Forecasts for Low Spin Black Hole Spectroscopy in Horndeski Gravity
(2019)
KiDS+GAMA: Intrinsic alignment model constraints for current and future weak lensing cosmology
Astronomy and Astrophysics EDP Sciences (2019)
Abstract:
© ESO 2019. We directly constrain the non-linear alignment (NLA) model of intrinsic galaxy alignments, analysing the most representative and complete flux-limited sample of spectroscopic galaxies available for cosmic shear surveys. We measure the projected galaxy position-intrinsic shear correlations and the projected galaxy clustering signal using high-resolution imaging from the Kilo Degree Survey (KiDS) overlapping with the GAMA spectroscopic survey, and data from the Sloan Digital Sky Survey. Separating samples by colour, we make no significant detection of blue galaxy alignments, constraining the blue galaxy NLA amplitude A IAB = 0.21 -0.36+0.37 to be consistent with zero. We make robust detections (∼9σ) for red galaxies, with A IAR = 3.18 -0.46+0.47 , corresponding to a net radial alignment with the galaxy density field, and we find no evidence for any scaling of alignments with galaxy luminosity. We provide informative priors for current and future weak lensing surveys, an improvement over de facto wide priors that allow for unrealistic levels of intrinsic alignment contamination. For a colour-split cosmic shear analysis of the final KiDS survey area, we forecast that our priors will improve the constraining power on S 8 and the dark energy equation of state w 0 , by up to 62% and 51%, respectively. Our results indicate, however, that the modelling of red/blue-split galaxy alignments may be insufficient to describe samples with variable central/satellite galaxy fractions.A Bayesian quantification of consistency in correlated data sets
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY 484:3 (2019) 3126-3153
Development of a sorption-cooled continuous miniature dilution refrigerator for 100 mK detector testing
IOP Conference Series: Materials Science and Engineering IOP Publishing 502 (2019)
Abstract:
As the forthcoming generation of Cosmic Microwave Background observatories move towards the use of large format detector arrays operating at ~100 mK, the need for test cryostats capable of operating in this temperature regime is becoming more pronounced. This has strongly driven the development of several related systems, including the continuous miniature dilution refrigerator (MDR) reported here. The MDR is comprised of a thermally separated mixing chamber, step heat exchangers, twin stills and twin condensation pumps. The pumps are alternately cooled to ~300 mK by a pair of single-shot 1He sorption coolers (cycled in anti-phase) to circulate 3He in the system. The system is therefore closed-cycle, with the circulation of 3He, both in the MDR and sorption coolers, contained to the cold stage. As a result, the reliability of the system is improved through a mechanically simple design and the absence of external connections, gas handling systems, and cold o-rings.
Explicit Bayesian treatment of unknown foreground contaminations in galaxy surveys
A&A 2019
Abstract:
The treatment of unknown foreground contaminations will be one of the major challenges for galaxy clustering analyses of coming decadal surveys. These data contaminations introduce erroneous large-scale effects in recovered power spectra and inferred dark matter density fields. In this work, we present an effective solution to this problem in the form of a robust likelihood designed to account for effects due to unknown foreground and target contaminations. Conceptually, this robust likelihood marginalizes over the unknown large-scale contamination amplitudes. We showcase the effectiveness of this novel likelihood via an application to a mock SDSS-III data set subject to dust extinction contamination. In order to illustrate the performance of our proposed likelihood, we infer the underlying dark-matter density field and reconstruct the matter power spectrum, being maximally agnostic about the foregrounds. The results are compared to those of an analysis with a standard Poissonian likelihood, as typically used in modern large-scale structure analyses. While the standard Poissonian analysis yields excessive power for large-scale modes and introduces an overall bias in the power spectrum, our likelihood provides unbiased estimates of the matter power spectrum over the entire range of Fourier modes considered in this work. Further, we demonstrate that our approach accurately accounts for and corrects the effects of unknown foreground contaminations when inferring three-dimensional density fields. Robust likelihood approaches, as presented in this work, will be crucial to control unknown systematic error and maximize the outcome of the decadal surveys.