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Abhirup Datta

Publications and source records attributed to Abhirup Datta.

At least 73 records · Page 4Linked to original sources
arXiv2024

Development of a Machine Learning based Radio source localisation algorithm for Tri-axial antenna configuration

Accurately determining the origin of radio emissions is essential for numerous scientific experiments, particularly in radio astronomy. Conventional techniques, such as the use of antenna arrays encounter significant challenges, specially at very low frequencies, due to factors like the substantial size of the antennas and ionospheric interference. To address these challenges, we employ a space-based single-telescope that utilizes co-located antennas, complemented by goniopolarimetric techniques for precise source localization. This study explores a novel and elementary machine learning (ML) technique as a way to improve and estimate Direction of Arrival (DoA), leveraging a tri-axial antenna arrangement for radio source localization. Employing a simplistic emission and receiving antenna model, our study involves training an artificial neural network (ANN) using synthetic radio signals. These synthetic signals can originate from any location in the sky and cover an incoherent frequency range of 0.3 to 30 MHz, with a signal-to-noise ratio (SNR) between 0 and 60 dB. Then, a large data set was generated to train the ANN model catering to the possible signal configurations and variations. After training, the developed ANN model demonstrated exceptional performance, achieving loss levels in the training ($\sim0.02$), validation ($\sim0.23\%$), and testing ($\sim0.21\%$) phases. The machine learning-based approach remarkably, exhibits substantially quicker inference times ($\sim5$ ms) in contrast to analytically derived Direction of Arrival (DoA) methods, which typically range from 100 ms to a few seconds. This underscores its practicality for real-time applications in radio source localization, particularly in scenarios with limited number of sensors.

physics.app-ph↗
arXiv2024

Consistency of common spatial estimators under spatial confounding

This paper addresses the asymptotic performance of popular spatial regression estimators of the linear effect of an exposure on an outcome under ``spatial confounding" -- the presence of an unmeasured spatially-structured variable influencing both the exposure and the outcome. We first show that the estimators from ordinary least squares (OLS) and restricted spatial regression are asymptotically biased under spatial confounding. We then prove a novel main result on the consistency of the generalized least squares (GLS) estimator using a Gaussian process (GP) working covariance matrix in the presence of spatial confounding under infill (fixed domain) asymptotics. The result holds under very general conditions -- for any exposure with some non-spatial variation (noise), for any spatially continuous fixed confounder function, using any Matèrn or square exponential kernel used to construct the GLS estimator, and without requiring Gaussianity of errors. Finally, we prove that spatial estimators from GLS, GP regression, and spline models that are consistent under confounding by a fixed function will also be consistent under endogeneity or confounding by a random function, i.e., a stochastic process. We conclude that, contrary to claims in some literature on spatial confounding, traditional spatial estimators are capable of estimating linear exposure effects under spatial confounding as long as there is some noise in the exposure. We support our theoretical arguments with simulation studies.

stat.ME↗
arXiv2024

A Multi-Frequency View of the Radio Phoenix in the Abell 85 Cluster

Radio phoenices are complex and filamentary diffuse radio sources found in both merging and relaxed clusters. The formation of these sources was proposed to be due to adiabatic compression of old Active Galactic Nucleus (AGN) plasma in shock waves. Most of the previous spectral studies of these sources were limited to integrated spectral indices, which were found to be very steep as well as showing a curved spectrum. Here, we have performed a multi-frequency investigation of the radio phoenix in the Abell 85 cluster. Owing to the sensitive high-resolution observations, we found some of the finer filamentary structures that were previously undetected. We produced resolved spectral index maps of the radio phoenix between 323, 700, and 1280 MHz. The orientation of the filaments, as well as the gradient across the spectral index maps, suggest the possible direction of the shock motion from northeast to southwest. The integrated spectrum of the radio phoenix was found to be very steep and curved toward high-frequencies. Furthermore, the spectral index of the filaments was found to be less steep compared to the non-filamentary regions, implying greater energy injection in the filaments. The observed features in the radio phoenix in the Abell 85 cluster seem to be in support of the adiabatic shock compression mechanism.

astro-ph.HE↗
arXiv2024

Visibility graph-based covariance functions for scalable spatial analysis in non-convex domains

