2019
DOI: 10.1093/mnras/stz491
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A convolutional neural network for cosmic string detection in CMB temperature maps

Abstract: We present in detail the convolutional neural network used in our previous work to detect cosmic strings in cosmic microwave background (CMB) temperature anisotropy maps. By training this neural network on numerically generated CMB temperature maps, with and without cosmic strings, the network can produce prediction maps that locate the position of the cosmic strings and provide a probabilistic estimate of the value of the string tension Gµ. Supplying noiseless simulations of CMB maps with arcmin resolution to… Show more

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Cited by 18 publications
(16 citation statements)
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“…We trained the neural network presented in ref. [36] on numerically generated CMB temperature maps with and without cosmic strings. This dataset was obtained with the same long string analytical model [35] used by previous studies of cosmic string detection in CMB maps [32][33][34].…”
Section: Results: Neural Network Predictions For String Locationsmentioning
confidence: 99%
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“…We trained the neural network presented in ref. [36] on numerically generated CMB temperature maps with and without cosmic strings. This dataset was obtained with the same long string analytical model [35] used by previous studies of cosmic string detection in CMB maps [32][33][34].…”
Section: Results: Neural Network Predictions For String Locationsmentioning
confidence: 99%
“…We had access to the C code used by [31] and [32] to perform the CMB simulations and then analyzed them with the Canny algorithm. We rewrote significant parts of the code both to generate maps and reconstruct the predictions of the Canny algorithm [36]. We found that without noise Canny can only distinguish between strings and no strings for a Gµ 10 −7 [36] and when noise is included this drops to Gµ 10 −6 .…”
Section: Results: Neural Network Predictions For String Locationsmentioning
confidence: 99%
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“…Finally, we note that deep learning may be amenable to searching for dark matter vortices in other observational windows, analogous to searches for cosmic strings in the cosmic microwave background [81,82] and 21cm [83,84]. We leave this, and the development of an unsupervised approach, to future work.…”
Section: Discussionmentioning
confidence: 99%
“…-cutting the sky into 'flattened' patches -is not technically necessary but is often adopted in the literature [42][43][44][45][46]. Historically it was used since CMB analyzers using wavelets or machine learning could not cope with spherical coordinates [37][38][39][47][48][49]. This technicality has been overcome in the neutral network analysis [50,51], but we will stick to the flat patches for simplicity in this analysis.…”
Section: Jhep11(2021)158mentioning
confidence: 99%