many social-living species. Collective motion of groups could be based on an “egalitarian” decision system, but in practice it is often influenced by underlying social network structures and by individual characteristics. We investigated whether dominance rank and personality traits are linked to leader and follower roles during joint motion of family dogs. We obtained high-resolution spatio-temporal GPS trajectory data (823,148 data points) from six dogs belonging to the same household and their owner during 14 30–40 min unleashed walks. We identified several features of the dogs paths (e.g., running speed or distance from the owner) which are characteristic of a given dog. A directional correlation analysis quantifies interactions between pairs of dogs that run loops jointly. We found that dogs play the role of the leader about 50–85% of the time, i.e. the leader and follower roles in a given pair are dynamically interchangable. However, on a longer timescale tendencies to lead differ consistently. The network constructed from these loose leader–follower relations is hierarchical, and the dogs positions in the network correlates with the age, dominance rank, trainability, controllability, and aggression measures derived from personality questionnaires. We demonstrated the possibility of determining dominance rank and personality traits of an individual based only on its logged movement data. The collective motion of dogs is influenced by underlying social network structures and by characteristics such as personality differences. Our findings could pave the way for automated animal personality and human social interaction measurements.

On the realistic validation of photometric redshifts

Two of the main problems encountered in the development and accurate validation of photometric redshift (photo-z) techniques are the lack of spectroscopic coverage in feature space (e.g. colours and magnitudes) and the mismatch between photometric error distributions associated with the spectroscopic and photometric samples. Although these issues are well known, there is currently no standard benchmark allowing a quantitative analysis of their impact on the final photo-z estimation. In this work, we present two galaxy catalogues, Teddy and Happy, built to enable a more demanding and realistic test of photo-z methods. Using photometry from the Sloan Digital Sky Survey and spectroscopy from a collection of sources, we constructed datasets which mimic the biases between the underlying probability distribution of the real spectroscopic and photometric sample. We demonstrate the potential of these catalogues by submitting them to the scrutiny of different photo-z methods, including machine learning (ML) and template fitting approaches. Beyond the expected bad results from most ML algorithms for cases with missing coverage in feature space, we were able to recognize the superiority of global models in the same situation and the general failure across all types of methods when incomplete coverage is convoluted with the presence of photometric errors - a data situation which photo-z methods were not trained to deal with up to now and which must be addressed by future large scale surveys. Our catalogues represent the first controlled environment allowing a straightforward implementation of such tests. The data are publicly available within the COINtoolbox (this https URL).

Concordance cosmology without dark energy

According to the separate universe conjecture, spherically symmetric sub-regions in an isotropic universe behave like mini-universes with their own cosmological parameters. This is an excellent approximation in both Newtonian and general relativistic theories. We estimate local expansion rates for a large number of such regions, and use a scale parameter calculated from the volume-averaged increments of local scale parameters at each time step in an otherwise standard cosmological

A probabilistic approach to emission-line galaxy classification

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Quantifying correlations between galaxy emission lines and stellar continua

-body simulation. The particle mass, corresponding to a coarse graining scale, is an adjustable parameter. This mean field approximation neglects tidal forces and boundary effects, but it is the first step towards a non-perturbative statistical estimation of the effect of non-linear evolution of structure on the expansion rate. Using our algorithm, a simulation with an initial

Photo-z-SQL: Integrated, flexible photometric redshift computation in a database

Omega_m=1

Quantifying correlations between galaxy emission lines and stellar continua using a PCA-based technique

Einstein--de~Sitter setting closely tracks the expansion and structure growth history of the

Photometric redshifts for the SDSS Data Release 12

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On the realistic validation of photometric redshifts, or why Teddy will never be Happy

CDM cosmology. Due to small but characteristic differences, our model can be distinguished from the

Optimizing spectroscopic follow-up strategies for supernova photometric classification with active learning

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