Impact of Galactic dust non-Gaussianity on searches for B-modes from inflation
(2023)
Science with the Einstein Telescope: a comparison of different designs
Journal of Cosmology and Astroparticle Physics IOP Publishing 2023 (2023) 068
Abstract:
The Einstein Telescope (ET), the European project for a third-generation gravitational-wave detector, has a reference configuration based on a triangular shape consisting of three nested detectors with 10 km arms, where each detector has a 'xylophone' configuration made of an interferometer tuned toward high frequencies, and an interferometer tuned toward low frequencies and working at cryogenic temperature. Here, we examine the scientific perspectives under possible variations of this reference design. We perform a detailed evaluation of the science case for a single triangular geometry observatory, and we compare it with the results obtained for a network of two L-shaped detectors (either parallel or misaligned) located in Europe, considering different choices of arm-length for both the triangle and the 2L geometries. We also study how the science output changes in the absence of the low-frequency instrument, both for the triangle and the 2L configurations. We examine a broad class of simple 'metrics' that quantify the science output, related to compact binary coalescences, multi-messenger astronomy and stochastic backgrounds, and we then examine the impact of different detector designs on a more specific set of scientific objectives.Constraints on dark matter and astrophysics from tomographic $\gamma$-ray cross-correlations
(2023)
Cosmology with 6 parameters in the Stage-IV era: efficient marginalisation over nuisance parameters
Open Journal of Astrophysics Maynooth Academic Publishing 6 (2023)
Abstract:
The analysis of photometric large-scale structure data is often complicated by the need to account for many observational and astrophysical systematics. The elaborate models needed to describe them often introduce many "nuisance parameters’', which can be a major inhibitor of an efficient parameter inference. In this paper we introduce an approximate method to analytically marginalise over a large number of nuisance parameters based on the Laplace approximation. We discuss the mathematics of the method, its relation to concepts such as volume effects and profile likelihood, and show that it can be further simplified for calibratable systematics by linearising the dependence of the theory on the associated parameters. We quantify the accuracy of this approach by comparing it with traditional sampling methods in the context of existing data from the Dark Energy Survey, as well as futuristic Stage-IV photometric data. The linearised version of the method is able to obtain parameter constraints that are virtually equivalent to those found by exploring the full parameter space for a large number of calibratable nuisance parameters, while reducing the computation time by a factor 3-10. Furthermore, the non-linearised approach is able to analytically marginalise over a large number of parameters, returning constraints that are virtually indistinguishable from the brute-force method in most cases, accurately reproducing both the marginalised uncertainty on cosmological parameters, and the impact of volume effects associated with this marginalisation. We provide simple recipes to diagnose when the approximations made by the method fail and one should thus resort to traditional methods. The gains in sampling efficiency associated with this method enable the joint analysis of multiple surveys, typically hindered by the large number of nuisance parameters needed to describe them.Galaxy bias in the era of LSST: perturbative bias expansions
(2023)