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Abstract:

The next generation of large scale imaging surveys (such as those conducted with the Large Synoptic Survey Telescope and Euclid) will require accurate photometric redshifts in order to optimally extract cosmological information. Gaussian Processes for photometric redshift estimation (GPz) is a promising new method that has been proven to provide efficient, accurate photometric redshift estimations with reliable variance predictions. In this paper, we investigate a number of methods for improving the photometric redshift estimations obtained using GPz (but which are also applicable to others). We use spectroscopy from the Galaxy and Mass Assembly Data Release 2 with a limiting magnitude of r<19.4 along with corresponding Sloan Digital Sky Survey visible (ugriz) photometry and the UKIRT Infrared Deep Sky Survey Large Area Survey near-IR (YJHK) photometry. We evaluate the effects of adding near-IR magnitudes and angular size as features for the training, validation and testing of GPz and find that these improve the accuracy of the results by ~15-20 per cent. In addition, we explore a post-processing method of shifting the probability distributions of the estimated redshifts based on their Quantile-Quantile plots and find that it improves the bias by ~40 per cent. Finally, we investigate the effects of using more precise photometry obtained from the Hyper Suprime-Cam Subaru Strategic Program Data Release 1 and find that it produces significant improvements in accuracy, similar to the effect of including additional features.

Abstract:

U-band observations with the LSST have yet to be fully optimized in cadence. The straw man survey design is a simple coverage of the medium-deep-fast survey. Here we argue that deep coverage of the four deep drilling fields (XMM-LSS, ECDFS, ELAIS-S1 and COSMOS) has a much higher scientific return, given that these are also the target of the Southern Hemisphere's Square Kilometer Array Pathfinder, the MeerKAT specifically, deep radio observations.

Abstract:

Abstract:

Despite their ubiquity, the origin of cosmic magnetic fields remains unknown. Various mechanisms have been proposed for their existence including primordial fields generated by inflation, or amplification and injection by compact astrophysical objects. Separating the potential impact of each magnetogenesis scenario on the magnitude and orientation of the magnetic field and their impact on gas dynamics may give insight into the physics that magnetised our Universe. In this work, we demonstrate that because the induction equation and solenoidal constraint are linear with $B$, the contribution from different sources of magnetic field can be separated in cosmological magnetohydrodynamics simulations and their evolution and influence on the gas dynamics can be tracked. Exploiting this property, we develop a magnetic field tracer algorithm for cosmological simulations that can track the origin and evolution of different components of the magnetic field. We present a suite of cosmological magnetohydrodynamical RAMSES simulations that employ this algorithm where the primordial field strength is varied to determine the contributions of the primordial and supernovae-injected magnetic fields to the total magnetic energy as a function of time and spatial location. We find that, for our specific model, the supernova-injected fields rarely penetrate far from haloes, despite often dominating the total magnetic energy in the simulations. The magnetic energy density from the supernova-injected field scales with density with a power-law slope steeper than 4/3 and often dominates the total magnetic energy inside of haloes. However, the star formation rates in our simulations are not affected by the presence of magnetic fields, for the ranges of primordial field strengths examined. These simulations represent a first demonstration of the magnetic field tracer algorithm (abridged).

Abstract:

Observational cosmology in the next decade will rely on probes of the distribution of matter in the redshift range between $0