arXiv subjects

Abhirup Datta

Publications and source records attributed to Abhirup Datta.

At least 109 records · Page 6Linked to original sources

arXiv2022

Towards 21-cm Intensity Mapping at $z=2.28$ with uGMRT using the Tapered Gridded Estimator I: Foreground Avoidance

The post-reionization $(z \le 6)$ neutral hydrogen (HI) 21-cm intensity mapping signal holds the potential to probe the large scale structures, study the expansion history and constrain various cosmological parameters. Here we apply the Tapered Gridded Estimator (TGE) to estimate $P(k_{\perp},k_{\parallel})$ the power spectrum of the $z = 2.28$ $(432.8\, {\rm MHz})$ redshifted 21-cm signal using a $24.4\,{\rm MHz}$ sub-band drawn from uGMRT Band 3 observations of European Large-Area ISO Survey-North 1 (ELAIS-N1). The TGE allows us to taper the sky response which suppresses the foreground contribution from sources in the periphery of the telescope's field of view. We apply the TGE on the measured visibility data to estimate the multi-frequency angular power spectrum (MAPS) $C_{\ell}(Δν)$ from which we determine $P(k_{\perp},k_{\parallel})$ using maximum-likelihood which naturally overcomes the issue of missing frequency channels (55 \% here). The entire methodology is validated using simulations. For the data, using the foreground avoidance technique, we obtain a $2\,σ$ upper limit of $Δ^2(k) \le (133.97)^2 \, {\rm mK}^{2}$ for the 21-cm brightness temperature fluctuation at $k = 0.347 \, \textrm{Mpc}^{-1}$. This corresponds to $[Ω_{\rm HI}b_{\rm HI}] \le 0.23$, where $Ω_{\rm HI}$ and $b_{\rm HI}$ respectively denote the cosmic \HI mass density and the \HI bias parameter. A previous work has analyzed $8 \, {\rm MHz}$ of the same data at $z=2.19$, and reported $Δ^{2}(k) \le (61.49)^{2} \, {\rm mK}^{2}$ and $[Ω_{\rm HI} b_{\rm HI}] \le 0.11$ at $k=1 \, {\rm Mpc}^{-1}$. The upper limits presented here are still orders of magnitude larger than the expected signal corresponding to $Ω_{\rm HI} \sim 10^{-3}$ and $b_{\rm HI} \sim 2 $.

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arXiv2022

A study on the Clustering Properties of Radio-Selected sources in the Lockman Hole Region at 325 MHz

Studying the spatial distribution of extragalactic source populations is vital in understanding the matter distribution in the Universe. It also enables understanding the cosmological evolution of dark matter density fields and the relationship between dark matter and luminous matter. Clustering studies are also required for EoR foreground studies since it affects the relevant angular scales. This paper investigates the angular and spatial clustering properties and the bias parameter of radio-selected sources in the Lockman Hole field at 325 MHz. The data probes sources with fluxes $\gtrsim$0.3 mJy within a radius of 1.8$^\circ$ around the phase center of a $6^\circ \times 6^\circ$ mosaic. Based on their radio luminosity, the sources are classified into Active Galactic Nuclei (AGNs) and Star-Forming Galaxies (SFGs). Clustering and bias parameters are determined for the combined populations and the classified sources. The spatial correlation length and the bias of AGNs are greater than SFGs -- indicating that more massive haloes host the former. This study is the first reported estimate of the clustering property of sources at 325 MHz, intermediate between the preexisting studies at high and low-frequency bands. It also probes a well-studied deep field at an unexplored frequency with moderate depth and area. Clustering studies require such observations along different lines of sight, with various fields and data sets across frequencies to avoid cosmic variance and systematics. Thus, an extragalactic deep field has been studied in this work to contribute to this knowledge.

