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Abhirup Datta

Publications and source records attributed to Abhirup Datta.

At least 37 records · Page 2Linked to original sources
arXiv2026

Probing Merger Shocks in Galaxy Clusters in the SKA Era

Galaxy cluster mergers represent the most energetic phenomena in the Universe since the Big Bang releasing gravitational potential energy of $\sim 10^{63-64}$ erg, injecting turbulence and driving shocks through the intracluster medium (ICM). These merger shocks can accelerate cosmic ray electrons and compress magnetic fields, sometimes producing Mpc-scale synchrotron radio structures known as radio relics. Radio relics are powerful tracers of merger dynamics, particle acceleration, and magnetic field evolution, yet fundamental questions about the underlying physics and their time evolution remain unresolved. In this chapter, we review the current understanding of cluster merger shocks and their radio signatures, presenting the observational evidence from the SKA pathfinders and precursors, linking radio relics to shocks alongside outstanding theoretical challenges. We also consider related shock-influenced phenomena: radio phoenices from revived fossil AGN plasma and Gently Re-Energised Tails. Further, we outline the directions of investigation using the sensitivities of the SKA-Low and Mid complemented with X-ray observations that will allow us to make significant progress in understanding the cluster merger shocks. Detailed studies of individual targets in continuum and polarization and studies of populations of relics using wide surveys will both provide insights to the micro-physics and cosmic evolution of merger shocks. We present SKAO capabilities across staged deployments starting from AA0.5 to AA4 and identify science verification targets that will illuminate the physics of cluster merger shocks in the SKA era

astro-ph.HE↗
arXiv2026

Observational Frontiers in the post-EoR 21-cm Intensity Mapping: Lessons from the SKA Pathfinders

The 21-cm line from neutral hydrogen has long been recognised as a promising tracer of the large-scale structure of the Universe. The line is weak however, making individual galaxy detections quite inefficient, especially at higher redshifts. The technique of 21-cm intensity mapping has been pioneered over the last two decades to address this limitation. Instead of detecting individual galaxies, the brightness temperature field from the combined 21-cm emission of many unresolved galaxies is mapped as a function of angle and frequency, resulting in 3D tracer maps of the large-scale structure. In this chapter, we review the major pioneering efforts to develop this observable into a competitive cosmological tool, paying particular attention to the status of pathfinder observations that have paved the way for a large and highly sensitive 21-cm intensity mapping survey with the SKA-Mid telescope.

astro-ph.CO↗
arXiv2026

Searching for a superdisk in radio galaxy J0116-473

Superdisks have emerged as an active area of research in recent years, and J0116-473 represents a promising target for studying this extended structure. Our primary objective was to search for HI absorption associated with the suspected superdisk. However, no such absorption feature was detected, suggesting a low, or absence of neutral hydrogen content in the superdisk. In addition, we examined a compact point source located near the galaxy's core and the presumed plane of the superdisk, enabling us to search for HI absorption against this background continuum. We also present a detailed multi-band morphological analysis of the galaxy using Giant Metrewave Radio Telescope (GMRT) observations in Bands 3, 4, and 5. A spectral analysis of both the galaxy and the nearby point source was carried out using data from these three frequency bands. A systematic steepening of the spectral index is observed from the core toward the lobes, as expected for aging synchrotron-emitting plasma. We also found that the northern inner lobe exhibits a significantly steeper spectrum than its southern counterpart, possibly reflecting environmental effects associated with the proposed superdisk. Since superdisks are expected to contain hot, ionized gas, we additionally examined archival X-ray observations from the XMM-Newton telescope. Although diffuse X-ray emission associated with the radio lobes is visible, no significant emission is detected from the region corresponding to the suspected superdisk.

astro-ph.GA↗
arXiv2026

Source apportionment of air pollution burden using geometric non-negative matrix factorization and high-throughput multi-pollutant air sensor data in Curtis Bay, Baltimore, USA