We present a new method for constructing valid covariance functions of Gaussian processes for spatial analysis in irregular, non-convex domains such as bodies of water. Standard covariance functions based on geodesic distances are not guaranteed to be positive definite on such domains, while existing non-Euclidean approaches fail to respect the partially Euclidean nature of these domains where the geodesic distance agrees with the Euclidean distances for some pairs of points. Using a visibility graph on the domain, we propose a class of covariance functions that preserve Euclidean-based covariances between points that are connected in the domain while incorporating the non-convex geometry of the domain via conditional independence relationships. We show that the proposed method preserves the partially Euclidean nature of the intrinsic geometry on the domain while maintaining validity (positive definiteness) and marginal stationarity of the covariance function over the entire parameter space, properties which are not always fulfilled by existing approaches to construct covariance functions on non-convex domains. We provide useful approximations to improve computational efficiency, resulting in a scalable algorithm. We compare the performance of our method with those of competing state-of-the-art methods using simulation studies on synthetic non-convex domains. The method is applied to data regarding acidity levels in the Chesapeake Bay, showing its potential for ecological monitoring in real-world spatial applications on irregular domains.

stat.ME↗
arXiv2024

Neural networks for geospatial data

Analysis of geospatial data has traditionally been model-based, with a mean model, customarily specified as a linear regression on the covariates, and a covariance model, encoding the spatial dependence. We relax the strong assumption of linearity and propose embedding neural networks directly within the traditional geostatistical models to accommodate non-linear mean functions while retaining all other advantages including use of Gaussian Processes to explicitly model the spatial covariance, enabling inference on the covariate effect through the mean and on the spatial dependence through the covariance, and offering predictions at new locations via kriging. We propose NN-GLS, a new neural network estimation algorithm for the non-linear mean in GP models that explicitly accounts for the spatial covariance through generalized least squares (GLS), the same loss used in the linear case. We show that NN-GLS admits a representation as a special type of graph neural network (GNN). This connection facilitates use of standard neural network computational techniques for irregular geospatial data, enabling novel and scalable mini-batching, backpropagation, and kriging schemes. Theoretically, we show that NN-GLS will be consistent for irregularly observed spatially correlated data processes. We also provide a finite sample concentration rate, which quantifies the need to accurately model the spatial covariance in neural networks for dependent data. To our knowledge, these are the first large-sample results for any neural network algorithm for irregular spatial data. We demonstrate the methodology through simulated and real datasets.

stat.ML↗
arXiv2024

Direct Bayesian Regression for Distribution-valued Covariates

In this manuscript, we study the problem of scalar-on-distribution regression; that is, instances where subject-specific distributions or densities, or in practice, repeated measures from those distributions, are the covariates related to a scalar outcome via a regression model. We propose a direct regression for such distribution-valued covariates that circumvents estimating subject-specific densities and directly uses the observed repeated measures as covariates. The model is invariant to any transformation or ordering of the repeated measures. Endowing the regression function with a Gaussian Process prior, we obtain closed form or conjugate Bayesian inference. Our method subsumes the standard Bayesian non-parametric regression using Gaussian Processes as a special case. Theoretically, we show that the method can achieve an optimal estimation error bound. To our knowledge, this is the first theoretical study on Bayesian regression using distribution-valued covariates. Through simulation studies and analysis of activity count dataset, we demonstrate that our method performs better than approaches that require an intermediate density estimation step.

stat.ME↗
arXiv2024

Towards $21$-cm intensity mapping at $z=2.28$ with uGMRT using the tapered gridded estimator -- IV. Wideband analysis

