arXiv subjects

Abhirup Datta

Publications and source records attributed to Abhirup Datta.

At least 55 records · Page 3Linked to original sources

arXiv2025

Impact of Calibration and Position Errors on Astrophysical Parameters of the HI 21cm Signal

The Epoch of Reionization (EoR) and Cosmic Dawn (CD) are pivotal stages during the first billion years of the universe, exerting a significant influence on the development of cosmic structure. The detection of the redshifted 21-cm signal from these epochs is challenging due to the dominance of significantly stronger astrophysical foregrounds and the presence of systematics. This work used the 21cm E2E (end to end) pipeline, followed by simulation methodology described \cite{2022Mazumder} to conduct synthetic observations of a simulated sky model that includes both the redshifted 21-cm signal and foregrounds. A framework was constructed using Artificial Neural Networks (ANN) and Bayesian techniques to directly deduce astrophysical parameters from the measured power spectrum. This approach eliminates the need for explicit telescope layout effects correction in interferometric arrays such as SKA-Low. The present work investigates the impact of gain calibration errors and sky model position errors on the recovery of the redshifted 21-cm power spectrum for the SKA-Low AA$^{\ast}$ array configuration. We assessed the effects of these inaccuracies on the deduced astrophysical parameters and established acceptable tolerance levels. Based on our results, the gain calibration error tolerance for ideal signal detection is 0.001 \%. However, if the sky model position errors exceed 0.048 arcseconds, the remaining foregrounds would obscure the target signal.

astro-ph.CO↗

arXiv2025

LAVA: Language Model Assisted Verbal Autopsy for Cause-of-Death Determination

Verbal autopsy (VA) is a critical tool for estimating causes of death in resource-limited settings where medical certification is unavailable. This study presents LA-VA, a proof-of-concept pipeline that combines Large Language Models (LLMs) with traditional algorithmic approaches and embedding-based classification for improved cause-of-death prediction. Using the Population Health Metrics Research Consortium (PHMRC) dataset across three age categories (Adult: 7,580; Child: 1,960; Neonate: 2,438), we evaluate multiple approaches: GPT-5 predictions, LCVA baseline, text embeddings, and meta-learner ensembles. Our results demonstrate that GPT-5 achieves the highest individual performance with average test site accuracies of 48.6% (Adult), 50.5% (Child), and 53.5% (Neonate), outperforming traditional statistical machine learning baselines by 5-10%. Our findings suggest that simple off-the-shelf LLM-assisted approaches could substantially improve verbal autopsy accuracy, with important implications for global health surveillance in low-resource settings.

cs.CL↗

arXiv2025

Unified calibration and spatial mapping of fine particulate matter data from multiple low-cost air pollution sensor networks in Baltimore, Maryland

Low-cost air pollution sensor networks are increasingly being deployed globally, supplementing sparse regulatory monitoring with localized air quality data. In some areas, like Baltimore, Maryland, there are only few regulatory (reference) devices but multiple low-cost networks. While there are many available methods to calibrate data from each network individually, separate calibration of each network leads to conflicting air quality predictions. We develop a general Bayesian spatial filtering model combining data from multiple networks and reference devices, providing dynamic calibrations (informed by the latest reference data) and unified predictions (combining information from all available sensors) for the entire region. This method accounts for network-specific bias and noise (observation models), as different networks can use different types of sensors, and uses a Gaussian process (state-space model) to capture spatial correlations. We apply the method to calibrate PM$_{2.5}$ data from Baltimore in June and July 2023 -- a period including days of hazardous concentrations due to wildfire smoke. Our method helps mitigate the effects of preferential sampling of one network in Baltimore, results in better predictions and narrower confidence intervals. Our approach can be used to calibrate low-cost air pollution sensor data in Baltimore and any other areas with multiple low-cost networks.

stat.AP↗

arXiv2025

On the estimation of solar wind velocity under varying solar activity conditions using Akatsuki measurements

