arXiv subjects

B. Hensley

Publications and source records attributed to B. Hensley.

26 records · Page 2Linked to original sources

arXiv2022

BeyondPlanck X. Bandpass and beam leakage corrections

We discuss the treatment of bandpass and beam leakage corrections in the Bayesian BeyondPlanck CMB analysis pipeline as applied to the Planck LFI measurements. As a preparatory step, we first apply three corrections to the nominal LFI bandpass profiles including removal of a known systematic effect in the ground measuring equipment at 61 GHz; smoothing of standing wave ripples; and edge regularization. The main net impact of these modifications is an overall shift in the 70 GHz bandpass of +0.6 GHz; we argue that any analysis of LFI data products, either from Planck or BeyondPlanck, should use these new bandpasses. In addition, we fit a single free bandpass parameter for each radiometer of the form $Δ_i = Δ_0 + δ_i$, where $Δ_0$ represents an absolute frequency shift per frequency band and $δ_i$ is a relative shift per detector. The absolute correction is only fitted at 30 GHz with a full $χ^2$-based likelihood, resulting in a correction of $Δ_{30}=0.24\pm0.03\,$GHz. The relative corrections are fitted using a spurious map approach, fundamentally similar to the method pioneered by the WMAP team, but without introducing many additional degrees of freedom. All bandpass parameters are sampled using a standard Metropolis sampler within the main BeyondPlanck Gibbs chain, and bandpass uncertainties are thus propagated to all other data products in the analysis. In total, we find that our bandpass model significantly reduces leakage effects. For beam leakage corrections, we adopt the official Planck LFI beam estimates without additional degrees of freedom, and only marginalize over the underlying sky model. We note that this is the first time leakage from beam mismatch has been included for Planck LFI maps.

astro-ph.CO↗

arXiv2022

BeyondPlanck III. Commander3

We describe the computational infrastructure for end-to-end Bayesian CMB analysis implemented by the BeyondPlanck collaboration. This code is called commander3, and provides a statistically consistent framework for global analysis of CMB and microwave observations, and may be useful for a wide range of legacy, current, and future experiments. The paper has three main goals. Firstly, we provide a high-level overview of the existing code base, aiming to guide readers who wish to extend and adapt the code according to their own needs, or to reimplement it from scratch in a different programming language. Secondly, we discuss some critical computational challenges that arise within any global CMB analysis framework, for instance in-memory compression of time-ordered data, FFT optimization, and parallelization and load-balancing. Thirdly, we quantify the CPU and RAM requirements for the current BeyondPlanck analysis, and find that a total of 1.5 TB of RAM is required for efficient analysis, and the total cost of a full Gibbs sample is 170 CPU-hrs, including both low-level processing and high-level component separation, which is well within the capabilities of current low-cost computing facilities. The existing code base is made publicly available under a GNU General Public Library (GPL) license.

astro-ph.CO↗

arXiv2022

BeyondPlanck VIII. Efficient Sidelobe Convolution and Correction through Spin Harmonics

We introduce a new formulation of the Conviqt convolution algorithm in terms of spin harmonics, and apply this to the problem of sidelobe correction for BeyondPlanck, the first end-to-end Bayesian Gibbs sampling framework for CMB analysis. We compare our implementation to the previous Planck LevelS implementation, and find good agreement between the two codes in terms of accuracy, but with a speed-up reaching a factor of 3--10, depending on the frequency bandlimits, $l_{\textrm{max}}$ and $m_{\textrm{max}}$. The new algorithm is significantly simpler to implement and maintain, since all low-level calculations are handled through an external spherical harmonic transform library. We find that our mean sidelobe estimates for Planck LFI agree well with previous efforts. Additionally, we present novel sidelobe rms maps that quantify the uncertainty in the sidelobe corrections due to variations in the sky model.