We present a Wideband Tapered Gridded Estimator (TGE), which incorporates baseline migration and variation of the primary beam pattern for neutral hydrogen (${\rm H\hspace{0.5mm}}{\scriptsize {\rm I}}$) 21-cm intensity mapping (IM) with large frequency bandwidth radio-interferometric observations. Here we have analysed $394-494 \, {\rm MHz}$ $(z = 1.9 - 2.6)$ uGMRT data to estimate the Multi-frequency Angular Power Spectrum (MAPS) $C_\ell(Δν)$ from which we have removed the foregrounds using the polynomial fitting (PF) and Gaussian Process Regression (GPR) methods developed in our earlier work. Using the residual $C_\ell(Δν)$ to estimate the mean squared 21-cm brightness temperature fluctuation $Δ^2(k)$, we find that this is consistent with $0 \pm 2 σ$ in several $k$ bins. The resulting $2σ$ upper limit $Δ^2(k) < (4.68)^2 \, \rm{mK^2}$ at $k=0.219\,\rm{Mpc^{-1}}$ is nearly $15$ times tighter than earlier limits obtained from a smaller bandwidth ($24.4 \, {\rm MHz}$) of the same data. The $2σ$ upper limit $[Ω_{\rm H\hspace{0.5mm}{\scriptsize {\rm I}}} b_{\rm H\hspace{0.5mm}{\scriptsize {\rm I}}}] < 1.01 \times 10^{-2}$ is within an order of magnitude of the value expected from independent estimates of the ${\rm H\hspace{0.5mm}}{\scriptsize {\rm I}}$ mass density $Ω_{\rm H\hspace{0.5mm}{\scriptsize {\rm I}}}$ and the ${\rm H\hspace{0.5mm}}{\scriptsize {\rm I}}$ bias $b_{\rm H\hspace{0.5mm}{\scriptsize {\rm I}}}$. The techniques used here can be applied to other telescopes and frequencies, including $\sim 150 \, {\rm MHz}$ Epoch of Reionization observations.

astro-ph.CO↗
arXiv2024

Extracting the Global 21-cm signal from Cosmic Dawn and Epoch of Reionization in the presence of Foreground and Ionosphere

Detection of redshifted \ion{H}{i} 21-cm emission is a potential probe for investigating the Universe's first billion years. However, given the significantly brighter foreground, detecting 21-cm is observationally difficult. The Earth's ionosphere considerably distorts the signal at low frequencies by introducing directional-dependent effects. Here, for the first time, we report the use of Artificial Neural Networks (ANNs) to extract the global 21cm signal characteristics from the composite all-sky averaged signal, including foreground and ionospheric effects such as refraction, absorption, and thermal emission from the ionosphere's F and D-layers. We assume a 'perfect' instrument and neglect instrumental calibration and beam effects. To model the ionospheric effect, we considered the static and time-varying ionospheric conditions for the mid-latitude region where LOFAR is situated. In this work, we trained the ANN model for various situations using a synthetic set of the global 21cm signals created by altering its parameter space based on the "$\rm \tanh$" parameterized model and the Accelerated Reionization Era Simulations (ARES) algorithm. The obtained result shows that the ANN model can extract the global signal parameters with an accuracy of $\ge 96 \% $ in the final study when we include foreground and ionospheric effects. On the other hand, a similar ANN model can extract the signal parameters from the final prediction dataset with an accuracy ranging from $97 \%$ to $98 \%$ when considering more realistic sets of the global 21cm signals based on physical models.

astro-ph.CO↗
arXiv2023

A new enigmatic radio relic in the low mass cluster Abell 2108

We report the discovery of a radio relic in the northeastern periphery of the cluster Abell 2108 (A2108). A2108 is a part of the uGMRT LOw-MAss Galaxy Cluster Survey (GLOMACS), where our main aim is to search for diffuse radio emission signatures in very sparsely explored low-mass galaxy clusters using uGMRT band-3 (central frequency 400 MHz). We used our uGMRT band-3 data along with the existing archival band-3 uGMRT data to improve image sensitivity. Along with the previously reported southwestern relic, the discovery of the new relic makes A2108 one of the few low-mass clusters hosting double relics. The new relic spans over a region of 610 kpc $\times$ 310 kpc and, interestingly, differs considerably in size and morphology from the other relic. With XMM-Newton science archive data, we also report the tentative detection of a mildly supersonic shock of Mach number $\mathcal{M}_\mathrm{SB}=1.42$ and $\mathcal{M}_\mathrm{T} = 1.43$ from the surface brightness and temperature discontinuities, respectively near this newly found relic. Both the relics in A2108 are found to be significantly under-luminous compared to other double relic systems in the mass-luminosity plane. Though mild supersonic shocks resulting from an off-axis merger could have influenced their origin, we hypothesize that further local environments have played a crucial role in shaping their morphology.