We present an analysis of solar wind dynamics based on Doppler spectral width measurements of X-band radio signals from the Japanese Akatsuki spacecraft. The dataset includes two solar conjunction occultation experiments conducted in 2016 and 2022, capturing the transition from the descending phase of Solar Cycle 24, a period of low solar activity, to the ascending phase of Solar Cycle 25, which exhibited moderate to intense activity. Our study demonstrates the utility of this technique for estimating both slow and fast solar wind velocities across different phases of solar activity. A key focus is the 2022 experiment, which probed the solar corona near coronal holes at heliocentric distances ranging from 1.4 to 10 $R_\odot$. We also investigate the impact of electron density estimates on the accuracy of solar wind speed determinations, underscoring the need for improved electron density modeling to enhance the robustness of such measurements.

astro-ph.SR↗

arXiv2025

Broadband Modelling of GRB 230812B Afterglow: Implications for VHE $γ$-ray Detection with IACTs

A significant fraction of the energy from the $γ$-ray burst (GRB) jets, after powering the keV-MeV emission, forms an ultra-relativistic shock propagating into the circumburst medium. The particles in the medium accelerate through the shock and produce afterglow emission. Recently, a few GRB afterglows have been observed in TeV $γ$-rays by Cherenkov Telescopes. This provides access to broadband spectra of GRB afterglows containing rich information about the microphysics of relativistic shocks and the profile of the circumburst medium. Since the transition from synchrotron to inverse Compton regime in afterglow spectra occurs between hard X-rays and the very-high-energy (VHE) $γ$-rays, detection in one of these bands is required to identify the two spectral components. The early afterglow data in the hard X-rays, along with the GeV emission, could accurately constrain the spectral shape and help in capturing the spectral turnover to distinguish the two components. We present the multiwavelength spectral and temporal study, focused on the keV-VHE domain, of GRB 230812B, one of the brightest GRBs detected by Fermi Gamma Ray Burst Monitor (GBM), along with the detection of a 72 GeV photon in Large Area Telescope (LAT) during the early afterglow phase. Through a detailed modelling of the emission within the afterglow external forward shock in a wind-like scenario, we predict optical to high-energy observations up to ~1 day. We emphasize the importance of following up poorly localised GRBs by demonstrating that even without prompt sub-degree localisation, such as in GRB 230812B, it is possible to recover the emission using imaging atmospheric Cherenkov telescopes, thanks to their relatively wider field of view. Moreover, we show that the low energy threshold of Large-Sized Telescope is essential in discovering the VHE component at much higher redshifts, typical of long GRBs.

astro-ph.HE↗

arXiv2025

Fast Variational Bayes for Large Spatial Data

Recent variational Bayes methods for geospatial regression, proposed as an alternative to computationally expensive Markov chain Monte Carlo (MCMC) sampling, have leveraged Nearest Neighbor Gaussian processes (NNGP) to achieve scalability. Yet, these variational methods remain inferior in accuracy and speed compared to spNNGP, the state-of-the-art MCMC-based software for NNGP. We introduce spVarBayes, a suite of fast variational Bayesian approaches for large-scale geospatial data analysis using NNGP. Our contributions are primarily computational. We replace auto-differentiation with a combination of calculus of variations, closed-form gradient updates, and linear response corrections for improved variance estimation. We also accommodate covariates (fixed effects) in the model and offer inference on the variance parameters. Simulation experiments demonstrate that we achieve comparable accuracy to spNNGP but with reduced computational costs, and considerably outperform existing variational inference methods in terms of both accuracy and speed. Analysis of a large forest canopy height dataset illustrates the practical implementation of proposed methods and shows that the inference results are consistent with those obtained from the MCMC approach. The proposed methods are implemented in publicly available Github R-package spVarBayes.

stat.CO↗

arXiv2025

Calibration requirement for Epoch of Reionization 21-cm signal observation -- III. Bias and variance in uGMRT ELAIS-N1 field power spectrum

