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

Ciuca, Razvan; Hernández, Oscar F.

doi:10.1093/mnras/stz3551

Cited by 12 publications

(7 citation statements)

References 39 publications

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“…This method, used in Ref. [97] for a different purpose, could help with the reconstruction on scales dominated by noise. On the other hand, the noise levels where significant gains seem possible are much lower than those of currently planned CMB surveys, meaning that optimization in that very low noise regime may not lead to much practical improvement with forthcoming data.…”

Section: Discussionmentioning

confidence: 99%

Reconstructing Patchy Reionization with Deep Learning

Guzman,

Meyers

2021

Preprint

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The precision anticipated from next-generation cosmic microwave background (CMB) surveys will create opportunities for characteristically new insights into cosmology. Secondary anisotropies of the CMB will have an increased importance in forthcoming surveys, due both to the cosmological information they encode and the role they play in obscuring our view of the primary fluctuations. Quadratic estimators have become the standard tools for reconstructing the fields that distort the primary CMB and produce secondary anisotropies. While successful for lensing reconstruction with current data, quadratic estimators will be sub-optimal for the reconstruction of lensing and other effects at the expected sensitivity of the upcoming CMB surveys. In this paper we describe a convolutional neural network, ResUNet-CMB, that is capable of the simultaneous reconstruction of two sources of secondary CMB anisotropies, gravitational lensing and patchy reionization. We show that the ResUNet-CMB network significantly outperforms the quadratic estimator at low noise levels and is not subject to the lensing-induced bias on the patchy reionization reconstruction that would be present with a straightforward application of the quadratic estimator.

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Section: Discussionmentioning

confidence: 99%

Reconstructing Patchy Reionization with Deep Learning

Guzman,

Meyers

2021

Preprint

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“…Vafaei Sadr et al (2017) also used various combinations of image processing tools and statistical measures, followed by tree-based learning algorithms (Vafaei Sadr et al 2018) to forecast the detectability limit of different observational scenarios. There have been relatively recent neural network-based algorithms to locate the position of the strings for ideal noiseless experiments (Ciuca & Hernández 2017) or to put information-theoretic bounds on the CS tension for various observational setups (Ciuca & Hernández 2020). There are also Gµ measurements and forecasts from other observational routes.…”

Section: )mentioning

confidence: 99%

Planck Limits on Cosmic String Tension Using Machine Learning

Torki,

Hajizadeh,

Farhang

et al. 2021

Preprint

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We develop two parallel machine-learning pipelines to estimate the contribution of cosmic strings (CSs), conveniently encoded in their tension (Gµ), to the anisotropies of the cosmic microwave background radiation observed by Planck. The first approach is tree-based and feeds on certain map features derived by image processing and statistical tools. The second uses convolutional neural network with the goal to explore possible non-trivial features of the CS imprints. The two pipelines are trained on Planck simulations and when applied to Planck SMICA map yield the 3σ upper bound of Gµ 8.6 × 10 −7 . We also train and apply the pipelines to make forecasts for futuristic CMB-S4-like surveys and conservatively find their minimum detectable tension to be Gµ min ∼ 1.9 × 10 −7 .

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“…Finally, there has been much recent research to develop wavelets and machine learning as more sensitive probes of cosmic strings in the CMB (Hergt et al 2017;McEwen et al 2017;Vafaei Sadr et al 2018a,b;Ciuca & Hernández 2017, 2020. If indeed the SGWB seen by NANOGrav is due to string with 10 −11 𝐺 𝜇 10 −7 , the work in Ciuca & Hernández (2020 has show that there is enough information noisy in CMB maps for strings to be detected by machine learning methods.…”

Section: A Review Of Current Limits On the Cosmic String Tensionmentioning

confidence: 99%

The Global 21 cm Signal of a Network of Cosmic String Wakes

Hernández

2021

Preprint

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In previous works we discussed the 21 cm signature of a single cosmic string wake. However the 21 cm brightness temperature is influenced by a network of cosmic string wakes, and not one single wake. In this work we consider the signal from a network of wakes laid down during the matter era. We also improve on the previous calculation of a single wake signature. Finally we calculate the enhancement of the global 21 cm brightness temperature due to a network of wakes and discuss its affects of the signal measured in the Wouthuysen-Field absorption trough. We estimated that for string tensions between 10 −8 to 10 −7 there would be between a 10% to a factor 2 enhancement in the signal.

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

Cited by 12 publications

References 39 publications

Reconstructing Patchy Reionization with Deep Learning

Reconstructing Patchy Reionization with Deep Learning

Planck Limits on Cosmic String Tension Using Machine Learning

The Global 21 cm Signal of a Network of Cosmic String Wakes

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