We propose a novel approach using neural networks (NNs) to differentiate between cosmological models, and implemented lime as an interpretability identify the key features influencing our model's decisions. show potential of NNs enhance extraction meaningful information from large-scale structure data, based on current galaxy-clustering survey specifications, for constant cold dark matter (ΛCDM) model Hu-Sawicki f(R) model. find that NN can successfully distinguish ΛCDM by predicting correct...

Abstract The measurements of the temperature and polarisation anisotropies Cosmic Microwave Background (CMB) by ESA Planck mission have strongly supported current concordance model cosmology. However, latest cosmological data release from still has a powerful potential to test new science algorithms inference techniques. In this paper, we use advanced Machine Learning (ML) algorithms, such as Neural Networks (NNs), discern among different underlying models at angular power spectra level,...

The growth-rate f σ8(z) of the large-scale structure Universe is an important dynamic probe gravity that can be used to test for deviations from General Relativity.However, galaxy surveys extract this key quantity cosmological observations, two assumptions have made: i) a fiducial model, typically taken constant and cold dark matter (ΛCDM) model ii) modeling observed power spectrum, especially at non-linear scales, which particularly dangerous as most models in literature are...

We investigate whether neural networks (NNs) can accurately differentiate between growth-rate data of the large-scale structure (LSS) Universe simulated via two models: a cosmological constant and Lambda cold dark matter (CDM) model tomographic coupled energy (CDE) model. built an NN classifier tested its accuracy in distinguishing models. For our dataset, we generated $f observables that simulate realistic Stage IV galaxy survey-like setup for both CDM CDE various values parameters. then...

We present the discovery of correlations between X-ray spectral (photon) index and mass accretion rate observed in active galactic nuclei (AGNs) 3C 454.3 M 87. analyzed transition episodes these AGNs using Chandra , Swift Suzaku Beppo SAX, ASCA RXTE data. applied a scaling technique for black hole (BH) evaluation which uses correlation photon normalization seed (disk) component is proportional to rate. developed an analytical model that shows BH emergent spectrum undergoes evolution from...

We propose a novel approach using neural networks (NNs) to differentiate between cosmological models, especially in the case where they are nested and additional model parameters close zero, making it difficult discriminate them with traditional approaches. Our method complements Bayesian analyses for selection, which heavily depend on chosen priors average unnormalized posterior over potentially large prior volumes. By analyzing simulated realistic data sets of growth rate scale structure...

The measurements of the temperature and polarisation anisotropies Cosmic Microwave Background (CMB) by ESA Planck mission have strongly supported current concordance model cosmology. However, latest cosmological data release from still has a powerful potential to test new science algorithms inference techniques. In this paper, we use advanced Machine Learning (ML) algorithms, such as Neural Networks (NNs), discern among different underlying models at angular power spectra level, using both...

We investigate whether neural networks (NNs) can accurately differentiate between growth-rate data of the large-scale structure (LSS) Universe simulated via two models: a cosmological constant and $\Lambda$ cold dark matter (CDM) model tomographic coupled energy (CDE) model. built an NN classifier tested its accuracy in distinguishing models. For our dataset, we generated $f\sigma_8(z)$ observables that simulate realistic Stage IV galaxy survey-like setup for both $\Lambda$CDM CDE various...

The growth-rate $f\sigma_8(z)$ of the large-scale structure Universe is an important dynamic probe gravity that can be used to test for deviations from General Relativity. However, galaxy surveys extract this key quantity cosmological observations, two assumptions have made: i) a fiducial model, typically taken constant and cold dark matter ($\Lambda$CDM) model ii) modeling observed power spectrum, especially at non-linear scales, which particularly dangerous as most models in literature are...