arXiv subjects

Ben Thorne

Publications and source records attributed to Ben Thorne.

6 recordsLinked to original sources

arXiv2021

The Python Sky Model 3 software

The Python Sky Model (PySM) is a Python package used by Cosmic Microwave Background (CMB) experiments to simulate maps, in HEALPix pixelization, of the various diffuse astrophysical components of Galactic emission relevant at CMB frequencies (i.e. dust, synchrotron, free-free and Anomalous Microwave Emission), as well as the CMB itself. These maps may be integrated over a given instrument bandpass and smoothed with a given instrument beam. PySM 2, released in 2016, has become the de-facto standard for simulating Galactic emission, for example it is used by CMB-S4, Simons Observatory, LiteBird, PICO, CLASS, POLARBEAR and other CMB experiments, as shown by the 80+ citations of the PySM 2 publication. As the resolution of upcoming experiments increases, the PySM 2 software has started to show some limitations, the solution to these issues was to reimplement PySM from scratch focusing on these features: reimplement all the models with the numba Just-In-Time compiler for Python to reduce memory overhead and optimize performance; use MPI through mpi4py to coordinate execution of PySM 3 across multiple nodes and rely on libsharp, for distributed spherical harmonic transforms; employ the data utilities infrastructure provided by astropy to download the input templates and cache them when requested. At this stage we strive to maintain full compatibility with PySM 2, therefore we implement the exact same astrophysical emission models with the same naming scheme. In the extensive test suite we compare the output of each PySM 3 model with the results obtained by PySM 2.

astro-ph.IM↗

arXiv2021

A Generative Model of Galactic Dust Emission Using Variational Inference

Emission from the interstellar medium can be a significant contaminant of measurements of the intensity and polarization of the cosmic microwave background (CMB). For planning CMB observations, and for optimizing foreground-cleaning algorithms, a description of the statistical properties of such emission can be helpful. Here we examine a machine learning approach to inferring the statistical properties of dust from either observational data or physics-based simulations. In particular, we apply a type of neural network called a Variational Auto Encoder (VAE) to maps of the intensity of emission from interstellar dust as inferred from Planck sky maps and demonstrate its ability to a) simulate new samples with similar summary statistics as the training set, b) provide fits to emission maps withheld from the training set, and c) produce constrained realizations. We find VAEs are easier to train than another popular architecture: that of Generative Adversarial Networks (GANs), and are better-suited for use in Bayesian inference.

astro-ph.CO↗

arXiv2019

Removal of Galactic foregrounds for the Simons Observatory primordial gravitational wave search

Upcoming observations from the Simons Observatory have been projected to constrain the tensor-to-scalar ratio, $r$, at the level of $σ(r)=$0.003. Here we describe one of the forecasting algorithms for the Simons Observatory in more detail, based on cleaning CMB polarization maps using a parametric model. We present a new code to perform this end-to-end forecast, and explore the assumptions in greater detail. If spatial uniformity of the spectral energy distribution of synchrotron radiation and thermal dust emission is assumed over the region planned for observations, covering almost a fifth of the sky, a bias of order 1--3$σ$ in $r$ is projected for foreground models consistent with current data. We find that by masking the most contaminated regions of sky, or by adopting more parameters to describe the spatial variation in spectral index for synchrotron and dust, such a bias can be mitigated for the foreground models we consider. We also explore strategies for testing whether the cleaned CMB polarization maps contain residual foreground contamination, including cross-correlating with maps tracing the foregrounds. This method also has applications for other CMB polarization experiments.

astro-ph.CO↗

arXiv2018

Finding the chiral gravitational wave background of an axion-SU(2) inflationary model using CMB observations and laser interferometers

A detection of B-mode polarization of the Cosmic Microwave Background (CMB) anisotropies would confirm the presence of a primordial gravitational wave background (GWB). In the inflation paradigm this would be an unprecedented probe of the energy scale of inflation as it is directly proportional to the power spectrum of the GWB. However, similar tensor perturbations can be produced by the matter fields present during inflation, breaking this simple relationship. It is therefore important to be able to distinguish between different generation mechanisms of the GWB. In this paper, we analyse the detectability of a new axion-SU(2) gauge field model using its chiral, scale-dependent tensor spectrum. We forecast the detectability of the resulting CMB TB and EB cross-correlations by the LiteBIRD satellite, considering the effects of residual foregrounds, gravitational lensing, and for the first time assess the ability of such an experiment to jointly detect primordial TB and EB spectra and self-calibrate its polarimeter. We find that LiteBIRD will be able to detect the chiral signal for $r_*>0.03$ with $r_*$ denoting the tensor-to-scalar ratio at the peak scale, and that the maximum signal-to-noise for $r_*<0.07$ is $\sim 2$. We go on to consider an advanced stage of a LISA-like mission, and find that such experiments would complement CMB observations by providing sensitivity to GWB chirality on scales inaccessible to the CMB. We conclude that in order to use the CMB to distinguish this model from a conventional vacuum fluctuation model two-point statistics provide some power, but to achieve high statistical significance we would require higher order statistics which take advantage of the model's non-Gaussianity. On the other hand, in the case of a spectrum peaked at very small scales, inaccessible to the CMB, a highly significant detection could be made using space-based laser interferometers.

astro-ph.CO↗

arXiv2016

The Python Sky Model: software for simulating the Galactic microwave sky

We present a numerical code to simulate maps of Galactic emission in intensity and polarization at microwave frequencies, aiding in the design of Cosmic Microwave Background experiments. This Python code builds on existing efforts to simulate the sky by providing an easy-to-use interface and is based on publicly available data from the WMAP and Planck satellite missions. We simulate synchrotron, thermal dust, free-free, and anomalous microwave emission over the whole sky, in addition to the Cosmic Microwave Background, and include a set of alternative prescriptions for the frequency dependence of each component that are consistent with current data. We also present a prescription for adding small-scale realizations of these components at resolutions greater than current all-sky measurements. The code is available at https://github.com/bthorne93/PySM_public.

astro-ph.CO↗

arXiv2016

Simulated forecasts for primordial B-mode searches in ground-based experiments

Detecting the imprint of inflationary gravitational waves on the $B$-mode polarization of the Cosmic Microwave Background (CMB) is one of the main science cases for current and next-generation CMB experiments. In this work we explore some of the challenges that ground-based facilities will have to face in order to carry out this measurement in the presence of Galactic foregrounds and correlated atmospheric noise. We present forecasts for Stage-3 (S3) and planned Stage-4 (S4) experiments based on the analysis of simulated sky maps using a map-based Bayesian foreground cleaning method. Our results thus consistently propagate the uncertainties on foreground parameters such as spatially-varying spectral indices, as well as the bias on the measured tensor-to-scalar ratio $r$ caused by an incorrect modelling of the foregrounds. We find that S3 and S4-like experiments should be able to put constraints on $r$ of the order $σ(r)=(0.5-1.0)\times10^{-2}$ and $σ(r)=(0.5-1.0)\times10^{-3}$ respectively, assuming instrumental systematic effects are under control. We further study deviations from the fiducial foreground model, finding that, while the effects of a second polarized dust component would be minimal on both S3 and S4, a 2\% polarized anomalous dust emission (AME) component would be clearly detectable by Stage-4 experiments.

astro-ph.CO↗