Related papers: Extracting Axion String Network Parameters from Si…
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…
This paper explores a novel application of spherical convolutional neural networks (CNNs) to detect primordial non-Gaussianity in the cosmic microwave background (CMB), a key probe of inflationary dynamics. While effective, traditional…
Because cosmic superstrings generically form junctions and gauge theoretic strings typically do not, junctions may provide a signature to distinguish between cosmic superstrings and gauge theoretic cosmic strings. In cosmic microwave…
The evidence for a non-vanishing isotropic cosmic birefringence in recent analyses of the CMB data provides a tantalizing hint for new physics. Domain wall (DW) networks have recently been shown to generate an isotropic birefringence signal…
Cosmic Microwave Background (CMB) photons can undergo resonant conversion into axions in the presence of magnetized plasma distributed inside non-linear large-scale structure (LSS). This process leads to axion-induced patchy screening:…
We present an improved search for axion-like polarization oscillations in the cosmic microwave background (CMB) with observations from the Keck Array. An all-sky, temporally sinusoidal rotation of CMB polarization, equivalent to a…
Convolutional Neural Networks (CNNs) have recently been applied to cosmological fields -- weak lensing mass maps and galaxy maps. However, cosmological maps differ in several ways from the vast majority of images that CNNs have been tested…
Cosmological evolution of axionic string network is analyzed in terms of field-theoretic simulations in a box of 512^3 grids, which are the largest ever, using a new and more efficient identification scheme of global strings. The scaling…
Cosmic strings are a type of cosmic defect formed by a symmetry-breaking phase transition in the early universe. Individual strings would have gathered to build a network, and their dynamical motion would induce scalar--, vector-- and…
We propose a multi-scale edge-detection algorithm to search for the Gott-Kaiser-Stebbins imprints of a cosmic string (CS) network on the Cosmic Microwave Background (CMB) anisotropies. Curvelet decomposition and extended Canny algorithm are…
What happens when a black box (neural network) meets a black box (simulation of the Universe)? Recent work has shown that convolutional neural networks (CNNs) can infer cosmological parameters from the matter density field in the presence…
Deep learning is a powerful analysis technique that has recently been proposed as a method to constrain cosmological parameters from weak lensing mass maps. Due to its ability to learn relevant features from the data, it is able to extract…
Axion-like particles are dark matter candidates motivated by the Peccei-Quinn mechanism and also occur in effective field theories where their masses and photon couplings are independent. We estimate the dispersion of circularly polarized…
Recent advances in quantum sensing and computational technologies indicate the possibility of improving the precision of measurements aimed at detecting cosmological particles and weakly interacting massive particles using various qubit…
We explore the use of random forest and gradient boosting, two powerful tree-based machine learning algorithms, for the detection of cosmic strings in maps of the cosmic microwave background (CMB), through their unique Gott-Kaiser-Stebbins…
Low energy string theory predicts the existence of an axion field which can lead to cosmic birefringence. We solve the electromagnetic wave equations in the presence of such an axion and a dilaton field in order to determine their effect on…
Spiking neural networks (SNNs) are receiving increased attention because they mimic synaptic connections in biological systems and produce spike trains, which can be approximated by binary values for computational efficiency. Recently, the…
We perform the lattice simulation to estimate the axion dark matter abundance radiated from the global cosmic strings in the post-inflationary scenario. The independent numerical confirmation on the recently observed logarithmic growth in…
Reliable extraction of cosmological information from observed cosmic microwave background (CMB) maps may require removal of strongly foreground contaminated regions from the analysis. In this article, we employ an artificial neural network…
Spiking neural networks (SNNs) can be used in low-power and embedded systems (such as emerging neuromorphic chips) due to their event-based nature. Also, they have the advantage of low computation cost in contrast to conventional artificial…