Air sensor networks provide hyperlocal, high-frequency data on multiple pollutants, but unlike speciated particulate matter (PM) measurements, they lack direct chemical signatures for source identification. High temporal resolution and multiple spatial locations nonetheless create new opportunities to interpret latent sources through their relationships with spatial proximity to known origins, temporal patterns, and meteorology. We analyze 451946 one-minute air sensor records from Curtis Bay (Baltimore, USA; October 2022 - June 2023), covering size-resolved PM, black carbon (BC), carbon monoxide (CO), nitric oxide (NO), and nitrogen dioxide (NO2), using a geometric non-negative matrix factorization (NMF) approach that scales to large datasets and yields provably unique source attribution percentages. Three stable latent sources emerge with converging evidence toward recognizable source categories: Source 1 explains $>$ 70% of fine and coarse PM and $\sim$30% of BC; Source 2 dominates CO and contributes $\sim$70% of BC, NO, and NO2; Source 3 is specific to the larger PM fractions, PM10 to PM40. Regression analyses and a case study on a known bulldozer incident link Sources 1 and 3 to a nearby coal terminal. Extreme-intensity episodes from Sources 1 and 3 averaged $\sim$33 and $\sim$24 minutes per day at the site nearest the terminal, attenuating with distance. Source 2 reflects diurnal traffic patterns. Together, these results show that dense air sensor networks paired with the geometric NMF method can move community air monitoring beyond pollution detection toward identifying likely source categories and informing actionable mitigation strategies.

stat.AP↗
arXiv2026

Tracing Large-Scale Structure Morphology with Multiwavelength Line Intensity Maps

Line intensity mapping (LIM) is an emerging technique for probing the large-scale structure (LSS) in the post-reionisation era. This captures the integrated flux of a particular spectral line emission from multiple sources within a patch of the sky without resolving them. Mapping different galaxy line emissions, such as the HI $21$-cm and CO rotational lines via LIM, can reveal complementary information about the bias with which the line emitters trace the underlying matter distribution and how different astrophysical phenomena affect the clustering pattern of these signals. The stage at which the structures in the "cosmic web" merge to form a single connected structure is known as the percolation transition. Using mock HI $21$-cm and CO($1-0$) LIM signals in the post-reionisation universe, we explore the connectivity of structures through percolation analysis and compare it with the underlying galaxy distribution. We probe the relative contributions of voids, filaments, and sheets to the galaxy density and line intensity maps using a morphological measure known as the local dimension. The CO($1-0$) map exhibits an increased filamentary behaviour and larger contribution from sheets than the $21$-cm map. We attempt to explain such an emission of the CO($1-0$) line from biased environments. The upcoming SKA-Mid will produce tomographic intensity maps of the $21$-cm signal at $z \lesssim 3$ in Band-1. CO maps can be produced at these redshifts in phase 2 of SKA-Mid, where the frequency coverage is expected to increase up to $\sim 50$ GHz. We present forecasts for the recovery of the local dimensions of these line intensity maps contaminated by thermal noise and line interlopers in SKA-Mid surveys.

astro-ph.CO↗
arXiv2026

Consistent Infill Estimability of the Regression Slope Between Gaussian Random Fields Under Spatial Confounding

The problem of estimating the slope parameter in regression between two spatial processes under confounding by an unmeasured spatial process has received widespread attention in the recent statistical literature. Yet, a fundamental question remains unresolved: when is this slope consistently estimable under spatial confounding, with existing insights being largely empirical or estimator-specific. We characterize conditions for consistent estimability of the regression slope between Gaussian random fields (GRFs), the common stochastic model for spatial processes, under spatial confounding. Under fixed-domain (infill) asymptotics, we give sufficient conditions for consistent estimability in terms of the smoothness or local behavior of the exposure and confounder processes. When estimability holds, we provide consistent estimators of the slope using local differencing (taking discrete differences or Laplacians of the processes of suitable order). Using functional analysis results on Paley-Wiener spaces, we then provide an easy-to-verify necessary condition for consistent estimability of the slope in terms of the relative spectral tail decays of the confounder and exposure. As a by-product, we establish a novel and general spectral condition on the equivalence of measures on the paths of multivariate GRFs with component fields of varying smoothnesses. We show that for many covariance classes like the Matérn, power-exponential, generalized Cauchy, and coregionalization families, the necessary and sufficient conditions become identical, thereby providing a sharp characterization of consistent estimability of the slope for these processes. The results are extended to multivariate slopes, to accommodate measurement error, to popular classes of non-stationary Gaussian random fields and some non-Gaussian random fields, and for irregular designs.

math.ST↗
arXiv2026

Probing Reionization up to the Mark: The Marked Power Spectrum to unveil the HI 21-cm signal from the EoR