astro-ph.HE↗
arXiv2023

Modeling Structure and Country-specific Heterogeneity in Misclassification Matrices of Verbal Autopsy-based Cause of Death Classifiers

Verbal autopsy (VA) algorithms are routinely used to determine individual-level causes of death (COD) in many low-and-middle-income countries, which are then aggregated to derive population-level cause-specific mortality fractions (CSMF), essential to informing public health policies. However, VA algorithms frequently misclassify COD and introduce bias in CSMF estimates. A recent method, VA-calibration, can correct for this bias using a VA misclassification matrix estimated from paired data on COD from both VA and minimally invasive tissue sampling (MITS) from the Child Health and Mortality Prevention Surveillance (CHAMPS) Network. Due to the limited sample size, CHAMPS data are pooled across all countries, implicitly assuming that the misclassification rates are homogeneous. In this research, we show that the VA misclassification matrices are substantially heterogeneous across countries, thereby biasing the VA-calibration. We develop a coherent framework for modeling country-specific misclassification matrices in data-scarce settings. We first introduce a novel base model based on two latent mechanisms: intrinsic accuracy and systematic preference to parsimoniously characterize misclassifications. We prove that they are identifiable from the data and manifest as a form of invariance in certain misclassification odds, a pattern evident in the CHAMPS data. Then we expand from this base model, adding higher complexity and country-specific heterogeneity via interpretable effect sizes. Shrinkage priors balance the bias-variance tradeoff by adaptively favoring simpler models. We publish uncertainty-quantified estimates of VA misclassification rates for 6 countries. This effort broadens VA-calibration's future applicability and strengthens ongoing efforts of using VA for mortality surveillance.

stat.ME↗
arXiv2023

Ionospheric response during Tropical Cyclones-a brief review on Amphan and Nisarga

Here, we explore the different characteristics of a possible coupling between tropospheric and ionospheric activities during the impact of tropical cyclones (TC) like Amphan and Nisarga in the Indian subcontinent. We have analyzed the effect of TCs Amphan and Nisarga on the low latitude ionosphere using the measurements from several IGS stations around India and a GPS+ NavIC station in Indore, India. For the first time, this study assesses the impact of tropical cyclones on the equatorial ionosphere using both GPS and NavIC. After the landfall of TC Amphan, the VTEC analysis shows a significant drop from nominal values in both NavIC as well in GPS by $5.1$ TECU and $3.6$ TECU, respectively. In contrast to TC Amphan, Nisarga showed a rise in VTEC which ranged from $0.9$ TECU in GPS to $1.7$ - $5$ TECU in NavIC satellites except for PRN6. The paper examines Outgoing Longwave Radiation as a proxy to the convective activity which may be responsible for the ionospheric variation through the generation of gravity waves. In addition, the horizontal neutral wind observations at the location of TC landfall confirm the presence of ionospheric disturbances. VTEC perturbation analysis using a band-pass filter reveals a variation in differential TEC values between $\pm0.4$ and $\pm0.8$ based on the IGS station measurements. This indicates that the gravity wave is one of the responsible mechanisms for the lower-upper atmospheric coupling during both cyclones.

physics.ao-ph↗
arXiv2023

Towards $21$-cm intensity mapping at $z=2.28$ with uGMRT using the tapered gridded estimator III: Foreground removal

