arXiv subjects

Purba Mukherjee

Publications and source records attributed to Purba Mukherjee.

At least 37 records · Page 2Linked to original sources

arXiv2024

Non-parametric reconstruction of cosmological observables using Gaussian Processes Regression

The current accelerated expansion of the Universe remains ones of the most intriguing topics in modern cosmology, driving the search for innovative statistical techniques. Recent advancements in machine learning have significantly enhanced its application across various scientific fields, including physics, and particularly cosmology, where data analysis plays a crucial role in problem-solving. In this work, a non-parametric regression method with Gaussian processes is presented along with several applications to reconstruct some cosmological observables, such as the deceleration parameter and the dark energy equation of state, in order to contribute with some information that helps to clarify the behavior of the Universe. It was found that the results are consistent with $Λ$CDM and the predicted value of the Hubble parameter at redshift zero is $H_{0}=68.798\pm 6.340(1σ) \text{ km}\text{ s}^{-1}\text{ Mpc}^{-1}$.

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arXiv2024

Reconciling $S_8$: Insights from Interacting Dark Sectors

We do a careful investigation of the prospects of dark energy (DE) interacting with cold dark matter in alleviating the $S_8$ clustering tension. To this end, we consider various well-known parametrizations of the DE equation of state (EoS) and consider perturbations in both the dark sectors, along with an interaction term. Moreover, we perform a separate study for the phantom and non-phantom regimes. Using cosmic microwave background (CMB), baryon acoustic oscillations, and Type Ia supernovae data sets, constraints on the model parameters for each case have been obtained and a generic reduction in the $H_0-σ_{8,0}$ correlation has been observed, both for constant and dynamical DE EoS. This reduction, coupled with a significant negative correlation between the interaction term and $σ_{8,0}$, contributes to easing the clustering tension by lowering $σ_{8,0}$ to somewhere in between the early CMB and late-time clustering measurements for the phantom regime, for almost all the models under consideration. Additionally, this is achieved without exacerbating the Hubble tension. In this regard, the interacting Chevallier-Polarski-Linder and Jassal-Bagla-Padmanabhan models perform the best in relaxing the $S_8$ tension to $<1σ$. However, for the non-phantom regime the $σ_{8,0}$ tension tends to have worsened, which reassures the merits of phantom DE from latest data. We further investigate the role of redshift space distortion data sets and find an overall reduction in tension, with a $σ_{8,0}$ value relatively closer to the CMB value. We finally check whether further extensions of this scenario, such as the inclusion of the sound speed of DE and warm dark matter interacting with DE, can have some effects.

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arXiv2024

Deep Learning Based Recalibration of SDSS and DESI BAO Alleviates Hubble and Clustering Tensions

Conventional calibration of Baryon Acoustic Oscillations (BAO) data relies on estimation of the sound horizon at drag epoch $r_d$ from early universe observations by assuming a cosmological model. We present a recalibration of two independent BAO datasets, SDSS and DESI, by employing deep learning techniques for model-independent estimation of $r_d$, and explore the impacts on $Λ$CDM cosmological parameters. Significant reductions in both Hubble ($H_0$) and clustering ($S_8$) tensions are observed for both the recalibrated datasets. Moderate shifts in some other parameters hint towards further exploration of such data-driven approaches.

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arXiv2024

Model-independent cosmological inference post DESI DR1 BAO measurements

In this work, we implement Gaussian process regression to reconstruct the expansion history of the universe in a model-agnostic manner, using the Pantheon-Plus SN-Ia compilation in combination with two different BAO measurements (SDSS-IV and DESI DR1). In both the reconstructions, the $Λ$CDM model is always included in the 95\% confidence intervals. We find evidence that the DESI LRG data at $z_{\text{eff}} = 0.51$ is not an outlier within our model-independent framework. We study the $\mathcal{O}m$-diagnostics and the evolution of the total equation of state (EoS) of our universe, which hint towards the possibility of a quintessence-like dark energy scenario with a very slowly varying EoS, and a phantom-crossing in higher $z$. The entire exercise is later complemented by considering two more SN-Ia compilations - DES-5YR and Union3 - in combination with DESI BAO. Reconstruction with the DESI BAO + DES-5YR SN data sets predicts that the $Λ$CDM model lies outside the 3$σ$ confidence levels, whereas with DESI BAO + Union3 data, the $Λ$CDM model is always included within 1$σ$. We also report constraints on $H_0 r_d$ from our model-agnostic analysis, independent of the pre-recombination physics. Our results point towards an $\approx$ 2$σ$ discrepancy between the DESI + Pantheon-Plus and DESI + DES-5YR data sets, which calls for further investigation.