The redshifted 21-cm signal from neutral hydrogen (HI) in the intergalactic medium (IGM) is a powerful probe of the Epoch of Reionization (EoR). Owing to the complex growth and morphology of ionized regions, the 21-cm brightness-temperature field becomes strongly non-Gaussian during the EoR, limiting the information captured by the standard power spectrum alone. While higher-order statistics such as the bispectrum can recover part of this information, they are computationally expensive and often less straightforward to interpret. In this work, we investigate marked statistics as an alternative framework for characterizing the EoR 21-cm signal. Using semi-numerical 21-cm simulations, we introduce a set of EoR-tailored mark functions, inspired by and extending existing marked-statistics ideas, and study both the power spectrum of the mark and that of the marked field. We show that suitably chosen marks can selectively enhance contributions from different IGM environments, capture additional non-Gaussian information beyond the standard power spectrum, and improve the statistical constraining power on EoR model parameters in a Fisher-matrix analysis. These results demonstrate that Fourier-space marked statistics, including both the power spectrum of the mark and that of the marked field, provide a computationally simple and flexible extension of standard two-point statistics for extracting astrophysical information from the EoR 21-cm signal.

astro-ph.CO↗
arXiv2026

Mitigating residual foregrounds and systematic errors in SKA1-Low AA* EoR observations via Bayesian Gaussian Process Regression

The redshifted 21\,cm line is an emerging tool in observational cosmology that can serve as a direct probe of the intergalactic medium throughout the cosmic timeline. However, the observation of the cosmological 21\,cm signal from early epochs is extremely challenging in practice, regardless of the scale of interest and redshift. The presence of bright astrophysical foregrounds and residual systematic errors along the line of sight poses challenges for its detection. Machine-learning-based Gaussian process regression\,(ML-GPR) has proven to be the most effective strategy for signal separation in LOFAR and NenuFAR observations to measure the 21\,cm signal power spectrum from the Cosmic Dawn\,(CD) and Epoch of Reionization\,(EoR). In this work, we extend this framework to synthetic CD/EoR SKA1-Low observations to assess its robustness in mitigating residual foregrounds against instrumental and environmental systematic effects. We use our developed end-to-end realistic simulation pipeline (\textsc{21cmE2E}) for SKA-Low observations. Our 4-hour tracking simulation includes extragalactic point sources, the AA* telescope configuration, primary beam response, and error models. The modelled errors incorporate residual antenna-based gain calibration errors, residual ionospheric phase errors, partial de-mixing of the out-of-field sources, and instrumental noise for 1000\,hours of deep integration time. We compare different Bayesian GPR frameworks to assess their ability to suppress residual foreground contamination while minimizing signal loss and providing reliable uncertainty estimates. Our analysis demonstrates that the 21\,cm signal can robustly recover within the $2σ$ credible interval for almost all k-modes over the range of $0.06 \leq k \leq 1.0$~h Mpc$^{-1}$.

astro-ph.CO↗
arXiv2026

Mitigating gain calibration errors from EoR observations with SKA1-Low AA*

The observations of the redshifted 21-cm signal from neutral hydrogen are a promising probe for understanding the Cosmic Dawn and the Epoch of Reionisation (EoR). One of the primary obstacles to the statistical detection of the Cosmological signal is the presence of residual foreground arising from gain calibration errors. Previous studies have shown that gain calibration errors as small as 0.01$\%$ can lead to a biased interpretation of the observed signal power spectrum estimation, by nearly an order of magnitude. A recent study further highlights that to accurately retrieve astrophysical parameters, the threshold gain calibration error should be below 0.01$\%$. This work investigates the impact of residual extragalactic foregrounds arising from gain calibration errors on the efficacy of foreground mitigation strategies. We use an end-to-end pipeline $\textsc{21cmE2E}$ to simulate a realistic sky model and telescope configuration within the 138-146 MHz frequency range and perform a detailed power spectrum analysis across several threshold levels of the gain calibration error. We introduce a hybrid mitigation technique that combines the foreground removal techniques, Gaussian process regression and principal component analysis, with foreground avoidance. Our results indicate that recovery of the \HI\ signal within 2$σ$ is possible for calibration gain error of $\leq 1\%$ with minimal loss of power spectrum sensitivity over the scale range $0.05 \leq k \leq 0.5$ Mpc$^{-1}$. We find that gain calibration errors beyond this threshold lead to signal suppression on large scales due to the loss of spectral smoothness of the residual foreground. In effect, this work offers a comparative assessment of three foreground mitigation strategies, removal, avoidance, and a hybrid approach, in the context of future SKA1-Low AA* observations.

astro-ph.CO↗
arXiv2026

A generalized method for estimating solar wind speeds and densities using spectral broadening for a Kolmogorov turbulence spectrum