Neutral hydrogen (HI) $21$-cm intensity mapping (IM) is a promising probe of the large-scale structures in the Universe. However, a few orders of magnitude brighter foregrounds obscure the IM signal. Here we use the Tapered Gridded Estimator (TGE) to estimate the multi-frequency angular power spectrum (MAPS) $C_{\ell}(Δν)$ from a $24.4\,\rm{MHz}$ bandwidth uGMRT Band $3$ data at $432.8\,\rm{MHz}$. In $C_{\ell}(Δν)$ foregrounds remain correlated across the entire $Δν$ range, whereas the $21$-cm signal is localized within $Δν\le[Δν]$ (typically $0.5-1\,\rm{MHz}$). Assuming the range $Δν>[Δν]$ to have minimal $21$-cm signal, we use $C_{\ell}(Δν)$ in this range to model the foregrounds. This foreground model is extrapolated to $Δν\leq[Δν]$, and subtracted from the measured $C_{\ell}(Δν)$. The residual $[C_{\ell}(Δν)]_{\rm res}$ in the range $Δν\le[Δν]$ is used to constrain the $21$-cm signal, compensating for the signal loss from foreground subtraction. $[C_{\ell}(Δν)]_{\rm{res}}$ is found to be noise-dominated without any trace of foregrounds. Using $[C_{\ell}(Δν)]_{\rm res}$ we constrain the $21$-cm brightness temperature fluctuations $Δ^2(k)$, and obtain the $2σ$ upper limit $Δ_{\rm UL}^2(k)\leq(18.07)^2\,\rm{mK^2}$ at $k=0.247\,\rm{Mpc}^{-1}$. We further obtain the $2σ$ upper limit $ [Ω_{\rm HI}b_{\rm HI}]_{\rm UL}\leq0.022$ where $Ω_{\rm HI}$ and $b_{\rm HI}$ are the comoving HI density and bias parameters respectively. Although the upper limit is nearly $10$ times larger than the expected $21$-cm signal, it is $3$ times tighter over previous works using foreground avoidance on the same data.

astro-ph.CO↗
arXiv2023

Commentary on Guyll et al. (2023): Misuse of Statistical Method Results in Highly Biased Interpretation of Forensic Evidence

Since the National Academy of Sciences released their report outlining paths for improving reliability, standards, and policies in the forensic sciences NAS (2009), there has been heightened interest in evaluating and improving the scientific validity within forensic science disciplines. Guyll et al. (2023) seek to evaluate the validity of forensic cartridge-case comparisons. However, they make a serious statistical error that leads to highly inflated claims about the probability that a cartridge case from a crime scene was fired from a reference gun, typically a gun found in the possession of a defendant. It is urgent to address this error since these claims, which are generally biased against defendants, are being presented by the prosecution in an ongoing homicide case where the defendant faces the possibility of a lengthy prison sentence (DC Superior Court, 2023).

stat.AP↗
arXiv2023

Detailed study of the Bootes field using 300-500 MHz uGMRT observations: Source Properties and radio--infrared correlations

The dominant source of radio continuum emissions at low frequencies is synchrotron radiation, which originates from star-forming regions in disk galaxies and from powerful jets produced by active galactic nuclei (AGN). We studied the Bootes field using the upgraded Giant Meterwave Radio Telescope (uGMRT) at 400 MHz, achieving a central minimum off-source RMS noise of 35$μ$Jy beam$^{-1}$ and a catalogue of 3782 sources in $\sim6$ sq. degrees of the sky. The resulting catalogue was compared to other radio frequency catalogues, and the corrected normalised differential source counts were derived. We use standard multi-wavelength techniques to classify the sources in star-forming galaxies (SFGs), radio-loud (RL) AGN, and radio-quiet (RQ) AGN that confirm a boost in the SFGs and RQ\,AGN AGN populations at lower flux levels. For the first time, we investigated the properties of the radio--IR relations at 400\,MHz in this field. The $L_{\rm 400 MHz}$--$L_{\rm TIR}$ relations for SFGs were found to show a strong correlation with non-linear slope values of $1.10\pm0.01$, and variation of $q_{\rm TIR}$ with $z$ is given as, $q_{\rm TIR} = (2.19 \pm 0.07)\ (1+z)^{-0.15 \pm 0.08}$. This indicates that the non-linearity of the radio--IR relations can be attributed to the mild variation of $q_{\rm TIR}$ values with $z$. The derived relationships exhibit similar behaviour when applied to LOFAR at 150 MHz and also at 1.4 GHz. This emphasises the fact that other parameters like magnetic field evolution with $z$ or the number densities of cosmic ray electrons can play a vital role in the mild evolution of $q$ values.