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arXiv2024

A possible late-time transition of $M_B$ inferred via neural networks

The strengthening of tensions in the cosmological parameters has led to a reconsideration of fundamental aspects of standard cosmology. The tension in the Hubble constant can also be viewed as a tension between local and early Universe constraints on the absolute magnitude $M_B$ of Type Ia supernova. In this work, we reconsider the possibility of a variation of this parameter in a model-independent way. We employ neural networks to agnostically constrain the value of the absolute magnitude as well as assess the impact and statistical significance of a variation in $M_B$ with redshift from the Pantheon+ compilation, together with a thorough analysis of the neural network architecture. We find an indication for a possible transition redshift at the $z\approx 1$ region.

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arXiv2024

LADDER: Revisiting the Cosmic Distance Ladder with Deep Learning Approaches and Exploring its Applications

We investigate the prospect of reconstructing the ''cosmic distance ladder'' of the Universe using a novel deep learning framework called LADDER - Learning Algorithm for Deep Distance Estimation and Reconstruction. LADDER is trained on the apparent magnitude data from the Pantheon Type Ia supernovae compilation, incorporating the full covariance information among data points, to produce predictions along with corresponding errors. After employing several validation tests with a number of deep learning models, we pick LADDER as the best performing one. We then demonstrate applications of our method in the cosmological context, including serving as a model-independent tool for consistency checks for other datasets like baryon acoustic oscillations, calibration of high-redshift datasets such as gamma ray bursts, and use as a model-independent mock catalog generator for future probes. Our analysis advocates for careful consideration of machine learning techniques applied to cosmological contexts.

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arXiv2024

Role of Future SNIa Data from Rubin LSST in Reinvestigating Cosmological Models

We study how future Type-Ia supernovae (SNIa) standard candles detected by the Vera C. Rubin Observatory (LSST) can constrain some cosmological models. We use a realistic three-year SNIa simulated dataset generated by the LSST Dark Energy Science Collaboration (DESC) Time Domain pipeline, which includes a mix of spectroscopic and photometrically identified candidates. We combine this data with Cosmic Microwave Background (CMB) and Baryon Acoustic Oscillation (BAO) measurements to estimate the dark energy model parameters for two models -- the baseline $Λ$CDM and Chevallier-Polarski-Linder (CPL) dark energy parametrization. We compare them with the current constraints obtained from joint analysis of the latest real data from the Pantheon SNIa compilation, CMB from Planck 2018 and BAO. Our analysis finds tighter constraints on the model parameters along with a significant reduction of correlation between $H_0$ and $σ_{8,0}$. We find that LSST is expected to significantly improve upon the existing SNIa data in the critical analysis of cosmological models.

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arXiv2023

Examining the validity of the minimal varying speed of light model through cosmological observations: relaxing the null curvature constraint

We revisit a consistency test for the speed of light variability, using the latest cosmological observations. This exercise can serve as a new diagnostics for the standard cosmological model and distinguish between the minimal varying speed of light in the Friedmann-Lemaître-Robertson-Walker universe. We deploy Gaussian processes to reconstruct cosmic distances and ages in the redshift range $0 1$ with a particular reconstruction kernel when $a$-BAO data are included. Still, we ascribe no statistical significance to this result bearing in mind the degeneracy between the associated priors for combined analysis, and incompleteness of the $a$-BAO data set at higher $z$.

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arXiv2023

Checking the second law at cosmic scales

Based on recent data about the history of the Hubble factor, it is argued that the second law of thermodynamics holds at the largest scales accessible to observation. This is consistent with previous studies of the same question.

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arXiv2023

Neural network reconstruction of cosmology using the Pantheon compilation

In this work, we reconstruct the Hubble diagram using various data sets, including correlated ones, in Artificial Neural Networks (ANN). Using ReFANN, that was built for data sets with independent uncertainties, we expand it to include non-Guassian data points, as well as data sets with covariance matrices among others. Furthermore, we compare our results with the existing ones derived from Gaussian processes and we also perform null tests in order to test the validity of the concordance model of cosmology.