We present a unified method to derive both solar wind velocities and coronal electron densities in the near-Sun corona using Doppler spectral broadening of spacecraft radio signals. The method is generalized to be frequency independent under the assumption that electron density fluctuations follow a Kolmogorov spectrum. We validate the approach using S-band data from India's Mars Orbiter Mission during the October 2021 superior conjunction at 5-8 R$_\odot$, and X-band data from Japan's Akatsuki during June 2016 and October 2022 conjunctions spanning 1.4-10 R$_\odot$. From S-band we obtained wind speeds of 100-150 km s$^{-1}$ and electron densities of order $10^{10}$ m$^{-3}$. X-band results show speeds ranging from $\sim$150 km s$^{-1}$ near the equator to $\sim$400 km s$^{-1}$ in coronal-hole regions, with consistent radial trends in density. We provide a compact, frequency-scaled relation that maps Doppler spectral width to both $v$ and $N_e$. The formulation enables consistent application across telecommunication bands and complements in-situ probes for coronal plasma studies.

astro-ph.SR↗
arXiv2026

A turbulence index independent framework for deriving solar wind speed and coronal electron density from radio spectral broadening

We present a turbulence index independent framework for simultaneously deriving solar wind velocity and coronal electron density in the near-Sun region using the spectral broadening of spacecraft radio signals. The formulation accommodates arbitrary turbulence spectral indices ($p$), providing a direct analytical link between the observed Doppler spectra and underlying plasma parameters without assuming a fixed turbulence regime. This generalization extends conventional radio occultation techniques and enables consistent interpretation across multiple radio frequencies. We apply the method to X-band ($\sim$ 8.41 GHz) radio occultation measurements from JAXA's Akatsuki spacecraft during the 2016 and 2022 Venus - Earth superior conjunctions, spanning heliocentric distances of 1.4 - 10 $R_{\odot}$ and sampling both equatorial streamer regions and mid-latitude coronal holes. The retrieved electron densities exhibit systematic trends consistent with empirical coronal models and in-situ observations. By coupling the measured spectral widths with a turbulence-based frequency-scaling relation, we obtain a compact expression that links spectral broadening, solar wind speed, and electron density, applicable for any turbulence index $p$. Fast-solar-wind intervals, characterized by nearly isotropic turbulence, yield speed estimates in close agreement with expectations, while the anisotropic nature of the slow solar wind introduces small but systematic deviations. Our results refine earlier work and demonstrate that explicit consideration of near-coronal turbulence anisotropy is essential for accurate solar-wind parameter retrievals.

astro-ph.SR↗
arXiv2026

VA-Calibration: Correcting for Algorithmic Misclassification in Estimating Cause Distributions

Accurate estimation of cause-specific mortality fractions (CSMFs), the percentage of deaths attributable to each cause in a population, is essential for global health monitoring. Challenge arises because computer-coded verbal autopsy (CCVA) algorithms, commonly used to estimate CSMFs, frequently misclassify the cause of death (COD). This misclassification is further complicated by structured patterns and substantial variation across countries. To address this, we introduce the R package 'vacalibration'. It implements a modular Bayesian framework to correct for the misclassification, thereby yielding more accurate CSMF estimates from verbal autopsy (VA) questionnaire data. The package utilizes uncertainty-quantified CCVA misclassification matrix estimates derived from data collected in the CHAMPS project and available on the 'CCVA-Misclassification-Matrices' GitHub repository. Currently, these matrices cover three CCVA algorithms (EAVA, InSilicoVA, and InterVA) and two age groups (neonates aged 0-27 days, and children aged 1-59 months) across countries (specific estimates for Bangladesh, Ethiopia, Kenya, Mali, Mozambique, Sierra Leone, and South Africa, and a combined estimate for all other countries), enabling global calibration. The 'vacalibration' package also supports ensemble calibration when multiple algorithms are available. Implemented using the 'RStan', the package offers rapid computation, uncertainty quantification, and seamless compatibility with openVA, a leading COD analysis software ecosystem. We demonstrate the package's flexibility with two real-world applications in COMSA-Mozambique and CA CODE. The package and its foundational methodology applies more broadly and can calibrate any discrete classifier or their ensemble.

stat.AP↗
arXiv2026

Interpreting the HI 21-cm cosmology maps through Largest Cluster Statistics III: Impact of the lightcone effect