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arXiv2022

Magnetic field measurement in TMC-1C using 22.3 GHz CCS Zeeman splitting

Measurement of magnetic fields in dense molecular clouds is essential for understanding the fragmentation process prior to star formation. Radio interferometric observations of CCS 22.3 GHz emission, from the starless core TMC-1C, have been carried out with the Karl G. Jansky Very Large Array to search for Zeeman splitting of the line in order to constrain the magnetic field strength. Toward a region offset from the dust peak, we report a detection of the Zeeman splitting of the CCS 2_1 - 1_0 transition, with an inferred magnetic field of ~2 mG. If we interpret the dust peak to be the core of TMC-1C, and the region where we have made a detection of the magnetic field to be the envelope, then our observed value for the magnetic field is consistent with a subcritical mass-to-flux ratio envelope around a core with supercritical mass-to-flux ratio. The ambipolar diffusion timescale for the formation of the core is consistent with the relevant timescale based on chemical modeling of the TMC-1C core. This work demonstrates the potential of deep CCS observation to carry out future measurements of magnetic field strengths in dense molecular clouds and, in turn, understand the role of the magnetic field in star formation.

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arXiv2022

Observing the Reionization : Effect of Calibration and Position Errors on Realistic Observation Conditions

Observation of the redshifted 21-cm signal from Cosmic Dawn and Epoch of Reionization is a challenging endeavor in observational cosmology. Presence of orders of magnitude brighter astrophysical foregrounds and various instrumental systematics increases the complexity of these observations. This work presents an end-to-end pipeline dealing with synthetic interferometric data of sensitive radio observations . The mock sky model includes the redshifted 21-cm signal and astrophysical foregrounds. The effects of calibration error and position error in the extraction of the redshifted 21-cm power spectrum has been simulated. The effect of the errors in the image plane detection of the cosmological signal has also been studied. A comparative analysis for array configurations like the SKA1-Low, MWA and HERA has been demonstrated. The calibration error tolerance of the arrays, under some assumptions about the nature of the systematic components, is optimally found to be $\sim 0.01\%$ for the detection of the signal. For position errors, an offset of $\gtrapprox 5\arcsec$ makes the residual foregrounds obscure the target signal. These simulations also imply that in the SKA-1 Low performs marginally better than the others in the image domain, while the same is true for MWA in the power spectrum domain. This is one of the first studies that compares performance of various radio telescopes operating under similar observing conditions towards detecting the cosmological signal. This end-to-end pipeline can also be extended to study effects of chromatic primary beam, radio frequency inferences, foregrounds with spectral features, etc.

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arXiv2022

Unveiling The Origin of Peculiar Diffuse Radio Emission in Abell 1351

Abell 1351 is a massive merging cluster that hosts a giant radio halo and a bright radio edge blended in the halo. In this paper, we present the first ever spectral analysis of this cluster using GMRT 610 MHz and VLA 1.4 GHz archival data and discuss the radio edge property. Using \textit{Chandra} data, we report the first tentative detection of shock front at the location of the edge in A1351 with discontinuities in both X-ray surface brightness and temperature. Our analysis strengthens the previous claim of the detected "edge" being a high luminosity radio relic. The radio relic has an integrated spectral index $α= -1.63 \pm 0.33$ and radio power $P_{1.4 \mathrm{\ GHz}} = 4.46 \pm 0.61\times10^{24}$ W $Hz^{-1}$ with a largest Linear Size (LLS) of 570 kpc. The radio spectral index map shows steepening in the shock downstream region. Our analysis favours the scenario where the Diffusive Shock Acceleration (DSA) of particles is responsible for the origin of the radio relic in the presence of strong magnetic field. We have also estimated the magnetic field at the relic location assuming equipartition condition.

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arXiv2022

On the Origin of Diffuse Radio Emission in Abell 85 -- Insights from new GMRT Observations

Extended, steep, and ultra-steep spectrum radio emission in a galaxy cluster is usually associated with recent mergers. Simulations show that radio phoenixes are aged radio galaxy lobes whose emission reactivates when a low Mach shock compresses it. A85 hosts a textbook example of a radio phoenix at about 320 kpc southwest of the cluster center. We present a new high resolution 325 MHz GMRT radio map illustrating this radio phoenix's complex and filamentary structure. The full extent of the radio structure is revealed for the first time from these radio images of A85. Using archival \textit{Chandra} X-ray observations, we applied an automated 2-D shock finder to the X-ray surface brightness and Adaptive Circular Binning (ACB) temperature maps which confirmed a bow shock at the location of the radio phoenix. We also compared the Mach number from the X-ray data with the radio-derived Mach number in the same region using multi-frequency radio observations and find that they are consistent within the 1$σ$ error level.

