Beecroft Institute for Particle Astrophysics and Cosmology

Abstract:

A key challenge in the search for primordial B-modes is the presence of polarized Galactic foregrounds, especially thermal dust emission. Power-spectrum-based analysis methods generally assume the foregrounds to be Gaussian random fields when constructing a likelihood and computing the covariance matrix. In this paper, we investigate how non-Gaussianity in the dust field instead affects CMB and foreground parameter inference in the context of inflationary B-mode searches, capturing this effect via modifications to the dust power-spectrum covariance matrix. For upcoming experiments such as the Simons Observatory, we find no dependence of the tensor-to-scalar ratio uncertainty σ(r)on the degree of dust non-Gaussianity or the nature of the dust covariance matrix. We provide an explanation of this result, noting that when frequency decorrelation is negligible, dust in mid-frequency channels is cleaned using high-frequency data in a way that is independent of the spatial statistics of dust. We show that our results hold also for non-zero levels of frequency decorrelation that are compatible with existing data. We find, however, that neglecting the impact of dust non-Gaussianity in the covariance matrix can lead to inaccuracies in goodness-of-fit metrics. Care must thus be taken when using such metrics to test B-mode spectra and models, although we show that any such problems can be mitigated by using only cleaned spectrum combinations when computing goodness-of-fit statistics.

Abstract:

The increasing statistical power of cosmic microwave background (CMB) datasets requires a commensurate effort in understanding their noise properties. The noise in maps from ground-based instruments is dominated by large-scale correlations, which poses a modeling challenge. This paper develops novel models of the complex noise covariance structure in the Atacama Cosmology Telescope Data Release 6 (ACT DR6) maps. We first enumerate the noise properties that arise from the combination of the atmosphere and the ACT scan strategy. We then prescribe a class of Gaussian, map-based noise models, including a new wavelet-based approach that uses directional wavelet kernels for modeling correlated instrumental noise. The models are empirical, whose only inputs are a small number of independent realizations of the same region of sky. We evaluate the performance of these models against the ACT DR6 data by drawing ensembles of noise realizations. Applying these simulations to the ACT DR6 power spectrum pipeline reveals a $\sim 20\%$ excess in the covariance matrix diagonal when compared to an analytic expression that assumes noise properties are uniquely described by their power spectrum. Along with our public code, $\mathtt{mnms}$, this work establishes a necessary element in the science pipelines of both ACT DR6 and future ground-based CMB experiments such as the Simons Observatory (SO).41 pages (+10 appendix), 22 figures (+5 appendix), submitted to JCA

Abstract:

There is compelling evidence that the Universe is undergoing a late phase of accelerated expansion. One of the simplest explanations for this behavior is the presence of dark energy. A plethora of microphysical models for dark energy have been proposed. The hope is that, with the ever increasing precision of cosmological surveys, it will be possible to precisely pin down the model. We show that this is unlikely and that, at best, we will have a phenomenological description for the microphysics of dark energy. Furthermore, we argue that the current phenomenological prescriptions are ill-equipped for shedding light on the fundamental theory of dark energy.

Abstract:

The Milky Way is thought to host a huge population of interstellar objects (ISOs), numbering approximately $10^{15}\mathrm{pc}^{-3}$ around the Sun, which are formed and shaped by a diverse set of processes ranging from planet formation to galactic dynamics. We define a novel framework: firstly to predict the properties of this Galactic ISO population by combining models of processes across planetary and galactic scales, and secondly to make inferences about the processes modelled, by comparing the predicted population to what is observed. We predict the spatial and compositional distribution of the Galaxy's population of ISOs by modelling the Galactic stellar population with data from the APOGEE survey and combining this with a protoplanetary disk chemistry model. Selecting ISO water mass fraction as an example observable quantity, we evaluate its distribution both at the position of the Sun and averaged over the Galactic disk; our prediction for the Solar neighbourhood is compatible with the inferred water mass fraction of 2I/Borisov. We show that the well-studied Galactic stellar metallicity gradient has a corresponding ISO compositional gradient. We also demonstrate the inference part of the framework by using the current observed ISO composition distribution to constrain the parent star metallicity dependence of the ISO production rate. This constraint, and other inferences made with this framework, will improve dramatically as the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) progresses and more ISOs are observed. Finally, we explore generalisations of this framework to other Galactic populations, such as that of exoplanets.Comment: Accepted to A