arXiv subjects

Anshuman Tripathi

Publications and source records attributed to Anshuman Tripathi.

At least 19 recordsLinked to original sources

arXiv2026

Foreground Characterization and Mitigation in the Observations of the CD/EoR with the SKA

The Square Kilometre Array (SKA), with its unprecedented sensitivity, frequency coverage, and large collecting area, is poised to revolutionize our understanding of the Cosmic Dawn (CD) and Epoch of Reionization (EoR) epochs marking the formation of the first luminous sources and the subsequent reionization of the intergalactic medium (IGM). However, detecting the faint redshifted 21-cm signal from neutral hydrogen remains one of the foremost challenges in observational cosmology, as it is buried beneath bright foregrounds from Galactic synchrotron radiation, free-free emission, and extragalactic point sources that are 4-5 orders of magnitude stronger than the cosmological signal. In this chapter, we highlight the key components and characteristics of these foregrounds and review ongoing efforts to model, characterize, and mitigate them. We emphasize how the SKA-Low AA* configuration, through its optimized array design, wide field of view, and improved calibration accuracy, enhances our capacity to suppress foreground contamination and recover the cosmological signal. The SKA Observatory Foreground Challenge plays a pivotal role in this effort by bringing together the global EoR/CD community to develop, compare, and validate foreground removal pipelines using realistic simulated datasets. Building on the experience of existing pathfinders such as LOFAR, MWA, and HERA, these collaborative initiatives are helping refine statistical and machine learning-based approaches for signal recovery. Together, these advancements are laying the groundwork for the SKA to probe the thermal and ionization history of the early Universe with unprecedented precision.

astro-ph.CO↗

arXiv2026

Machine Learning and the SKA for Cosmic Dawn and the Epoch of Reionization

When operational, the SKA will generate unprecedented amounts of data and provide exquisite sensitivity for 21 cm tomography of Cosmic Dawn (CD) and the Epoch of Reionization (EoR). With this comes opportunities for new data-driven algorithms that unlock new methods for instrument modelling, data analysis, theoretical simulation, and inference for understanding the high-redshift universe. In this chapter, we provide an overview of some machine learning algorithms that have been proposed for CD and EoR science with the SKA

astro-ph.IM↗

arXiv2026

Studying Ionosphere Using SKA-Low and SKA-Mid

The Earth's ionosphere introduces systematic effects that limit the performance of radio interferometers operating at low frequencies ($\lesssim 1$\,GHz). These ionospheric effects intensify during periods of heightened geomagnetic activity or for observations with extended baseline configurations. As each Pathfinder telescope operates at a different magnetic latitude, they experience distinct ionospheric regimes, offering complementary insights into ionospheric behaviour. In this work, we present a comparative study of ionospheric disturbances using observations from the uGMRT, VLA, MWA, and LOFAR, spanning a wide range of geographic and geomagnetic conditions. We present both antenna-based and field-based analyses to quantify phase fluctuations, positional offsets, and scintillation effects across these arrays. The measured total electron content (TEC) gradients reveal variations in spatial and temporal ionospheric structures with sensitivities that exceed those achievable with Global Navigation Satellite System (GNSS) measurements. By combining multi-telescope results, we assess the impact of ionospheric turbulence on calibration and imaging fidelity, and use these findings to forecast the expected ionospheric effects on observations with SKA-Low and SKA-Mid.

astro-ph.IM↗

arXiv2026

Cosmology with Intensity Mapping via Statistics Beyond the Power Spectrum in the SKAO Era