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arXiv2022

Deep uGMRT observations of the ELAIS-North 1 field: statistical properties of radio--infrared relations up to $z \sim$2

Comprehending the radio--infrared (IR) relations of the faint extragalactic radio sources is important for using radio emission as a tracer of star-formation in high redshift ($z$) star-forming galaxies (SFGs). Using deep uGMRT observations of the ELAIS-N1 field in the 0.3--0.5\,GHz range, we study the statistical properties of the radio--IR relations and the variation of the `$q$-parameter' up to $z=2$ after broadly classifying the faint sources as SFGs and AGN. We find the dust temperature ($\tdust$) to increase with $z$. This gives rise to $\qmir$, measured at $24\,\upmu$m, to increase with $z$ as the peak of IR emission shifts towards shorter wavelengths, resulting in the largest scatter among different measures of $q$-parameters. $\qfir$ measured at $70\,\upmu$m, and $q_{\rm TIR}$ using total-IR (TIR) emission are largely unaffected by $\tdust$. We observe strong, non-linear correlations between the radio luminosities at 0.4 and 1.4\,GHz with $70\,\upmu$m luminosity and TIR luminosity($\ltir$). To assess the possible role of the radio-continuum spectrum in making the relations non-linear, for the first time we study them at high $z$ using integrated radio luminosity ($\lrc$) in the range 0.1--2\,GHz. In SFGs, the $\lrc$--$\ltir$ relation remains non-linear with a slope of $1.07\pm0.02$, has a factor of 2 lower scatter compared to monochromatic radio luminosities, and $\qtirbol$ decreases with $z$ as $\qtirbol = (2.27 \pm 0.03)\,(1+z)^{-0.12 \pm 0.03}$. A redshift variation of $q$ is a natural consequence of non-linearity. We suggest that a redshift evolution of magnetic field strengths and/or cosmic ray acceleration efficiency in high-$z$ SFGs could give rise to non-linear radio--IR relations.

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arXiv2022

Study of the equatorial ionosphere using the Giant Metrewave Radio Telescope (GMRT) at sub-GHz frequencies

Radio interferometers, which are designed to observe astrophysical objects in the universe, can also be used to study the Earth's ionosphere. Radio interferometers like the Giant Metrewave Radio Telescope (GMRT) detect variations in ionospheric total electron content (TEC) on a much wider spatial scale at a relatively higher sensitivity than traditional ionospheric probes like the Global Navigation Satellite System (GNSS). The hybrid configuration of the GMRT (compact core and extended arms) and its geographical location make this interferometer an excellent candidate to explore the sensitive regions between the northern crest of the Equatorial Ionization Anomaly (EIA) and the magnetic equator. For this work, a bright radio source, 3C68.2, is observed from post-midnight to post-sunrise ($\sim$\,9 hours) to study the ionospheric activities at solar-minima. This study presents data reduction and processing techniques to measure differential TEC ($δ\rm{TEC}$) between the set of antennas with an accuracy of $1\times10^{-3}$ TECU. Furthermore, using these $δ\rm{TEC}$ measurements, we have demonstrated techniques to compute the TEC gradient over the full array and micro-scale variation in two-dimensional TEC gradient surface. These variations are well equipped to probe ionospheric plasma, especially during the night-time. Our study, for the first time, reports the capability of the GMRT to detect ionospheric activities. Our result validates, compared to previous studies with VLA, LOFAR and MWA, the ionosphere over the GMRT is more active, which is expected due to its location near the magnetic equator.

astro-ph.IM↗

arXiv2022

A Comparative Analysis To Deal With Missing Spectral Information Caused By RFI In Cosmological HI 21CM Observations

