No Arabic abstract
Obtaining high-sensitivity measurements of degree-scale cosmic microwave background (CMB) polarization is the most direct path to detecting primordial gravitational waves. Robustly recovering any primordial signal from the dominant foreground emission will require high-fidelity observations at multiple frequencies, with excellent control of systematics. We explore the potential for a new platform for CMB observations, the Airlander 10 hybrid air vehicle, to perform this task. We show that the Airlander 10 platform, operating at commercial airline altitudes, is well-suited to mapping frequencies above 220 GHz, which are critical for cleaning CMB maps of dust emission. Optimizing the distribution of detectors across frequencies, we forecast the ability of Airlander 10 to clean foregrounds of varying complexity as a function of altitude, demonstrating its complementarity with both existing (Planck) and ongoing (C-BASS) foreground observations. This novel platform could play a key role in defining our ultimate view of the polarized microwave sky.
We apply a messenger field method to solve the linear minimum-variance mapmaking equation in the context of Cosmic Microwave Background (CMB) observations. In simulations, the method produces sky maps that converge significantly faster than those from a conjugate gradient descent algorithm with a diagonal preconditioner, even though the computational cost per iteration is similar. The messenger method recovers large scales in the map better than conjugate gradient descent, and yields a lower overall $chi^2$. In the single, pencil beam approximation, each iteration of the messenger mapmaking procedure produces an unbiased map, and the iterations become more optimal as they proceed. A variant of the method can handle differential data or perform deconvolution mapmaking. The messenger method requires no preconditioner, but a high-quality solution needs a cooling parameter to control the convergence. We study the convergence properties of this new method, and discuss how the algorithm is feasible for the large data sets of current and future CMB experiments.
3D printing presents an attractive alternative to visual representation of physical datasets such as astronomical images that can be used for research, outreach or teaching purposes, and is especially relevant to people with a visual disability. We here report the use of 3D printing technology to produce a representation of the all-sky Cosmic Microwave Background (CMB) intensity anisotropy maps produced by the Planck mission. The success of this work in representing key features of the CMB is discussed as is the potential of this approach for representing other astrophysical data sets. 3D printing such datasets represents a highly complementary approach to the usual 2D projections used in teaching and outreach work, and can also form the basis of undergraduate projects. The CAD files used to produce the models discussed in this paper are made available.
The Cosmic Microwave Background (CMB) is a relict of the early universe. Its perfect 2.725K blackbody spectrum demonstrates that the universe underwent a hot, ionized early phase; its anisotropy (about 80 mu K rms) provides strong evidence for the presence of photon-matter oscillations in the primeval plasma, shaping the initial phase of the formation of structures; its polarization state (about 3 mu K rms), and in particular its rotational component (less than 0.1 mu K rms) might allow to study the inflation process in the very early universe, and the physics of extremely high energies, impossible to reach with accelerators. The CMB is observed by means of microwave and mm-wave telescopes, and its measurements drove the development of ultra-sensitive bolometric detectors, sophisticated modulators, and advanced cryogenic and space technologies. Here we focus on the new frontiers of CMB research: the precision measurements of its linear polarization state, at large and intermediate angular scales, and the measurement of the inverse-Compton effect of CMB photons crossing clusters of Galaxies. In this framework, we will describe the formidable experimental challenges faced by ground-based, near-space and space experiments, using large arrays of detectors. We will show that sensitivity and mapping speed improvement obtained with these arrays must be accompanied by a corresponding reduction of systematic effects (especially for CMB polarimeters), and by improved knowledge of foreground emission, to fully exploit the huge scientific potential of these missions.
Cosmic strings are a well-motivated extension to the standard cosmological model and could induce a subdominant component in the anisotropies of the cosmic microwave background (CMB), in addition to the standard inflationary component. The detection of strings, while observationally challenging, would provide a direct probe of physics at very high energy scales. We develop a new framework for cosmic string inference, constructing a Bayesian analysis in wavelet space where the string-induced CMB component has distinct statistical properties to the standard inflationary component. Our wavelet-Bayesian framework provides a principled approach to compute the posterior distribution of the string tension $Gmu$ and the Bayesian evidence ratio comparing the string model to the standard inflationary model. Furthermore, we present a technique to recover an estimate of any string-induced CMB map embedded in observational data. Using Planck-like simulations we demonstrate the application of our framework and evaluate its performance. The method is sensitive to $Gmu sim 5 times 10^{-7}$ for Nambu-Goto string simulations that include an integrated Sachs-Wolfe (ISW) contribution only and do not include any recombination effects, before any parameters of the analysis are optimised. The sensitivity of the method compares favourably with other techniques applied to the same simulations.
The observation of cosmic microwave background (CMB) anisotropies is one of the key probes of physical cosmology. The weak nature of this signal has driven the construction of increasingly complex and sensitive experiments observing the sky at multiple frequencies with thousands of polarization sensitive detectors. Given the high sensitivity of such experiments, instrumental systematic effects can become the limiting factor towards the full scientific exploitation of their data. In this paper we present s4cmb (Systematics for CMB), a Python package designed to simulate raw data streams in time domain of modern CMB experiments based on bolometric technology, and to inject in these realistic instrumental systematics effects. The aim of the package is to help assessing the contamination due to instrumental systematic effects on real data, to guide the design of future instruments, as well as to increase the realism of simulated data sets required in the development of accurate data analysis methods.