Galaxy Zoo: star-formation versus spiral arm number
Monthly Notices of the Royal Astronomical Society (2017)
Abstract:
Spiral arms are common features in low-redshift disc galaxies, and are prominent sites of star-formation and dust obscuration. However, spiral structure can take many forms: from galaxies displaying two strong `grand design' arms, to those with many `flocculent' arms. We investigate how these different arm types are related to a galaxy's star-formation and gas properties by making use of visual spiral arm number measurements from Galaxy Zoo 2. We combine UV and mid-IR photometry from GALEX and WISE to measure the rates and relative fractions of obscured and unobscured star formation in a sample of low-redshift SDSS spirals. Total star formation rate has little dependence on spiral arm multiplicity, but two-armed spirals convert their gas to stars more efficiently. We find significant differences in the fraction of obscured star-formation: an additional $\sim 10$ per cent of star-formation in two-armed galaxies is identified via mid-IR dust emission, compared to that in many-armed galaxies. The latter are also significantly offset below the IRX-$\beta$ relation for low-redshift star-forming galaxies. We present several explanations for these differences versus arm number: variations in the spatial distribution, sizes or clearing timescales of star-forming regions (i.e., molecular clouds), or contrasting recent star-formation histories.Superluminous Supernovae at High Redshift
Publications of the Astronomical Society of Australia 34 (2017)
Abstract:
Copyright © Astronomical Society of Australia 2017. Superluminous supernovae are beginning to be discovered at redshifts as early as the epoch of reionisation. A number of candidate mechanisms is reviewed, together with the discovery programmes.Observational future of cosmological scalar-tensor theories
PHYSICAL REVIEW D 95:6 (2017) ARTN 063502
Cosmic expansion history from SNe Ia data via information field theory
A&A 2017
Abstract:
We present charm (cosmic history agnostic reconstruction method), a novel inference algorithm that reconstructs the cosmic expansion history as encoded in the Hubble parameter H(z) from SNe Ia data. The novelty of the approach lies in the usage of information field theory, a statistical field theory that is very well suited for the construction of optimal signal recovery algorithms. The charm algorithm infers non-parametrically s(a)=ln(ρ(a)/ρcrit0), the density evolution which determines H(z), without assuming an analytical form of ρ(a) but only its smoothness with the scale factor a=(1+z)−1. The inference problem of recovering the signal s(a) from the data is formulated in a fully Bayesian way. In detail, we have rewritten the signal as the sum of a background cosmology and a perturbation. This allows us to determine the maximum a posteriory estimate of the signal by an iterative Wiener filter method. Applying charm to the Union2.1 supernova compilation, we have recovered a cosmic expansion history that is fully compatible with the standard ΛCDM cosmological expansion history with parameter values consistent with the results of the Planck mission.
NIFTy 3 - Numerical Information Field Theory - A Python framework for multicomponent signal inference on HPC clusters
Annalen der Physik 2017
Abstract:
NIFTy, "Numerical Information Field Theory", is a software framework designed to ease the development and implementation of field inference algorithms. Field equations are formulated independently of the underlying spatial geometry allowing the user to focus on the algorithmic design. Under the hood, NIFTy ensures that the discretization of the implemented equations is consistent. This enables the user to prototype an algorithm rapidly in 1D and then apply it to high-dimensional real-world problems. This paper introduces NIFTy 3, a major upgrade to the original NIFTy framework. NIFTy 3 allows the user to run inference algorithms on massively parallel high performance computing clusters without changing the implementation of the field equations. It supports n-dimensional Cartesian spaces, spherical spaces, power spaces, and product spaces as well as transforms to their harmonic counterparts. Furthermore, NIFTy 3 is able to treat non-scalar fields. The functionality and performance of the software package is demonstrated with example code, which implements a real inference algorithm from the realm of information field theory.