We investigate the effect of radio-frequency interference (RFI) excision in estimating the cosmological \hi\ 21 cm power spectrum. Flagging of RFI-contaminated channels results in a non-uniform sampling of the instrumental bandpass response. Hence, the Fourier transformation (FT) of visibilities from frequency to delay domain contaminates the higher foreground-free delay modes, and separating the spectrally fluctuating \hi\ signal from spectrally smooth foregrounds becomes challenging. We have done a comparative analysis between two algorithms, one-dimensional CLEAN and Least Square Spectral Analysis (LSSA), which have been used widely to solve this issue in the literature. We test these algorithms using the simulated SKA-1 low observations in the presence of different RFI flagging scenarios. We find that in the presence of random flagging of data, both algorithms perform well and can mitigate the foreground leakage issue. But, CLEAN fails to restrict the foreground leakage in the presence of periodic and periodic plus broadband RFI flagging and gives an extra bias to the estimated power spectrum. However, LSSA can restrict the foreground leakage for these RFI flagging scenarios and gives an unbiased estimate of the \hi\ 21 cm power spectrum. We have also applied these algorithms to the upgraded GMRT observation and found that both CLEAN and LSSA give consistent results in the presence of realistic random flagging scenarios for this observed data set. This comparative analysis demonstrates the effectiveness and robustness of these two algorithms in estimating the \hi\ 21 cm power spectrum from the data set affected by different RFI scenarios.

astro-ph.CO↗

arXiv2022

Extracting the 21-cm Power Spectrum and the reionization parameters from mock datasets using Artificial Neural Networks

Detection of the \hi~ 21-cm power spectrum is one of the key science drivers of several ongoing and upcoming low-frequency radio interferometers. However, the major challenge in such observations come from bright foregrounds, whose accurate removal or avoidance is key to the success of these experiments. In this work, we demonstrate the use of artificial neural networks (ANNs) to extract the \hi~ 21-cm power spectrum from synthetic datasets and extract the reionization parameters from the \hi~ 21-cm power spectrum. For the first time, using a suite of simulations, we present an ANN based framework capable of extracting the \hi~ signal power spectrum directly from the total observed sky power spectrum (which contains the 21-cm signal, along with the foregrounds and effects of the instrument). To achieve this, we have used a combination of two separate neural networks sequentially. As the first step, \texttt{ANN1} predicts the 21-cm power spectrum directly from foreground corrupted synthetic datasets. In the second step, \texttt{ANN2} predicts the reionization parameters from the predicted \hi~ power spectra from \texttt{ANN1}. Our ANN-based framework is trained at a redshift of $9.01$, and for \kk-modes in the range, $\rm{0.17<\kk<0.37~Mpc^{-1}}$. We have tested the network's performance with mock datasets that include foregrounds and are corrupted with thermal noise, corresponding to $1080$ hrs of observations of the \textsc{ska-1 low} and \textsc{hera}. Using our ANN framework, we are able to recover the \hi~ power spectra with an accuracy of $\approx95-99\%$ for the different test sets. For the predicted astrophysical parameters, we have achieved an accuracy of $\approx~81-90\%$ and $\approx~50-60\%$ for the test sets corrupted with thermal noise corresponding to the \textsc{ska-1 low} and \textsc{hera}, respectively.

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arXiv2021

Graphical Gaussian Process Models for Highly Multivariate Spatial Data

For multivariate spatial Gaussian process (GP) models, customary specifications of cross-covariance functions do not exploit relational inter-variable graphs to ensure process-level conditional independence among the variables. This is undesirable, especially for highly multivariate settings, where popular cross-covariance functions such as the multivariate Matérn suffer from a "curse of dimensionality" as the number of parameters and floating point operations scale up in quadratic and cubic order, respectively, in the number of variables. We propose a class of multivariate "Graphical Gaussian Processes" using a general construction called "stitching" that crafts cross-covariance functions from graphs and ensures process-level conditional independence among variables. For the Matérn family of functions, stitching yields a multivariate GP whose univariate components are Matérn GPs, and conforms to process-level conditional independence as specified by the graphical model. For highly multivariate settings and decomposable graphical models, stitching offers massive computational gains and parameter dimension reduction. We demonstrate the utility of the graphical Matérn GP to jointly model highly multivariate spatial data using simulation examples and an application to air-pollution modelling.