astro-ph.CO↗

arXiv2020

BeyondPlanck II. CMB map-making through Gibbs sampling

We present a Gibbs sampling solution to the map-making problem for CMB measurements, building on existing destriping methodology. Gibbs sampling breaks the computationally heavy destriping problem into two separate steps; noise filtering and map binning. Considered as two separate steps, both are computationally much cheaper than solving the combined problem. This provides a huge performance benefit as compared to traditional methods, and allows us for the first time to bring the destriping baseline length to a single sample. We apply the Gibbs procedure to simulated Planck 30 GHz data. We find that gaps in the time-ordered data are handled efficiently by filling them with simulated noise as part of the Gibbs process. The Gibbs procedure yields a chain of map samples, from which we may compute the posterior mean as a best-estimate map. The variation in the chain provides information on the correlated residual noise, without need to construct a full noise covariance matrix. However, if only a single maximum-likelihood frequency map estimate is required, we find that traditional conjugate gradient solvers converge much faster than a Gibbs sampler in terms of total number of iterations. The conceptual advantages of the Gibbs sampling approach lies in statistically well-defined error propagation and systematic error correction, and this methodology forms the conceptual basis for the map-making algorithm employed in the BeyondPlanck framework, which implements the first end-to-end Bayesian analysis pipeline for CMB observations.

astro-ph.CO↗

arXiv2019

PICO: Probe of Inflation and Cosmic Origins

The Probe of Inflation and Cosmic Origins (PICO) is a proposed probe-scale space mission consisting of an imaging polarimeter operating in frequency bands between 20 and 800 GHz. We describe the science achievable by PICO, which has sensitivity equivalent to more than 3300 Planck missions, the technical implementation, the schedule and cost.

astro-ph.IM↗

arXiv2019

Astro2020 APC White Paper: The need for better tools to design future CMB experiments

This white paper addresses key challenges for the design of next-decade Cosmic Microwave Background (CMB) experiments, and for assessing their capability to extract cosmological information from CMB polarization. We focus here on the challenges posed by foreground emission, CMB lensing, and instrumental systematics to detect the signal that arises from gravitational waves sourced by inflation and parameterized by $r$, at the level of $r \sim 10^{-3}$ or lower, as proposed for future observational efforts. We argue that more accurate and robust analysis and simulation tools are required for these experiments to realize their promise. We are optimistic that the capability to simulate the joint impact of foregrounds, CMB lensing, and systematics can be developed to the level necessary to support the design of a space mission at $r \sim 10^{-4}$ in a few years. We make the case here for supporting such work. Although ground-based efforts present additional challenges (e.g., atmosphere, ground pickup), which are not addressed here, they would also benefit from these improved simulation capabilities.

astro-ph.CO↗

arXiv2018

The State-of-Play of Anomalous Microwave Emission (AME) Research

Anomalous Microwave Emission (AME) is a component of diffuse Galactic radiation observed at frequencies in the range $\approx 10$-60 GHz. AME was first detected in 1996 and recognised as an additional component of emission in 1997. Since then, AME has been observed by a range of experiments and in a variety of environments. AME is spatially correlated with far-IR thermal dust emission but cannot be explained by synchrotron or free-free emission mechanisms, and is far in excess of the emission contributed by thermal dust emission with the power-law opacity consistent with the observed emission at sub-mm wavelengths. Polarization observations have shown that AME is very weakly polarized ($\lesssim 1$%). The most natural explanation for AME is rotational emission from ultra-small dust grains ("spinning dust"), first postulated in 1957. Magnetic dipole radiation from thermal fluctuations in the magnetization of magnetic grain materials may also be contributing to the AME, particularly at higher frequencies ($\gtrsim 50$ GHz). AME is also an important foreground for Cosmic Microwave Background analyses. This paper presents a review and the current state-of-play in AME research, which was discussed in an AME workshop held at ESTEC, The Netherlands, June 2016.

astro-ph.GA↗

arXiv2008

Towards Real-time Classification of Astronomical Transients

Exploration of time domain is now a vibrant area of research in astronomy, driven by the advent of digital synoptic sky surveys. While panoramic surveys can detect variable or transient events, typically some follow-up observations are needed; for short-lived phenomena, a rapid response is essential. Ability to automatically classify and prioritize transient events for follow-up studies becomes critical as the data rates increase. We have been developing such methods using the data streams from the Palomar-Quest survey, the Catalina Sky Survey and others, using the VOEventNet framework. The goal is to automatically classify transient events, using the new measurements, combined with archival data (previous and multi-wavelength measurements), and contextual information (e.g., Galactic or ecliptic latitude, presence of a possible host galaxy nearby, etc.); and to iterate them dynamically as the follow-up data come in (e.g., light curves or colors). We have been investigating Bayesian methodologies for classification, as well as discriminated follow-up to optimize the use of available resources, including Naive Bayesian approach, and the non-parametric Gaussian process regression. We will also be deploying variants of the traditional machine learning techniques such as Neural Nets and Support Vector Machines on datasets of reliably classified transients as they build up.

astro-ph↗