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arXiv2023

Reconstructing the Hubble parameter with future Gravitational Wave missions using Machine Learning

We study the prospects of Gaussian processes (GP), a machine learning (ML) algorithm, as a tool to reconstruct the Hubble parameter $H(z)$ with two upcoming gravitational wave missions, namely the evolved Laser Interferometer Space Antenna (eLISA) and the Einstein Telescope (ET). Assuming various background cosmological models, the Hubble parameter has been reconstructed in a non-parametric manner with the help of GP using realistically generated catalogs for each mission. The effects of early-time and late-time priors on the reconstruction of $H(z)$, and hence on the Hubble constant ($H_0$), have also been focused on separately. Our analysis reveals that GP is quite robust in reconstructing the expansion history of the Universe within the observational window of the specific missions under consideration. We further confirm that both eLISA and ET would be able to provide constraints on $H(z)$ and $H_0$ which would be competitive to those inferred from current datasets. In particular, we observe that an eLISA run of $\sim10$-year duration with $\sim80$ detected bright siren events would be able to constrain $H_0$ as good as a $\sim3$-year ET run assuming $\sim 1000$ bright siren event detections. Further improvement in precision is expected for longer eLISA mission durations such as a $\sim15$-year time-frame having $\sim120$ events. Lastly, we discuss the possible role of these future gravitational wave missions in addressing the Hubble tension, for each model, on a case-by-case basis.

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arXiv2023

A thorough investigation of the prospects of eLISA in addressing the Hubble tension: Fisher Forecast, MCMC and Machine Learning

We carry out an in-depth analysis of the capability of the upcoming space-based gravitational wave mission eLISA in addressing the Hubble tension, with a primary focus on observations at intermediate redshifts ($3<z<8$). We consider six different parametrizations representing different classes of cosmological models, which we constrain using the latest datasets of cosmic microwave background (CMB), baryon acoustic oscillations (BAO), and type Ia supernovae (SNIa) observations, in order to find out the up-to-date tensions with direct measurement data. Subsequently, these constraints are used as fiducials to construct mock catalogs for eLISA. We then employ Fisher analysis to forecast the future performance of each model in the context of eLISA. We further implement traditional Markov Chain Monte Carlo (MCMC) to estimate the parameters from the simulated catalogs. Finally, we utilize Gaussian Processes (GP), a machine learning algorithm, for reconstructing the Hubble parameter directly from simulated data. Based on our analysis, we present a thorough comparison of the three methods as forecasting tools. Our Fisher analysis confirms that eLISA would constrain the Hubble constant ($H_0$) at the sub-percent level. MCMC/GP results predict reduced tensions for models/fiducials which are currently harder to reconcile with direct measurements of $H_0$, whereas no significant change occurs for models/fiducials at lesser tensions with the latter. This feature warrants further investigation in this direction.

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arXiv2023

Neural network reconstruction of scalar-tensor cosmology

Neural networks have shown great promise in providing a data-first approach to exploring new physics. In this work, we use the full implementation of late time cosmological data to reconstruct a number of scalar-tensor cosmological models within the context of neural network systems. In this pipeline, we incorporate covariances in the data in the neural network training algorithm, rather than a likelihood which is the approach taken in Markov chain Monte Carlo analyses. For general subclasses of classic scalar-tensor models, we find stricter bounds on functional models which may help in the understanding of which models are observationally viable.

gr-qc↗

arXiv2023

Spatial Curvature and Thermodynamics

Reasonable parametrizations of the current Hubble data set of the expansion rate of our homogeneous and isotropic universe, after suitable smoothing of these data, strongly suggests that the area of the apparent horizon increases irrespective of whether the spatial curvature of the metric is open, flat or closed. Put in another way, any sign of the spatial curvature appears consistent with the second law of thermodynamics.