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arXiv2021

Sparse nearest neighbor Cholesky matrices in spatial statistics

Gaussian Processes (GP) is a staple in the toolkit of a spatial statistician. Well-documented computing roadblocks in the analysis of large geospatial datasets using Gaussian Processes have now been successfully mitigated via several recent statistical innovations. Nearest Neighbor Gaussian Processes (NNGP) has emerged as one of the leading candidates for such massive-scale geospatial analysis owing to their empirical success. This article reviews the connection of NNGP to sparse Cholesky factors of the spatial precision (inverse-covariance) matrices. Focus of the review is on these sparse Cholesky matrices which are versatile and have recently found many diverse applications beyond the primary usage of NNGP for fast parameter estimation and prediction in the spatial (generalized) linear models. In particular, we discuss applications of sparse NNGP Cholesky matrices to address multifaceted computational issues in spatial bootstrapping, simulation of large-scale realizations of Gaussian random fields, and extensions to non-parametric mean function estimation of a Gaussian Process using Random Forests. We also review a sparse-Cholesky-based model for areal (geographically-aggregated) data that addresses interpretability issues of existing areal models. Finally, we highlight some yet-to-be-addressed issues of such sparse Cholesky approximations that warrants further research.

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arXiv2021

Bayesian Functional Registration of fMRI Activation Maps

Functional magnetic resonance imaging (fMRI) has provided invaluable insight into our understanding of human behavior. However, large inter-individual differences in both brain anatomy and functional localization after anatomical alignment remain a major limitation in conducting group analyses and performing population-level inference. This paper addresses this problem by developing and validating a new computational technique for reducing misalignment across individuals in functional brain systems by spatially transforming each subject's functional data to a common reference map. Our proposed Bayesian functional registration approach allows us to assess differences in brain function across subjects and individual differences in activation topology. It combines intensity-based and feature-based information into an integrated framework and allows inference to be performed on the transformation via the posterior samples. We evaluate the method in a simulation study and apply it to data from a study of thermal pain. We find that the proposed approach provides increased sensitivity for group-level inference.

stat.AP↗

arXiv2021

On the detection of multiple shock fronts in A1914 using deep Chandra X-ray observations

Here, we report the new detection of three shock fronts using archival \textit{Chandra} X-ray observations in Abell 1914, which also hosts a radio halo, a radio phoenix, and a head-tail galaxy. In this study, we report the X-ray shock front at the position of the radio phoenix, which further strengthens the scenario that radio phoenix traces old plasma that gets lit up when compressed by shock passage. We further analyze the thermodynamic structure of the cluster in detail. We create temperature maps of A1914 using three different techniques, Adaptive Circular Binning (ACB), Weighted Voronoi Tessellations (WVT), and Contour binning (Contbin) method. These thermodynamic maps, along with the pseudo pressure and the pseudo entropy maps for the cluster, are evidence of disturbed morphology produced by multiple merger events. These merger events create cluster-wide turbulence, which may re-accelerate the relativistic particles and result in a radio halo within the cluster. Further, comparing X-ray and radio images reveals that the radio halo is contained within two X-ray shock fronts. Our analysis suggests that A1914 has both equatorial shock and axial shock within the cluster's ICM. We proposed a dual merging scenario based on the shock position and analysis of the thermodynamic maps obtained from the deeper \textit{Chandra} X-ray observations.

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arXiv2021

Random Forests for dependent data

Random forest (RF) is one of the most popular methods for estimating regression functions. The local nature of the RF algorithm, based on intra-node means and variances, is ideal when errors are i.i.d. For dependent error processes like time series and spatial settings where data in all the nodes will be correlated, operating locally ignores this dependence. Also, RF will involve resampling of correlated data, violating the principles of bootstrap. Theoretically, consistency of RF has been established for i.i.d. errors, but little is known about the case of dependent errors. We propose RF-GLS, a novel extension of RF for dependent error processes in the same way Generalized Least Squares (GLS) fundamentally extends Ordinary Least Squares (OLS) for linear models under dependence. The key to this extension is the equivalent representation of the local decision-making in a regression tree as a global OLS optimization which is then replaced with a GLS loss to create a GLS-style regression tree. This also synergistically addresses the resampling issue, as the use of GLS loss amounts to resampling uncorrelated contrasts (pre-whitened data) instead of the correlated data. For spatial settings, RF-GLS can be used in conjunction with Gaussian Process correlated errors to generate kriging predictions at new locations. RF becomes a special case of RF-GLS with an identity working covariance matrix. We establish consistency of RF-GLS under beta- (absolutely regular) mixing error processes and show that this general result subsumes important cases like autoregressive time series and spatial Matern Gaussian Processes. As a byproduct, we also establish consistency of RF for beta-mixing processes, which to our knowledge, is the first such result for RF under dependence. We empirically demonstrate the improvement achieved by RF-GLS over RF for both estimation and prediction under dependence.