The redshifted 21-cm signal emitted by neutral Hydrogen (HI) is a promising probe to understand the evolution of the topology of ionized regions during the Epoch of Reionization (EoR). The topology of ionized regions allows us to infer the nature and properties of ionizing sources, i.e., early galaxies and AGNs. Traditional Fourier statistics, such as the power spectrum, help us quantify the strength of fluctuations in this field at different length scales but do not preserve its phase information. Analyzing the 21-cm brightness temperature field in the image domain retains its non-Gaussian characteristics and morphological information. One such approach is to track the coalescence of multiple ionized regions to form one contiguous ionized region spanning the universe. This is referred to as percolation, and its onset is quantified by a sharp rise in the value of the Largest Cluster Statistic (LCS) approaching unity. In this work, we carry out a percolation analysis of 21-cm brightness temperature fields by studying the redshift evolution of the LCS along a lightcone to distinguish between several simulated reionization scenarios. We have extended previous results on reionization model comparison from the analysis of coeval 21-cm maps to understand how the lightcone effect biases the observed percolation behavior and affects the distinguishability of the source models. We estimate the LCS of subvolumes of different sizes in the 21-cm lightcone maps and study their redshift evolution for different reionization scenarios using a moving volume approach. We find that the percolation transition inferred from a lightcone approaches that from the coeval box as we increase the bandwidth of the moving volume in all but one reionization scenario.

astro-ph.CO↗
arXiv2025

Robust Spatial Confounding Adjustment via Basis Voting

Estimating effects of spatially structured exposures is complicated by unmeasured spatial confounders, which undermine identifiability in spatial linear regression models unless structural assumptions are imposed. We develop a general framework for effect estimation in spatial regression models that relaxes the commonly assumed requirement that exposures contain higher-frequency variation than confounders. We propose basis voting, a plurality-rule estimator - novel in the spatial literature - that consistently identifies causal effects only under the assumption that, in a spatial basis expansion of the exposure and confounder, there exist several basis functions in the support of the exposure but not the confounder. This assumption generalizes existing assumptions of differential basis support used for identification of the causal effect under spatial confounding, and does not require prior knowledge of which basis functions satisfy this support condition. We design this estimator as the mode of several candidate estimators each computed based on a single working basis function. We also show that the standard projection-based candidate estimator typically used in other plurality-rule based methods is inefficient, and provide a more efficient novel candidate. Extensive simulations and a real-world application demonstrate that our approach reliably recovers unbiased causal estimates whenever exposure and confounder signals are separable on a plurality of basis functions. By not relying on higher-frequency variation, our method remains applicable to settings where exposures are smooth spatial functions, such as distance to pollution sources or major roadways, common in environmental studies.

stat.ME↗
arXiv2025

Exploring Machine Learning Regression Models for Advancing Foreground Mitigation and Global 21cm Signal Parameter Extraction

Extracting parameters from the global 21cm signal is crucial for understanding the early Universe. However, detecting the 21cm signal is challenging due to the brighter foreground and associated observational difficulties. In this study, we evaluate the performance of various machine-learning regression models to improve parameter extraction and foreground removal. This evaluation is essential for selecting the most suitable machine learning regression model based on computational efficiency and predictive accuracy. We compare four models: Random Forest Regressor (RFR), Gaussian Process Regressor (GPR), Support Vector Regressor (SVR), and Artificial Neural Networks (ANN). The comparison is based on metrics such as the root mean square error (RMSE) and $R^2$ scores. We examine their effectiveness across different dataset sizes and conditions, including scenarios with foreground contamination. Our results indicate that ANN consistently outperforms the other models, achieving the lowest RMSE and the highest $R^2$ scores across multiple cases. While GPR also performs well, it is computationally intensive, requiring significant RAM and longer execution times. SVR struggles with large datasets due to its high computational costs, and RFR demonstrates the weakest accuracy among the models tested. We also found that employing Principal Component Analysis (PCA) as a preprocessing step significantly enhances model performance, especially in the presence of foregrounds.

astro-ph.CO↗
arXiv2025

ELAIS-N1 deep field uGMRT Band-2: constraints on diffuse Galactic synchrotron emission power spectrum

