Searching for cosmic strings in CMB anisotropy maps using wavelets and curvelets

Abstract

We use wavelet and curvelet transforms to extract signals of cosmic strings from simulated cosmic microwave background (CMB) temperature anisotropy maps, and to study the limits on the cosmic string tension which various ongoing CMB temperature anisotropy experiments will be able to achieve. We construct sky maps with size and angular resolution corresponding to various experiments. These maps contain the signals of a scaling solution of long string segments with a given string tension Gµ, the contribution of the dominant Gaussian primordial cosmological fluctuations, and pixel by pixel white noise with an amplitude corresponding to the instrumental noise of the various experiments. In the case that we include white noise, we find that using curvelets we obtain lower bounds on the string tension than with wavelets. For maps with Planck specification, we obtain bounds comparable to what was obtained by the Planck collaboration [1]. Experiments with better angular resolution such as the South Pole Telescope third generation (SPT-3G) survey will be able to yield stronger limits. For maps with a specification of SPT-3G we find that string signals will be visible down to a string tension of Gµ = 1.4 × 10 −7 .

Information theoretic bounds on cosmic string detection in CMB maps with noise

“…Recently there has been a proliferation of methods (Hergt et al 2017;McEwen et al 2017;Vafaei Sadr et al 2018a;Ciuca & Hernández 2017 for the inference of the cosmic string tension Gµ from the Gott-Kaiser-Stebbings (GKS) effect (Gott 1985;Kaiser & Stebbins 1984) in cosmic microwave background (CMB) temperature anisotropy maps. In Ciuca & Hernández (2017; we argued for using convolutional neural networks (CNN) to find the locations of strings in the sky and then inferred the string tension using Bayesian statistics from those estimates.…”

Inferring cosmic string tension through the neural network prediction of string locations in CMB maps

“…We used numerically generated CMB temperature maps with and without cosmic strings. The data set was obtained with the same long string analytical model (Perivolaropoulos 1993) used in and other previous studies of cosmic string detection in CMB maps (Amsel et al 2008;Stewart & Brandenberger 2009;Hergt et al 2017). We used the PyTorch environment (pytorch.org) for machine learning and optimization algorithms, and we trained the model on a Tesla K80 GPU for 12 hours in total.…”

“…Movahed & Khosravi (2011) studied the simulated maps with a level crossing statistic and claimed that strings could be detected in noiseless maps when Gµ > 4 × 10 −9 . In another example Hergt et al (2017) used wavelets, and curvelets and found that strings could be detected down to a string tension of Gµ = 1.4 × 10 −7 at the 95% CL if the contribution of noise was not more than 1.6µK (see their table III).…”

A convolutional neural network for cosmic string detection in CMB temperature maps

“…However as shown in Ciuca & Hernández (2017) these edges do not necessarily correspond to the string locations. References Hergt et al (2017); McEwen et al (2017) used curvelet transforms to analyse simulated CMB temperature maps with noise, and most recently Vafaei Sadr et al (2018) has combined both Canny and curvelets to place a detection limit of Gµ ∼ 10 −7 on maps with realistic noise.…”