stat.ML↗

arXiv2021

An X-ray and Radio Study of the Hubble Frontier Field Cluster Abell S1063

We present results from \textit{Chandra} X-ray observations and 325 MHz Giant Metrewave Radio Telescope (GMRT) observations of the massive and X-ray luminous cluster of galaxies Abell S1063. We report the detection of large-scale \lq\lq excess brightness\rq\rq\ in the residual \textit{Chandra} X-ray surface brightness map, which extends at least 2.7 Mpc towards the north-east from the center of the cluster. We also present a high fidelity X-ray flux and temperature map using \textit{Chandra} archival data of 122 ksec, which shows the disturbed morphology in the cluster. The residual flux map shows the first observational confirmation of the merging axis proposed by earlier simulation by \citet{Gomez2012AJ....144...79G}. The average temperature within $R_{500}$ is $11.7 \pm 0.56$ keV, which makes AS1063 one of the hottest clusters in the nearby Universe. The integrated radio flux density at 325 MHz is found to be $62.0\pm6.3$ mJy. The integrated spectrum of the radio halo follows a power-law with a spectral index $α=-1.43\pm 0.13$. The radio halo is found to be significantly under-luminous, which favored for both the hadronic as well as the turbulent re-acceleration mechanism for its origin.

astro-ph.HE↗

arXiv2021

spNNGP R package for Nearest Neighbor Gaussian Process models

This paper describes and illustrates functionality of the spNNGP R package. The package provides a suite of spatial regression models for Gaussian and non-Gaussian point-referenced outcomes that are spatially indexed. The package implements several Markov chain Monte Carlo (MCMC) and MCMC-free Nearest Neighbor Gaussian Process (NNGP) models for inference about large spatial data. Non-Gaussian outcomes are modeled using a NNGP Polya-Gamma latent variable. OpenMP parallelization options are provided to take advantage of multiprocessor systems. Package features are illustrated using simulated and real data sets.

stat.CO↗

arXiv2021

Generalized Bayes Quantification Learning under Dataset Shift

Quantification learning is the task of prevalence estimation for a test population using predictions from a classifier trained on a different population. Quantification methods assume that the sensitivities and specificities of the classifier are either perfect or transportable from the training to the test population. These assumptions are inappropriate in the presence of dataset shift, when the misclassification rates in the training population are not representative of those for the test population. Quantification under dataset shift has been addressed only for single-class (categorical) predictions and assuming perfect knowledge of the true labels on a small subset of the test population. We propose generalized Bayes quantification learning (GBQL) that uses the entire compositional predictions from probabilistic classifiers and allows for uncertainty in true class labels for the limited labeled test data. Instead of positing a full model, we use a model-free Bayesian estimating equation approach to compositional data based only on a first-moment assumption. The idea will be useful in Bayesian compositional data analysis in general as it is robust to different generating mechanisms for compositional data and includes categorical outputs as a special case. We show how our method yields existing quantification approaches as special cases. Extension to an ensemble GBQL that uses predictions from multiple classifiers yielding inference robust to inclusion of a poor classifier is discussed. We outline a fast and efficient Gibbs sampler using a rounding and coarsening approximation to the loss functions. We also establish posterior consistency, asymptotic normality and valid coverage of interval estimates from GBQL, as well as finite sample posterior concentration rate. Empirical performance of GBQL is demonstrated through simulations and analysis of real data with evident dataset shift.

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