arXiv subjects

Srikanta Pal

Publications and source records attributed to Srikanta Pal.

5 recordsLinked to original sources

arXiv2024

ParamANN: A Neural Network to Estimate Cosmological Parameters for $Λ$CDM Universe Using Hubble Measurements

In this article, we employ a machine learning (ML) approach for the estimations of four fundamental parameters, namely, the Hubble constant ($H_0$), matter ($Ω_{0m}$), curvature ($Ω_{0k}$) and vacuum ($Ω_{0Λ}$) densities of non-flat $Λ$CDM model. We use $31$ Hubble parameter values measured by differential ages (DA) technique in the redshift interval $0.07 \leq z \leq 1.965$. We create an artificial neural network (ParamANN) and train it with simulated values of $H(z)$ using various sets of $H_0$, $Ω_{0m}$, $Ω_{0k}$, $Ω_{0Λ}$ parameters chosen from different and sufficiently wide prior intervals. We use a correlated noise model in the analysis. We demonstrate accurate validation and prediction using ParamANN. ParamANN provides an excellent cross-check for the validity of the $Λ$CDM model. We obtain $H_0 = 68.14 \pm 3.96$ $\rm{kmMpc^{-1}s^{-1}}$, $Ω_{0m} = 0.3029 \pm 0.1118$, $Ω_{0k} = 0.0708 \pm 0.2527$ and $Ω_{0Λ} = 0.6258 \pm 0.1689$ by using the trained network. These parameter values agree very well with the results of global CMB observations of the Planck collaboration. We compare the cosmological parameter values predicted by ParamANN with those obtained by the MCMC method. Both the results agree well with each other. This demonstrates that ParamANN is an alternative and complementary approach to the well-known Metropolis-Hastings algorithm for estimating the cosmological parameters by using Hubble measurements.

astro-ph.CO↗

arXiv2024

On Direct Estimation of Density Parameters and Hubble Constant for $Λ$CDM Universe using Hubble Measurements

The set of cosmological density parameters ($Ω_{0m}h_{0}^{2}$, $Ω_{0k}h_{0}^{2}$, $Ω_{0Λ}h_{0}^{2}$) and Hubble constant ($\hat{h}_{0}$) are useful for fundamental understanding of the universe from many perspectives. In this article, we propose a new procedure to estimate these parameters for $Λ$ cold dark matter ($Λ$CDM) universe in the Friedmann-Robertson-Walker (FRW) background. We generalize the two-point statistics first proposed by Sahni et al. (2008) to the three point case and estimate the parameters using currently available Hubble parameter ($H(z)$) values in the redshift range $0.07 \leq z \leq 2.36$ measured by differential age (DA) and baryon acoustic oscillation (BAO) techniques. All the parameters are estimated assuming the general case of non-flat universe. Using both DA and BAO data we obtain $Ω_{0m}h_{0}^{2}=0.1485 \pm 0.0065$, $Ω_{0k}h_{0}^{2}=-0.0137 \pm 0.017$, $Ω_{0Λ}h_{0}^{2}=0.3126 \pm 0.0145$ and $\hat{h}_{0}=0.6689 \pm 0.0021$. These results are in satisfactory agreement with the Planck results. An important advantage of our method is that to estimate the value of any one of the independent cosmological parameters one does not need to use the values for the rest of them. Each parameter is obtained solely from the measured values of Hubble parameters at different redshifts without any need to use values of other parameters. Such a method is expected to be less susceptible to the undesired effects of degeneracy issues between cosmological parameters during their estimations. Moreover, there is no requirement of assuming spatial flatness in our method.

astro-ph.CO↗

arXiv2024

Accurate and Unbiased Reconstruction of CMB B Mode using Deep Learning

An ingeniously designed autoencoder (PrimeNet) using simulated observations of future generation ECHO satellite mission recovers CMB B mode map, angular spectrum for multipoles $\ell \lesssim 9$ and tensor to scalar ratio $r$ {\it limited only by cosmic variance down to $r= 0.0001$ and below}. We use diverse, realistically complex and detailed foreground models. PrimeNet predicts accurate results even when data with $r=0$ are tested which were not used in training, implying robust and efficient predictive power. The work eliminates a major bottleneck of weak CMB B mode reconstruction and takes a leap forward for understanding fundamental physics of the primordial Universe.

astro-ph.CO↗

arXiv2023

Reconstruction of full sky CMB $\bf{E}$ and $\bf{B}$ modes spectra removing $\bf{E}$-to-$\bf{B}$ leakage from partial sky using deep learning

Incomplete sky analysis of cosmic microwave background (CMB) polarization spectra poses a major problem of leakage between $E$- and $B$-modes. We present a machine learning approach to remove this $E$-to-$B$ leakage using a convolutional neural network (CNN) in presence of detector noise. The CNN predicts the full sky $E$- and $B$-modes spectra for multipoles $2 \leq \ell \leq 384$ from the partial sky spectra for $N_{\rm{side}} = 256$. We use tensor-to-scalar ratio $r=0.001$ to simulate the CMB polarization maps. We train our CNN using $10^5$ full sky target spectra and an equal number of noise contaminated partial sky spectra obtained from the simulated maps. The CNN works well for two masks covering the sky area of $\sim 80\%$ and $\sim 10\%$ respectively after training separately for each mask. For the assumed theoretical $E$- and $B$-modes spectra, predicted full sky $E$- and $B$-modes spectra agree well with the corresponding target spectra and their means agree with theoretical spectra. The CNN preserves the cosmic variances at each multipole, effectively removes correlations of the partial sky $E$- and $B$-modes spectra, and retains the entire statistical properties of the targets avoiding the problem of so-called $E$-to-$B$ leakage for the chosen theoretical model.

astro-ph.CO↗

arXiv2023

Estimation of Full Sky Power Spectrum between Intermediate to Large Angular Scales from Partial Sky CMB Anisotropies using Artificial Neural Network

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 (ANN) to predict the full sky CMB angular power spectrum between intermediate to large angular scales from the partial sky spectrum obtained from masked CMB temperature anisotropy map. We use a simple ANN architecture with one hidden layer containing $895$ neurons. Using $1.2 \times 10^{5}$ training samples of full sky and corresponding partial sky CMB angular power spectra at Healpix pixel resolution parameter $N_{side} = 256$, we show that predicted spectrum by our ANN agrees well with the target spectrum at each realization for the multipole range $2 \leq l \leq 512$. The predicted spectra are statistically unbiased and they preserve the cosmic variance accurately. Statistically, the differences between the mean predicted and underlying theoretical spectra are within approximately $3σ$. Moreover, the probability densities obtained from predicted angular power spectra agree very well with those obtained from `actual' full sky CMB angular power spectra for each multipole. Interestingly, our work shows that the significant correlations in input cut-sky spectra, due to mode-mode coupling introduced on the partial sky, are effectively removed since the ANN learns the hidden pattern between the partial sky and full sky spectra preserving the entire statistical properties. The excellent agreement of statistical properties between the predicted and the ground-truth demonstrates the importance of using artificial intelligence systems in cosmological analysis more widely.

astro-ph.CO↗