Power spectrum of \HI 21-cm radiation is one of the promising probes to study large scale structure of the universe and understand galaxy formation and evolution. The presence of foregrounds, that are orders of magnitude larger in the same frequency range of the redshifted 21-cm signal has been one of the largest observational challenges. The foreground contamination also hinders the calibration procedures and introduces residual calibration errors in the interferometric data. It has been shown that the calibration errors can introduce bias in the 21-cm power spectrum estimates and introduce additional systematics. In this work, we assess the efficacy of 21-cm power spectrum estimation for the uGMRT Band-3 observations of the ELAIS-N1 field. We first evaluate the statistics of the residual gain errors and perform additional flagging based on these statistics. We then use an analytical method to estimate the bias and variance in the power spectrum. We found that (a) the additional flagging based on calibration accuracy help reduce the bias and systematics in the power spectrum, (b) the majority of the systematics at the lower angular scales, $\ell < 6000$, are due to the residual gain errors, (c) for the uGMRT baseline configuration and system parameters, the variance is always higher than the bias in the power spectrum estimates. Based on our analysis we observe that for an angular multipole of $\ell \sim3000$, $2000$ hours of `on source time' is required with the uGMRT to detect redshifted 21-cm signal at $3-σ$ significance from a redshift of $2.55$. In this work we only consider the power spectrum measurement in the plane of the sky, an assessment of residual gain statistics and its effect on multifrequency angular power spectrum estimation for the uGMRT and the SKA like telescopes will be presented in a companion paper.

astro-ph.IM↗

arXiv2025

How the Shortest and Longest HILDCAAs Shaped Earth Outer Radiation Belt During the Van Allen Probes Era?

High-intensity long-duration continuous auroral electrojet (AE) activity (HILDCAA) events are associated with the enhancement of relativistic electron fluxes in the inner magnetosphere. The physical mechanisms underlying this enhancement are not well established yet. In this study, we analyze two contrasting HILDCAA events, one representing the shortest and the other the longest duration, using NASA Van Allen Probes observations, which have provided unprecedented, unique in-situ observations of the harsh radiation environment around the Earth. Detailed spectral and temporal analyses reveal that while both events trigger enhancements in electron flux across multiple energy channels, the shortest event is characterized by rapid, transient increases in energy levels. In contrast, the longest event produced sudden and markedly higher flux variation. The long duration event showed an acceleration of electrons to higher energy as compared to the shorter one. Moreover, a clear correlation between elevated ULF wave power for the longest event compared to the shortest is observed, apart from chorus waves responsible for relativistic electron acceleration. These findings underscore the importance of considering the duration of events in space weather models and assessment and provide valuable insights into the magnetospheric processes that modulate the variability of the radiation belt during HILDCAA conditions.

physics.space-ph↗

arXiv2025

Exploring the Dynamical Nature and Radio Halo Emission of Abell 384: A Combined Radio, X-ray and Optical Study

Multiwavelength studies of galaxy clusters are crucial for understanding the complex interconnection of the thermal and non-thermal constituents of these massive structures and uncovering the physical processes involved in their formation and evolution. Here, we report a multiwavelength assessment of the galaxy cluster A384, which was previously reported to host a radio halo with a 660 kpc size at MeerKAT 1.28 GHz. The halo is slightly offset from the cluster centre. Our uGMRT observation reveals that the halo extends up to 690 kpc at 407 MHz with a nonuniform spectral index $α^{1284\ \mathrm{MHz}}_{407\ \mathrm{MHz}}$ distribution varying from flat (-0.5) to steep (-1.3) values. In addition, we use legacy GMRT 608 MHz, \textit{XMM-Newton} X-ray, and the Dark Energy Survey optical observations to obtain an extensive understanding of the dynamical nature of the galaxy cluster. The X-ray surface brightness concentration parameter ($C_{\rm{SB}} = 0.16$) and centroid shift ($w = 0.057$) reveal an ongoing dynamical disturbance in the cluster. This is also supported by the elongated 2-D optical galaxy density distribution map of the cluster. The obtained centre shift between optical and X-ray peaks and the asymmetry parameter from optical analysis further supports the dynamical disturbance in the cluster. The radio and X-ray surface brightness follows a sub-linear correlation. Our observations suggest that the cluster is currently in a merging state where particle re-acceleration in the turbulent ICM resulted in the radio halo emission.

astro-ph.CO↗

arXiv2025

Ionospheric effect on the synthetic Epoch of Reionization observations with the SKA1-Low