We present high sensitivity, low radio frequency continuum observations of the ELAIS-N1 field with 32 hours of observations of the uGMRT Band-2 ($120-250$ MHz) covering $5.86\,\text{deg}^2$ area, achieving a central off-source RMS noise of $237\,μ\mathrm{Jy}/\mathrm{beam}$ with a resolution of $11.45''$ at the central frequency of 183 MHz. A radio source catalogue of 1027 sources statistically matches with similar observations at different frequencies within the sensitivity range of the uGMRT. The calibrated data is further used to characterise the dominant foreground, the Diffuse Galactic Synchrotron Emission (DGSE), in angular scale and frequency regime. We derived the angular power spectrum (APS) of DGSE in two ways: image-based estimator (i-APS) and visibility-based Tapered Gridded Estimator (TGE; hereafter as t-APS). We assess the characteristics of DGSE with a power-law form of $C_{\ell} = A({1000}/{\ell})^β$. Combining data from Band-2 and earlier Band-3 observations, we derived a spectral variation of $C_{\ell}$ in the form of $C_{\ell} = A{ν^{-2α}}{\ell^{-β}}$. Our result indicates a spectral break at $ν= 230\,{\pm}\,5$ MHz, corresponding to a synchrotron age of $t_\text{syn} = 106\,{\pm}\,1$ Myr for the cosmic-ray electrons (CRe). This break result suggests a low-energy cutoff in the CRe population, leading to spectral curvature at low frequencies. Using both of the techniques, i-APS and t-APS, we find that the mean spectral index $α$ and power-law index $β$ are consistent within the frequency range $120-500$ MHz.

astro-ph.HE↗
arXiv2025

Interpreting the HI 21-cm cosmology maps through Largest Cluster Statistics. Part II. Impact of the realistic foreground and instrumental noise on synthetic SKA1-Low observations

The Largest Cluster Statistics\,(LCS) analysis of the redshifted 21\,cm maps has been demonstrated to be an efficient and robust method for following the time evolution of the largest ionized regions\,(LIRs) during the Epoch of Reionization\,(EoR). The LCS can, in principle, constrain the reionization model and history by quantifying the morphology of neutral hydrogen\,(\HI) distribution during the different stages of the EoR. Specifically, the percolation transition of ionized regions, quantified and constrained via LCS, provides a crucial insight about the underlying reionization model. The previous LCS analysis of EoR 21\,cm maps demonstrates that the convolution of the synthesized beam of the radio interferometric arrays, e.g. SKA1-Low with the target signal, shifts the apparent percolation transition of ionized regions towards the lower redshifts. In this study, we present an optimal thresholding strategy to reduce this bias in the recovered percolation transition. We assess the robustness of LCS analysis of the 21\,cm maps, considering the effects of antenna-based gain calibration errors and instrumental noise for SKA1-Low. This analysis is performed using synthetic observations simulated by the \textsc{21cmE2E} pipeline, considering SKA1-Low AA4 configuration within a radius of 2\,km from the array centre. Our findings suggest that a minimum of $2000$\,hours of observation (SNR $\gtrapprox 3$) are required for the LCS analysis to credibly suppress the confusion introduced by thermal noise. Further, we also demonstrate that for a maximum antenna-based calibration error tolerance of $\sim 0.02\%$ (post calibration), the reionization history can be recovered in a robust and relatively unbiased manner using the LCS.

astro-ph.CO↗
arXiv2025

Illuminating the Diffuse Radio Emission in Low-Mass Cluster: Abell 13

Recent advances in high-sensitivity radio observations have uncovered a population of faint, ultra-steep-spectrum sources in galaxy clusters, commonly known as radio phoenixes. However, their observational classification remains poorly constrained due to the limited number of confirmed detections. This study presents a detailed multi-frequency, high-sensitivity, and high-resolution analysis of diffuse radio emission in the merging galaxy cluster Abell 13. Using GMRT (147.5 MHz), uGMRT (400 MHz), ASKAP-low (887.5 MHz), and MGCLS (1284 MHz) images, we detect complex, filamentary diffuse emission with a largest linear extent of 521 kpc. This emission originates from the cluster center and extends westward, confined within the X-ray-emitting intra-cluster medium (ICM). Chandra X-ray data confirm that Abell 13 is undergoing a merger, and the radio morphology reflects signatures of this ongoing dynamical activity. We observed filamentary structures extending towards east-northeast and southwest directions. The spectral index across the emission appears irregular and lacks a coherent spatial gradient. The integrated spectrum reveals a steep spectral index of -1.85 +/- 0.05 and a spectral curvature of -0.93 +/- 0.21. These spectral properties, along with the observed morphology and brightness distribution, are consistent with a re-energization of a fossil radio plasma driven by adiabatic compression, supporting the classification of the emission as a radio phoenix.

astro-ph.CO↗