scholarly journals Cosmological parameter estimation and inference using deep summaries

2021 ◽  
Vol 104 (12) ◽  
Author(s):  
Janis Fluri ◽  
Tomasz Kacprzak ◽  
Alexandre Refregier ◽  
Aurelien Lucchi ◽  
Thomas Hofmann
Keyword(s):  
Parameter Estimation ◽  
Cosmological Parameter

scholarly journals The biasing of baryons on the cluster mass function and cosmological parameter estimation

2014 ◽  
Vol 440 (3) ◽  
pp. 2290-2299 ◽  
Author(s):  
Davide Martizzi ◽  
Irshad Mohammed ◽  
Romain Teyssier ◽  
Ben Moore
Keyword(s):  
Parameter Estimation ◽  
Mass Function ◽  
Cosmological Parameter ◽  
Cluster Mass

scholarly journals Cosmological parameter estimation using Very Small Array data out to ℓ= 1500

2004 ◽  
Vol 353 (3) ◽  
pp. 747-759 ◽  
Author(s):  
Rafael Rebolo ◽  
Richard A. Battye ◽  
Pedro Carreira ◽  
Kieran Cleary ◽  
Rod D. Davies ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Cosmological Parameter ◽  
Array Data ◽  
Small Array

scholarly journals Fast cosmological parameter estimation using neural networks

2007 ◽  
Vol 376 (1) ◽  
pp. L11-L15 ◽  
Author(s):  
T. Auld ◽  
M. Bridges ◽  
M. P. Hobson ◽  
S. F. Gull
Keyword(s):  
Neural Networks ◽  
Parameter Estimation ◽  
Cosmological Parameter

scholarly journals Cosmological Parameter Estimation Using 21 cm Radiation from the Epoch of Reionization

2006 ◽  
Vol 653 (2) ◽  
pp. 815-834 ◽  
Author(s):  
Matthew McQuinn ◽  
Oliver Zahn ◽  
Matias Zaldarriaga ◽  
Lars Hernquist ◽  
Steven R. Furlanetto
Keyword(s):  
Parameter Estimation ◽  
Cosmological Parameter

scholarly journals DES Y1 Results: validating cosmological parameter estimation using simulated Dark Energy Surveys

2018 ◽  
Vol 480 (4) ◽  
pp. 4614-4635 ◽  
Author(s):  
N MacCrann ◽  
J DeRose ◽  
R H Wechsler ◽  
J Blazek ◽  
E Gaztanaga ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Dark Energy ◽  
Cosmological Parameter

scholarly journals Convolutional neural networks on the HEALPix sphere: a pixel-based algorithm and its application to CMB data analysis

2019 ◽  
Vol 628 ◽  
pp. A129 ◽  
Author(s):  
N. Krachmalnicoff ◽  
M. Tomasi
Keyword(s):  
Neural Networks ◽  
Parameter Estimation ◽  
Convolutional Neural Networks ◽  
Bayesian Methods ◽  
Spherical Surface ◽  
Thomson Scattering ◽  
Cosmological Parameter ◽  
Gaussian Field ◽  
Standard Spectrum ◽  
Temperature Maps

We describe a novel method for the application of convolutional neural networks (CNNs) to fields defined on the sphere, using the Hierarchical Equal Area Latitude Pixelization scheme (HEALPix). Specifically, we have developed a pixel-based approach to implement convolutional and pooling layers on the spherical surface, similarly to what is commonly done for CNNs applied to Euclidean space. The main advantage of our algorithm is to be fully integrable with existing, highly optimized libraries for NNs (e.g., PyTorch, TensorFlow, etc.). We present two applications of our method: (i) recognition of handwritten digits projected on the sphere; (ii) estimation of cosmological parameter from simulated maps of the cosmic microwave background (CMB). The latter represents the main target of this exploratory work, whose goal is to show the applicability of our CNN to CMB parameter estimation. We have built a simple NN architecture, consisting of four convolutional and pooling layers, and we have used it for all the applications explored herein. Concerning the recognition of handwritten digits, our CNN reaches an accuracy of ∼95%, comparable with other existing spherical CNNs, and this is true regardless of the position and orientation of the image on the sphere. For CMB-related applications, we tested the CNN on the estimation of a mock cosmological parameter, defining the angular scale at which the power spectrum of a Gaussian field projected on the sphere peaks. We estimated the value of this parameter directly from simulated maps, in several cases: temperature and polarization maps, presence of white noise, and partially covered maps. For temperature maps, the NN performances are comparable with those from standard spectrum-based Bayesian methods. For polarization, CNNs perform about a factor four worse than standard algorithms. Nonetheless, our results demonstrate, for the first time, that CNNs are able to extract information from polarization fields, both in full-sky and masked maps, and to distinguish between E and B-modes in pixel space. Lastly, we have applied our CNN to the estimation of the Thomson scattering optical depth at reionization (τ) from simulated CMB maps. Even without any specific optimization of the NN architecture, we reach an accuracy comparable with standard Bayesian methods. This work represents a first step towards the exploitation of NNs in CMB parameter estimation and demonstrates the feasibility of our approach.

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