The redshifted $21$\,cm signal of neutral hydrogen can be used as a direct probe of the intergalactic medium during Cosmic Dawn\,(CD) and Epoch of Reionization\,(EoR). However, detecting this inherently weak signal has numerous challenges. The major ones include accurate foreground removal from low-frequency radio observations and systematics arising from instrumental effects. The Earth's ionosphere poses a major obstacle at these low radio frequencies. Thus, a systematic study of ionospheric effects on these sensitive low-frequency observations is critical, given that the construction of the Square Kilometre Array (SKA1-Low) is in full progress. We use the end-to-end pipeline, called {\sc 21cme2e}, to study the effect of time-varying ionospheric corruption on the $21$\,cm power spectrum recovery. We use two models: a) a catalogue-based model focused on source position shift due to the refractive effect of the ionosphere and b) a realistic ionospheric condition generated using Kolmogorov's turbulence model. We assess the effect of the imperfections thus generated on the extraction of \HI\ $21$\,cm signal power spectrum. Our study shows that beyond ``median ionospheric offset" ($θ_{\text{MIO}} \lesssim 0.1''$), the $21$\,cm signal from the EoR is unaffected by residual ionospheric effects. Our study emphasizes the need for the development of efficient ionospheric calibration algorithms for the upcoming SKA1-Low observations to extract the \HI\ $21$\,cm power spectra from the CD/EoR.

astro-ph.CO↗

arXiv2025

Insights into Solar Wind Flow Speeds from the Coronal Radio occultation Experiment: Findings from the Indian Mars Orbiter Mission

Using data collected by the Indian Mars Orbiter Mission in October 2021, we investigated coronal regions of the Sun by analyzing the Doppler spectral width of radio signals to estimate solar wind velocity. A simplified equation is introduced to directly relate these two parameters. The study focuses on observations conducted from October 2 to October 14, 2021, a relatively quiet phase of solar cycle 25. The analysis targeted the coronal region within heliocentric distances of 5-8 RSun, near the ecliptic plane. In this region, solar wind velocities ranged from 100 to 150 kms^-1, while electron densities were on the order of 10^10 m^(-3). We also compared our results with electron density observations and models derived from previous studies. Though the decrease in the electron densities with respect to increasing helio-centric distance matches quite well with the theoretical models, MOM estimates fall at the lower edge of the distribution. This difference may be attributed to the prolonged weak solar activity during the MOM observations, in contrast to prior studies conducted during periods of comparatively higher solar activity in earlier solar cycles.

astro-ph.SR↗

arXiv2025

Random forests for binary geospatial data

The manuscript develops new method and theory for non-linear regression for binary dependent data using random forests. Existing implementations of random forests for binary data cannot explicitly account for data correlation common in geospatial and time-series settings. For continuous outcomes, recent work has extended random forests (RF) to RF-GLS that incorporate spatial covariance using the generalized least squares (GLS) loss. However, adoption of this idea for binary data is challenging due to the use of the Gini impurity measure in classification trees, which has no known extension to model dependence. We show that for binary data, the GLS loss is also an extension of the Gini impurity measure, as the latter is exactly equivalent to the ordinary least squares (OLS) loss. This justifies using RF-GLS for non-parametric mean function estimation for binary dependent data. We then consider the special case of generalized mixed effects models, the traditional statistical model for binary geospatial data, which models the spatial random effects as a Gaussian process (GP). We propose a novel link-inversion technique that embeds the RF-GLS estimate of the mean function from the first step within the generalized mixed effects model framework, enabling estimation of non-linear covariate effects and offering spatial predictions. We establish consistency of our method, RF-GP, for both mean function and covariate effect estimation. The theory holds for a general class of stationary absolutely regular dependent processes that includes common choices like Gaussian processes with Matérn or compactly supported covariances and autoregressive processes. The theory relaxes the common assumption of additive mean functions and accounts for the non-linear link. We demonstrate that RF-GP outperforms competing methods for estimation and prediction in both simulated and real-world data.

stat.ME↗

arXiv2024

Deciphering the spectral properties of the atypical radio relic in A115 using uGMRT, VLA, and LOFAR