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arXiv2022

Neural Network Reconstruction of $H'(z)$ and its application in Teleparallel Gravity

In this work, we explore the possibility of using artificial neural networks to impose constraints on teleparallel gravity and its $f(T)$ extensions. We use the available Hubble parameter observations from cosmic chronometers and baryon acoustic oscillations from different galaxy surveys. We discuss the procedure for training a network model to reconstruct the Hubble diagram. Further, we describe the procedure to obtain $H'(z)$, the first order derivative of $H(z)$, using artificial neural networks which is a novel approach to this method of reconstruction. These analyses are complemented with further studies on the impact of two priors which we put on $H_0$ to assess their impact on the analysis, which are the local measurements by the SH0ES team ($H_0^{\text{R20}} = 73.2 \pm 1.3$ km Mpc$^{-1}$ s$^{-1}$) and the updated TRGB calibration from the Carnegie Supernova Project ($H_0^{\text{TRGB}} = 69.8 \pm 1.9$ km Mpc$^{-1}$ s$^{-1}$), respectively. Additionally, we investigate the validity of the concordance model, through some cosmological null tests with these reconstructed data sets. Finally, we reconstruct the allowed $f(T)$ functions for different combinations of the observational Hubble data sets. Results show that the $Λ$CDM model lies comfortably included at the 1$σ$ confidence level for all the examined cases.

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arXiv2022

Non-parametric Reconstruction Of Some Cosmological Parameters

The present thesis is devoted to the non-parametric reconstruction of some cosmological parameters using diverse observational datasets. The Universe is assumed to be spatially homogeneous and isotropic, thus described by the FLRW metric. The first chapter provides a brief introduction to cosmology and focuses on the reconstruction methods. An assessment of the cosmic distance-duality relation is discussed in chapter 2. In chapter 3, a non-parametric reconstruction of the cosmological jerk parameter is carried out. Chapter 4 explores the possibility of a non-gravitational interaction in the cosmic dark sector. In chapter 5, attempts are made to revisit a non-parametric reconstruction of the cosmic deceleration parameter using various combinations of recently updated background datasets and the growth rate of structure measurements from the redshift-space distortions. Finally, chapter 6 contains the concluding remarks and relevant discussion regarding the overall work presented in the thesis.

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arXiv2022

Revisiting a non-parametric reconstruction of the deceleration parameter from combined background and the growth rate data

The cosmic deceleration parameter $q$ has been reconstructed in a non-parametric way using various combinations of recent observational datasets. The Pantheon compilation of the Supernova (SN) distance modulus data, the Cosmic Chronometer (CC) measurements of the Hubble parameter including the full systematics and the Baryon Acoustic Oscillation (BAO) data have been considered in this work. The redshift $z_t$, where the transition from a past decelerated to a late-time accelerated phase of evolution occurs, is estimated from the reconstructed $q$. The possible effect of a non-zero spatial curvature from the Planck 2020 estimate is checked. The outcome of including different $H_0$ measurements from recent Planck 2020 and Riess 2021 probes having a maximum discrepancy at the $4.2σ$ level, is investigated. Results indicate that the transition from a past decelerated phase to the late-time accelerated phase occurs within the redshift range $0.5 1$, the reconstructed $q$ is observed to have a non-monotonic evolution in case of the combined CC and SN data. On introducing the BAO data, the reconstructed $q$ shows an oscillating behaviour for $z\gtrsim1$. To investigate the effect of matter perturbations, the growth rate data from the Redshift-Space Distortions (RSD) are utilized in reconstructing $q$. Using the $\mathcal{O}m(z)$ diagnostic, we draw inferences on the validity of $Λ$CDM as a consistency check. The $Λ$CDM model is well consistent and included at the 2$σ$ level in the domain of all the reconstructions.

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arXiv2022

Constraining the curvature density parameter in cosmology

The cosmic curvature density parameter has been constrained in the present work independent of any background cosmological model. The reconstruction is performed adopting the non-parametric Gaussian Processes (GP). The constraints on $Ω_{k0}$ are obtained via a Markov Chain Monte Carlo (MCMC) analysis. Late-time cosmological probes viz., the Supernova (SN) distance modulus data, the Cosmic Chronometer (CC) and the radial Baryon Acoustic Oscillations ($r$BAO) measurements of the Hubble data have been utilized for this purpose. The results are further combined with the data from redshift space distortions (RSD) which studies the growth of large scale structure in the universe. The only \textit{a priori} assumption is that the universe is homogeneous and isotropic, described by the FLRW metric. Results indicate that a spatially flat universe is well consistent in 2$σ$ within the domain of reconstruction $0<z<2$ for the background data. On combining the RSD data we find that the results obtained are consistent with spatial flatness mostly within 2$σ$ and always within 3$σ$ in the domain of reconstruction $0<z<2$.

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