arXiv subjects

Oscar F. Hernández

Publications and source records attributed to Oscar F. Hernández.

7 recordsLinked to original sources

arXiv2021

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

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.

astro-ph.CO↗

arXiv2021

Information Theoretic Bounds on Cosmic String Detection in CMB Maps with Noise

We use a convolutional neural network (CNN) to study cosmic string detection in cosmic microwave background (CMB) flat sky maps with Nambu-Goto strings. On noiseless maps we can measure string tensions down to order $10^{-9}$, however when noise is included we are unable to measure string tensions below $10^{-7}$. Motivated by this impasse, we derive an information theoretic bound on the detection of the cosmic string tension $Gμ$ from CMB maps. In particular we bound the information entropy of the posterior distribution of $Gμ$ in terms of the resolution, noise level and total survey area of the CMB map. We evaluate these bounds for the ACT, SPT-3G, Simons Observatory, Cosmic Origins Explorer, and CMB-S4 experiments. These bounds cannot be saturated by any method.

astro-ph.CO↗

arXiv2019

A Convolutional Neural Network For Cosmic String Detection in CMB Temperature Maps

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 the network resulted in the accurate determination both of string locations and string tension for sky maps having strings with string tension as low as $Gμ=5\times10^{-9}$. The code is publicly available online. Though we trained the network with a long straight string toy model, we show the network performs well with realistic Nambu-Goto simulations.

astro-ph.CO↗

arXiv2019

Inferring Cosmic String Tension through the Neural Network Prediction of String Locations in CMB Maps

In previous work, we constructed a convolutional neural network used to estimate the location of cosmic strings in simulated cosmic microwave background temperature anisotropy maps. We derived a connection between the estimates of cosmic string locations by this neural network and the posterior probability distribution of the cosmic string tension $Gμ$. Here, we significantly improve the calculation of the posterior distribution of the string tension $Gμ$. We also improve our previous plain convolutional neural network by using residual networks. We apply our new neural network and posterior calculation method to maps from the same simulation used in our previous work and quantify the improvement.

astro-ph.CO↗

arXiv2017

A Bayesian Framework for Cosmic String Searches in CMB Maps

There exists various proposals to detect cosmic strings from Cosmic Microwave Background (CMB) or 21 cm temperature maps. Current proposals do not aim to find the location of strings on sky maps, all of these approaches can be thought of as a statistic on a sky map. We propose a Bayesian interpretation of cosmic string detection and within that framework, we derive a connection between estimates of cosmic string locations and cosmic string tension $Gμ$. We use this Bayesian framework to develop a machine learning framework for detecting strings from sky maps and outline how to implement this framework with neural networks. The neural network we trained was able to detect and locate cosmic strings on noiseless CMB temperature map down to a string tension of $Gμ=5 \times10^{-9}$ and when analyzing a CMB temperature map that does not contain strings, the neural network gives a 0.95 probability that $Gμ\leq2.3\times10^{-9}$.

astro-ph.CO↗

arXiv2017

Disruption of Cosmic String Wakes by Gaussian Fluctuations

We study the stability of cosmic string wakes against the disruption by the dominant Gaussian fluctuations which are present in cosmological models. We find that for a string tension given by $G μ= 10^{-7}$ wakes remain locally stable until a redshift of $z = 6$, and for a value of $G μ= 10^{-14}$ they are stable beyond a redshift of $z = 20$. We study a global stability criterion which shows that wakes created by strings at times after $t_{eq}$ are identifiable up to the present time, independent of the value of $G μ$. Taking into account our criteria it is possible to develop strategies to search for the distinctive position space signals in cosmological maps which are induced by wakes.

astro-ph.CO↗

arXiv2017

Neutrino Masses, Scale-Dependent Growth, and Redshift-Space Distortions

Massive neutrinos leave a unique signature in the large scale clustering of matter. We investigate the wavenumber dependence of the growth factor arising from neutrino masses and use a Fisher analysis to determine the aspects of a galaxy survey needed to measure this scale dependence.

astro-ph.CO↗