The mega-parsec scale radio relics at the galaxy cluster periphery are intriguing structures. While textbook examples of relics posit arc-like elongated structures at the clusters' peripheries, several relics display more complex structures deviating from the conventional type. Abell 115 is a galaxy cluster, hosting an atypical radio relic at its northern periphery. Despite the multi-wavelength study of the cluster over the last decades, the origin of the radio relic is still unclear. In this paper, we present a multi-frequency radio study of the cluster to infer the possible mechanism behind the formation of the radio relic. We used new 400 MHz observations with the uGMRT, along with archival VLA 1.5 GHz observations and archival LOFAR 144 MHz observations. Our analysis supports the previous theory on the relic's origin from the passage of a shock front due to an off-axis merger, where the old population of particles from the radio galaxies at the relic location has been re-energised to illuminate the 2 Mpc radio relic.

astro-ph.CO↗

arXiv2024

Impact of High Intensity Long-Duration Continuous Auroral Electrojet Activity (HILDCAAs) on relativistic electrons of the radiation belt of Earth during Van Allen probe era

This study investigates the impact of High-intensity Long-Duration Continuous Auroral Electrojet Activity (HILDCAA) on the relativistic electrons in radiation belt of Earth. Utilizing data from Van Allen Probe mission of NASA, we conducted a comprehensive statistical analysis to understand the impact of HILDCAA events on the radiation belt fluxes. The super epoch analysis was carried out to determine the general response of L-shell, pitch angle, and energy-dependency of relativistic electrons to HILDCAAs. The analysis reveals a significant flux enhancement in the relativistic electron fluxes, predominantly occurring with a delay of 0 to 2 days following the onset of HILDCAA events. The general response indicates that the maximum energy of accelerated electrons reaches up to 6 MeV. Additionally, electrons with perpendicular pitch angles exhibit a significantly greater enhancement in flux and achieve higher maximum acceleration energies compared to those with parallel pitch angles. The observed time-delayed and pitch angle-dependent response related to the onset of HILDCAAs highlights the significant influence of wave-particle interactions, particularly in relation to ultra-low frequency (ULF) waves in this context. This is further supported by ground-based magnetometers and in-situ magnetic field observations from the RBSP probe, which demonstrated enhanced power of ULF waves during HILDCAA events. The study strengthens our current understanding of radiation belt particle acceleration processes and has potential implications for satellite operations and other space-based technologies, both on Earth and in the magnetospheres of other planets.

physics.space-ph↗

arXiv2024

Comparing sampling techniques to chart parameter space of 21 cm Global signal with Artificial Neural Networks

Understanding the first billion years of the universe requires studying two critical epochs: the Epoch of Reionization (EoR) and Cosmic Dawn (CD). However, due to limited data, the properties of the Intergalactic Medium (IGM) during these periods remain poorly understood, leading to a vast parameter space for the global 21cm signal. Training an Artificial Neural Network (ANN) with a narrowly defined parameter space can result in biased inferences. To mitigate this, the training dataset must be uniformly drawn from the entire parameter space to cover all possible signal realizations. However, drawing all possible realizations is computationally challenging, necessitating the sampling of a representative subset of this space. This study aims to identify optimal sampling techniques for the extensive dimensionality and volume of the 21cm signal parameter space. The optimally sampled training set will be used to train the ANN to infer from the global signal experiment. We investigate three sampling techniques: random, Latin hypercube (stratified), and Hammersley sequence (quasi-Monte Carlo) sampling, and compare their outcomes. Our findings reveal that sufficient samples must be drawn for robust and accurate ANN model training, regardless of the sampling technique employed. The required sample size depends primarily on two factors: the complexity of the data and the number of free parameters. More free parameters necessitate drawing more realizations. Among the sampling techniques utilized, we find that ANN models trained with Hammersley sequence sampling demonstrate greater robustness compared to those trained with Latin hypercube and Random sampling.

astro-ph.IM↗

arXiv2024

On the Low-Latitude Ionospheric Irregularities under Geomagnetically Active and Quiet Conditions using NavIC observables: A Spectral Analysis Approach