astro-ph.GA↗
arXiv2023

A radio bridge connecting the minihalo and phoenix in the Abell 85 cluster

Galaxy clusters are located at the nodes of cosmic filaments and therefore host a lot of hydrodynamical activity. However, cool core clusters are considered to be relatively relaxed systems without much merging activity. The Abell 85 cluster is a unique example where the cluster hosts both a cool core and multiple ongoing merging processes. In this work, we used 700 MHz uGMRT as well as MeerKAT L-band observations, carried out as part of the MGCLS, of the Abell 85. We reconfirm the presence of a minihalo in the cluster centre at 700MHz that was recently discovered in MGCLS. Furthermore, we discovered a radio bridge connecting the central minihalo and the peripheral radio phoenix. The mean surface brightness, size and flux density of the bridge at 700 MHz is found to be $\sim 0.14\ μ$Jy/arcsec$^2$, $\sim 220$ kpc and $\sim 4.88$ mJy, respectively, with a spectral index of $α_{700}^{1.28} = -0.92$. Although the origin of the seed relativistic electrons is still unknown, turbulent re-acceleration caused by both the spiralling sloshing gas in the intracluster medium (ICM) and the post-shock turbulence from the outgoing merging shock associated with the phoenix formation may be responsible for the bridge.

astro-ph.HE↗
arXiv2023

The spectral study of the faint radio sources in the ELAIS N1 field

Understanding the spectral properties of sources is crucial for the characterization of the radio source population. In this work, we have extensively studied the ELAIS N1 field using various low-frequency radio observations. For the first time, we present the 1250\,MHz observations of the field using the upgraded Giant Meterwave Radio Telescope (uGMRT) that reach a central off-source RMS noise of $\sim 12\,μ$Jy\,beam$^{-1}$. A source catalogue of 1086 sources is compiled at $5σ$ threshold ($>60\,μ$Jy) to derive the normalized differential source counts at this frequency that is consistent with existing observations and simulations. We present the spectral indices derived in two ways: two-point spectral indices and by fitting a power-law. The latter yielded a median $α= -0.57\pm 0.14$, and we identified nine ultra-steep spectrum sources using these spectral indices. Further, using a radio colour diagram, we identify the three mega-hertz peaked spectrum (MPS) sources, while three other MPS sources are identified from the visual inspection of the spectra, the properties of which are discussed. In our study of the classified sources in the ELAIS N1 field, we present the relationship between $α$ and $z$. We find no evidence of an inverse correlation between these two quantities and suggest that the nature of the radio spectrum remains independent of the large-scale properties of the galaxies that vary with redshifts.

astro-ph.GA↗
arXiv2023

Spectral Analysis of ionospheric density variations measured with the large radio telescope in the Low-Latitude region

The low-latitude ionosphere is a dynamic region with a wide range of disturbances in temporal and spatial scales. The Giant Metrewave Radio Telescope (GMRT) situated in the low-latitude region has demonstrated its ability to detect various ionospheric phenomena. It can detect total electron content (TEC) variation with precision of 1 mTECU and also can measure TEC gradient with an accuracy of about $\rm7\times 10^{-4}\,TECU\,km^{-1}$. This paper describes the spectral analysis of previously calculated TEC gradient measurements and validates them by comparing their properties using two bands. The analysis tracked individual waves associated with medium-scale traveling ionospheric disturbances (MSTIDs) and smaller waves down to wavelengths of $\sim$ 10 km. The ionosphere is found to have unanticipated changes during sunrise hours, with waves changed propagation direction as the sun approached the zenith. Equatorial spread\,$F$ disturbances during sunrise hours is observed, along with smaller structures moving in the same direction.

physics.space-ph↗
arXiv2023

Density-on-Density Regression

In this study, a density-on-density regression model is introduced, where the association between densities is elucidated via a warping function. The proposed model has the advantage of a being straightforward demonstration of how one density transforms into another. Using the Riemannian representation of density functions, which is the square-root function (or half density), the model is defined in the correspondingly constructed Riemannian manifold. To estimate the warping function, it is proposed to minimize the average Hellinger distance, which is equivalent to minimizing the average Fisher-Rao distance between densities. An optimization algorithm is introduced by estimating the smooth monotone transformation of the warping function. Asymptotic properties of the proposed estimator are discussed. Simulation studies demonstrate the superior performance of the proposed approach over competing approaches in predicting outcome density functions. Applying to a proteomic-imaging study from the Alzheimer's Disease Neuroimaging Initiative, the proposed approach illustrates the connection between the distribution of protein abundance in the cerebrospinal fluid and the distribution of brain regional volume. Discrepancies among cognitive normal subjects, patients with mild cognitive impairment, and Alzheimer's disease (AD) are identified and the findings are in line with existing knowledge about AD.

stat.ME↗