Foreground model recognition through Neural Networks for CMB B-mode observations

• URL: http://arxiv.org/abs/2003.02278v2
• Date: Thu, 11 Jun 2020 14:42:51 GMT
• Title: Foreground model recognition through Neural Networks for CMB B-mode observations
• Authors: Farida Farsian, Nicoletta Krachmalnicoff, Carlo Baccigalupi
• Abstract summary: In particular, we have focused our analysis on low frequency foregrounds relevant for polarization observation. We have implemented and tested our approach on a set of simulated maps corresponding to the frequency coverage and sensitivity represented by future satellite and low frequency ground based probes. The NN efficiency in recognizing the right parametrization of foreground emission in different sky regions reaches an accuracy of about $90%$.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work we present a Neural Network (NN) algorithm for the identification of the appropriate parametrization of diffuse polarized Galactic emissions in the context of Cosmic Microwave Background (CMB) $B$-mode multi-frequency observations. In particular, we have focused our analysis on low frequency foregrounds relevant for polarization observation: namely Galactic Synchrotron and Anomalous Microwave Emission (AME). We have implemented and tested our approach on a set of simulated maps corresponding to the frequency coverage and sensitivity represented by future satellite and low frequency ground based probes. The NN efficiency in recognizing the right parametrization of foreground emission in different sky regions reaches an accuracy of about $90\%$. We have compared this performance with the $\chi^{2}$ information following parametric foreground estimation using multi-frequency fitting, and quantify the gain provided by a NN approach. Our results show the relevance of model recognition in CMB $B$-mode observations, and highlight the exploitation of dedicated procedures to this purpose.

Related papers

• End-to-End Training of Neural Networks for Automotive Radar Interference Mitigation [9.865041274657823]
  We propose a new method for training neural networks (NNs) for frequency modulated continuous wave (WFMC) radar mutual interference mitigation. Instead of training NNs to regress from interfered to clean radar signals as in previous work, we train NNs directly on object detection maps. We do so by performing a continuous relaxation of the cell-averaging constant false alarm rate (CA-CFAR) peak detector, which is a well-established algorithm for object detection using radar.
  arXiv  Detail & Related papers  (2023-12-15T13:47:16Z)
• Semantic Segmentation of Radar Detections using Convolutions on Point Clouds [59.45414406974091]
  We introduce a deep-learning based method to convolve radar detections into point clouds. We adapt this algorithm to radar-specific properties through distance-dependent clustering and pre-processing of input point clouds. Our network outperforms state-of-the-art approaches that are based on PointNet++ on the task of semantic segmentation of radar point clouds.
  arXiv  Detail & Related papers  (2023-05-22T07:09:35Z)
• An Anomaly Detection Method for Satellites Using Monte Carlo Dropout [7.848121055546167]
  We present a tractable approximation for BNN based on the Monte Carlo (MC) dropout method for capturing the uncertainty in the satellite telemetry time series. Our proposed time series AD approach outperforms the existing methods from both prediction accuracy and AD perspectives.
  arXiv  Detail & Related papers  (2022-11-27T21:12:26Z)
• Toward deep-learning-assisted spectrally-resolved imaging of magnetic noise [52.77024349608834]
  We implement a deep neural network to efficiently reconstruct the spectral density of the underlying fluctuating magnetic field. These results create opportunities for the application of machine-learning methods to color-center-based nanoscale sensing and imaging.
  arXiv  Detail & Related papers  (2022-08-01T19:18:26Z)
• Inferring Structural Parameters of Low-Surface-Brightness-Galaxies with Uncertainty Quantification using Bayesian Neural Networks [70.80563014913676]
  We show that a Bayesian Neural Network (BNN) can be used for the inference, with uncertainty, of such parameters from simulated low-surface-brightness galaxy images. Compared to traditional profile-fitting methods, we show that the uncertainties obtained using BNNs are comparable in magnitude, well-calibrated, and the point estimates of the parameters are closer to the true values.
  arXiv  Detail & Related papers  (2022-07-07T17:55:26Z)
• Constraining dark matter annihilation with cosmic ray antiprotons using neural networks [0.0]
  We present a new method that significantly accelerates simulations of secondary and dark matter Galactic cosmic ray antiprotons. We identify importance sampling as particularly suitable for ensuring that the network is only evaluated in well-trained parameter regions. The fully trained networks are released as DarkRayNet together with this work and achieve a speed-up of the runtime by at least two orders of magnitude compared to conventional approaches.
  arXiv  Detail & Related papers  (2021-07-26T18:00:04Z)
• Real-time gravitational-wave science with neural posterior estimation [64.67121167063696]
  We demonstrate unprecedented accuracy for rapid gravitational-wave parameter estimation with deep learning. We analyze eight gravitational-wave events from the first LIGO-Virgo Gravitational-Wave Transient Catalog. We find very close quantitative agreement with standard inference codes, but with inference times reduced from O(day) to a minute per event.
  arXiv  Detail & Related papers  (2021-06-23T18:00:05Z)
• Deep Networks for Direction-of-Arrival Estimation in Low SNR [89.45026632977456]
  We introduce a Convolutional Neural Network (CNN) that is trained from mutli-channel data of the true array manifold matrix. We train a CNN in the low-SNR regime to predict DoAs across all SNRs. Our robust solution can be applied in several fields, ranging from wireless array sensors to acoustic microphones or sonars.
  arXiv  Detail & Related papers  (2020-11-17T12:52:18Z)
• Gravitational-wave selection effects using neural-network classifiers [0.0]
  We train a series of neural-network classifiers to predict the LIGO/Virgo detectability of gravitational-wave signals from compact-binary mergers. We include the effect of spin precession, higher-order modes, and multiple detectors. Our approach is ready to be used in conjunction with full pipeline injections, thus paving the way toward including actual distributions of astrophysical and noise triggers into gravitational-wave population analyses.
  arXiv  Detail & Related papers  (2020-07-13T18:00:04Z)
• Graph Neural Networks for Massive MIMO Detection [8.516590865173407]
  We learn a message-passing solution for the inference task of massive multiple-input multiple-output (MIMO) detection in wireless communication. We adopt a graphical model based on the Markov random field (MRF) where belief propagation (BP) yields poor results when it assumes a uniform prior over the transmitted symbols.
  arXiv  Detail & Related papers  (2020-07-11T07:34:56Z)
• Multi-Tones' Phase Coding (MTPC) of Interaural Time Difference by Spiking Neural Network [68.43026108936029]
  We propose a pure spiking neural network (SNN) based computational model for precise sound localization in the noisy real-world environment. We implement this algorithm in a real-time robotic system with a microphone array. The experiment results show a mean error azimuth of 13 degrees, which surpasses the accuracy of the other biologically plausible neuromorphic approach for sound source localization.
  arXiv  Detail & Related papers  (2020-07-07T08:22:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.