Ionospheric irregularities and associated scintillations under geomagnetically active/quiet conditions have detrimental effects on the reliability and performance of space- and ground-based navigation satellite systems, especially over the low-latitude region. The current work investigates the low-latitude ionospheric irregularities using the phase screen theory and the corresponding temporal Power Spectral Density (PSD) analysis to present an estimate of the outer irregularity scale sizes over these locations. The study uses simultaneous L5 signal C/N$_o$ observations of NavIC (a set of GEO and GSO navigation satellite systems) near the northern crest of EIA (Indore: 22.52$^\circ$N, 75.92$^\circ$E, dip: 32.23$^\circ$N) and in between the crest and the dip equator (Hyderabad: 17.42$^\circ$N, 78.55$^\circ$E, dip: 21.69$^\circ$N). The study period (2017-2018) covers disturbed and quiet-time conditions in the declining phase of the solar cycle 24. The PSD analysis brings forward the presence of irregularities, of the order of a few hundred meters during weak-to-moderate and quiet-time conditions and up to a few km during the strong event, over both locations. The ROTI values validate the presence of such structures in the Indian region. Furthermore, only for the strong event, a time delay of scintillation occurrence over Indore, with values of 36 minutes and 50 minutes for NavIC satellites (PRNs) 5 and 6, respectively, from scintillation occurrence at Hyderabad is observed, suggesting a poleward evolution of irregularity structures. Further observations show a westward propagation of these structures on this day. This study brings forward the advantage of utilizing continuous data from the GEO and GSO satellite systems in understanding the evolution and propagation of the ionospheric irregularities over the low-latitude region.

physics.space-ph↗

arXiv2024

A causal inference framework for spatial confounding

Over the past few decades, addressing "spatial confounding" has become a major topic in spatial statistics. However, the literature has provided conflicting definitions, and many proposed solutions are tied to specific analysis models and do not address the issue of confounding as it is understood in causal inference. We offer an analysis-model-agnostic definition of spatial confounding as the existence of an unmeasured causal confounder variable with a spatial structure. We present a causal inference framework for nonparametric identification of the causal effect of a continuous exposure on an outcome in the presence of spatial confounding. In particular, we identify two critical additional assumptions that allow the use of the spatial coordinates as a proxy for the unmeasured spatial confounder: the measurability of the confounder as a function of space, which is required for conditional ignorability to hold, and the presence of a non-spatial component in the exposure, required for positivity to hold. We also propose studying a causal estimand based on a "shift intervention" that requires less stringent identifying assumptions than traditional estimands. We then turn to estimation and focus on "double machine learning" (DML), a procedure in which flexible models are used to regress both the exposure and outcome variables on confounders to arrive at a causal estimator with favorable robustness properties and convergence rates. This procedure avoids restrictive assumptions, such as linearity and effect homogeneity, which are typically made in spatial models and which can lead to bias when violated. We demonstrate the advantages of the DML approach analytically and via extensive simulation studies. We apply our methods and reasoning to a study of the effect of fine particulate matter exposure during pregnancy on birthweight in California.

stat.ME↗

arXiv2024

Graph-constrained Analysis for Multivariate Functional Data

Functional Gaussian graphical models (GGM) used for analyzing multivariate functional data customarily estimate an unknown graphical model representing the conditional relationships between the functional variables. However, in many applications of multivariate functional data, the graph is known and existing functional GGM methods cannot preserve a given graphical constraint. In this manuscript, we demonstrate how to conduct multivariate functional analysis that exactly conforms to a given inter-variable graph. We first show the equivalence between partially separable functional GGM and graphical Gaussian processes (GP), proposed originally for constructing optimal covariance functions for multivariate spatial data that retain the conditional independence relations in a given graphical model. The theoretical connection help design a new algorithm that leverages Dempster's covariance selection to calculate the maximum likelihood estimate of the covariance function for multivariate functional data under graphical constraints. We also show that the finite term truncation of functional GGM basis expansion used in practice is equivalent to a low-rank graphical GP, which is known to oversmooth marginal distributions. To remedy this, we extend our algorithm to better preserve marginal distributions while still respecting the graph and retaining computational scalability. The insights obtained from the new results presented in this manuscript will help practitioners better understand the relationship between these graphical models and in deciding on the appropriate method for their specific multivariate data analysis task. The benefits of the proposed algorithms are illustrated using empirical experiments and an application to functional modeling of neuroimaging data using the connectivity graph among regions of the brain.

stat.ME↗