Fast and precise map-making for massively multi-detector CMB experiments

Future cosmic microwave background (CMB) polarisation experiments aim to measure an unprecedentedly small signal - the primordial gravity wave component of the polarisation field B-mode. To achieve this, they will analyse huge datasets, involving years worth of time-ordered data (TOD) from massively multi-detector focal planes. This creates the need for fast and precise methods to complement the M-L approach in analysis pipelines. In this paper, we investigate fast map-making methods as applied to long duration, massively multi-detector, ground-based experiments, in the context of the search for B-modes. We focus on two alternative map-making approaches: destriping and TOD filtering, comparing their performance on simulated multi-detector polarisation data. We have written an optimised, parallel destriping code, the DEStriping CARTographer DESCART, that is generalised for massive focal planes, including the potential effect of cross-correlated TOD 1/f noise. We also determine the scaling of computing time for destriping as applied to a simulated full-season data-set for a realistic experiment. We find that destriping can out-perform filtering in estimating both the large-scale E and B-mode angular power spectra. In particular, filtering can produce significant spurious B-mode power via EB mixing. Whilst this can be removed, it contributes to the variance of B-mode bandpower estimates at scales near the primordial B-mode peak. For the experimental configuration we simulate, this has an effect on the possible detection significance for primordial B-modes. Destriping is a viable alternative fast method to the full M-L approach that does not cause the problems associated with filtering, and is flexible enough to fit into both M-L and Monte-Carlo pseudo-Cl pipelines.Comment: 16 pages, 14 figures. MNRAS accepted. Typos corrected and computing time/memory requirement orders-of-magnitude numbers in section 4 replaced by precise number

Impact of modulation on CMB B-mode polarization experiments

We investigate the impact of both slow and fast polarization modulation strategies on the science return of upcoming ground-based experiments aimed at measuring the B-mode polarization of the CMB. Using simulations of the Clover experiment, we compare the ability of modulated and un-modulated observations to recover the signature of gravitational waves in the polarized CMB sky in the presence of a number of anticipated systematic effects. The general expectations that fast modulation is helpful in mitigating low-frequency detector noise, and that the additional redundancy in the projection of the instrument's polarization sensitivity directions onto the sky when modulating reduces the impact of instrumental polarization, are borne out by our simulations. Neither low-frequency polarized atmospheric fluctuations nor systematic errors in the polarization sensitivity directions are mitigated by modulation. Additionally, we find no significant reduction in the effect of pointing errors by modulation. For a Clover-like experiment, pointing jitter should be negligible but any systematic mis-calibration of the polarization coordinate reference system results in significant E-B mixing on all angular scales and will require careful control. We also stress the importance of combining data from multiple detectors in order to remove the effects of common-mode systematics (such as 1/f atmospheric noise) on the measured polarization signal. Finally we compare the performance of our simulated experiment with the predicted performance from a Fisher analysis. We find good agreement between the Fisher predictions and the simulations except for the very largest scales where the power spectrum estimator we have used introduces additional variance to the B-mode signal recovered from our simulations.Comment: Replaced with version accepted by MNRAS. Analysis of half-wave plate systematic (differential transmittance) adde

A Backend Framework for the Efficient Management of Power System Measurements

Increased adoption and deployment of phasor measurement units (PMU) has provided valuable fine-grained data over the grid. Analysis over these data can provide insight into the health of the grid, thereby improving control over operations. Realizing this data-driven control, however, requires validating, processing and storing massive amounts of PMU data. This paper describes a PMU data management system that supports input from multiple PMU data streams, features an event-detection algorithm, and provides an efficient method for retrieving archival data. The event-detection algorithm rapidly correlates multiple PMU data streams, providing details on events occurring within the power system. The event-detection algorithm feeds into a visualization component, allowing operators to recognize events as they occur. The indexing and data retrieval mechanism facilitates fast access to archived PMU data. Using this method, we achieved over 30x speedup for queries with high selectivity. With the development of these two components, we have developed a system that allows efficient analysis of multiple time-aligned PMU data streams.Comment: Published in Electric Power Systems Research (2016), not available ye

ModDrop: adaptive multi-modal gesture recognition

We present a method for gesture detection and localisation based on multi-scale and multi-modal deep learning. Each visual modality captures spatial information at a particular spatial scale (such as motion of the upper body or a hand), and the whole system operates at three temporal scales. Key to our technique is a training strategy which exploits: i) careful initialization of individual modalities; and ii) gradual fusion involving random dropping of separate channels (dubbed ModDrop) for learning cross-modality correlations while preserving uniqueness of each modality-specific representation. We present experiments on the ChaLearn 2014 Looking at People Challenge gesture recognition track, in which we placed first out of 17 teams. Fusing multiple modalities at several spatial and temporal scales leads to a significant increase in recognition rates, allowing the model to compensate for errors of the individual classifiers as well as noise in the separate channels. Futhermore, the proposed ModDrop training technique ensures robustness of the classifier to missing signals in one or several channels to produce meaningful predictions from any number of available modalities. In addition, we demonstrate the applicability of the proposed fusion scheme to modalities of arbitrary nature by experiments on the same dataset augmented with audio.Comment: 14 pages, 7 figure

Random Beamforming over Correlated Fading Channels

We study a multiple-input multiple-output (MIMO) multiple access channel (MAC) from several multi-antenna transmitters to a multi-antenna receiver. The fading channels between the transmitters and the receiver are modeled by random matrices, composed of independent column vectors with zero mean and different covariance matrices. Each transmitter is assumed to send multiple data streams with a random precoding matrix extracted from a Haar-distributed matrix. For this general channel model, we derive deterministic approximations of the normalized mutual information, the normalized sum-rate with minimum-mean-square-error (MMSE) detection and the signal-to-interference-plus-noise-ratio (SINR) of the MMSE decoder, which become arbitrarily tight as all system parameters grow infinitely large at the same speed. In addition, we derive the asymptotically optimal power allocation under individual or sum-power constraints. Our results allow us to tackle the problem of optimal stream control in interference channels which would be intractable in any finite setting. Numerical results corroborate our analysis and verify its accuracy for realistic system dimensions. Moreover, the techniques applied in this paper constitute a novel contribution to the field of large random matrix theory and could be used to study even more involved channel models.Comment: 35 pages, 5 figure

Optimal combination of signals from co-located gravitational wave interferometers for use in searches for a stochastic background

This article derives an optimal (i.e., unbiased, minimum variance) estimator for the pseudo-detector strain for a pair of co-located gravitational wave interferometers (such as the pair of LIGO interferometers at its Hanford Observatory), allowing for possible instrumental correlations between the two detectors. The technique is robust and does not involve any assumptions or approximations regarding the relative strength of gravitational wave signals in the detector pair with respect to other sources of correlated instrumental or environmental noise. An expression is given for the effective power spectral density of the combined noise in the pseudo-detector. This can then be introduced into the standard optimal Wiener filter used to cross-correlate detector data streams in order to obtain an optimal estimate of the stochastic gravitational wave background. In addition, a dual to the optimal estimate of strain is derived. This dual is constructed to contain no gravitational wave signature and can thus be used as on "off-source" measurement to test algorithms used in the "on-source" observation.Comment: 14 pages, 4 figures, submitted to Physical Review D Resubmitted after editing paper in response to referee comments. Removed appendices A, B and edited text accordingly. Improved legibility of figures. Corrected several references. Corrected reference to science run number (S1 vs. S2) in text and figure caption