The cosmological distribution of neutral hydrogen (HI) during the post-reionization era is highly non-Gaussian due to the underlying non-linear structure formation, complex galaxy biasing, and potential primordial non-Gaussianity. One needs higher-order (beyond two-point) statistics to maximally extract the non-Gaussian information out of the 21-cm intensity maps. This chapter summarizes the potential of several higher-order statistics, including voxel intensity distribution, emission line stacking, probability density functions, $\ell_1$-norm, bispectrum, and various marked statistics. Additionally, image-based morphological descriptors, such as the Largest Cluster Statistic, local dimensions, and Minkowski functionals, etc., can potentially characterize the morphology and geometry of the cosmic web encoded in the 21-cm intensity maps. This chapter presents forecasts of the detectability of these higher-order statistics in the context of the future SKAO observations. These forecasts incorporate instrumental noise, observational effects, and, in some cases, foreground removal in their analyses. With its unprecedented sensitivity, the future SKAO 21-cm observations will enable us to measure these higher-order statistics more precisely, possibly helping to break degeneracies between astrophysical and cosmological parameters, and maximizing the science outcome from these surveys.

astro-ph.CO↗

arXiv2026

Cosmology with Multi-Wavelength Line Intensity Mapping Synergies in the SKAO Era

Line intensity mapping (LIM) has emerged as a powerful tool for surveying the large-scale structure of the Universe across cosmic time by measuring spatial fluctuations in the cumulative emission of spectral lines from unresolved sources or the intergalactic medium. Besides the most abundant 21-cm hyperfine line of neutral hydrogen, there are bright far-infrared fine-structure lines like [CII] 158 $μ$m, [OIII] 88 $μ$m, [NII] 122/205 $μ$m, and [OI] 63 $μ$m, as well as mid-/high-$J$ CO rotational transitions, hydrogen Ly$α$ and H$α$ as potential LIM probes. A key opportunity lies in combining and cross-correlating 21-cm intensity maps from SKAO with other line intensity maps, targeted by a range of ongoing and forthcoming LIM experiments that probe overlapping cosmic volumes. Cross-correlation between 21-cm maps and other line tracers mitigates uncorrelated systematics and enhances sensitivity to the underlying matter distribution, while multi-line analyses help disentangle cosmological and astrophysical parameters. Beyond cross-power spectra, higher-order and morphological statistics -- such as cross-bispectra, marked correlations, and morphological measures -- capture non-Gaussian features and the environmental dependence of structure formation. This chapter explores the synergies that can be achieved by combining SKAO observations with other line-intensity mapping experiments, demonstrating how such joint analyses can unlock new insights into galaxy evolution and cosmology.

astro-ph.CO↗

arXiv2026

Overview of 21cm Experiments at high redshift with SKAO

We provide an overview of the eight SKAO Science Book chapters that motivate the Epoch of Reionisation and Cosmic Dawn experiments with SKA-Low. We describe the individual SKA-Low experiments and expected sensitivity - power spectrum, tomography, 21-cm forest, cross-correlations, building on the broad observational plan laid out in the 2015 SKA Science Book. Finally, we outline features of the telescope that will be critical for the success of EoR/CD science, e.g., beam apodization, substations, and multi-beaming.

astro-ph.CO↗

arXiv2026

Inferring Cosmology and Astrophysics from the High-redshift 21cm Signal with SKA-Low

The Square Kilometre Array's low frequency telescope (SKA-Low) will enable inference of astrophysical and cosmological parameters from the redshifted 21 cm signal, probing the Cosmic Dawn and Epoch of Reionisation. While the power spectrum is the primary target for initial detection, the inherently non-Gaussian nature of the 21 cm signal, driven by the patchy evolution of ionised regions and spin temperature fluctuations, encodes rich information accessible through higher-order statistics and morphological measurements. Extracting these constraints requires diverse inference tools, encompassing both sophisticated modelling frameworks (analytical, semi-numerical, numerical, and emulators) used to predict the 21 cm signal, and advanced inference techniques (Bayesian, simulation-based, field-level) to connect statistics to the underlying physics. This chapter reviews these tools and explores the constraining power of different statistical probes accessible with SKA-Low, including the power spectrum, statistics beyond order two, moments of the signal distribution, and morphological measures. Combining these complementary statistics is crucial for breaking parameter degeneracies and unveiling the properties of the early Universe. We specifically assess the potential of the initial SKA-Low configuration (AA*) to measure galaxy and IGM properties, demonstrating its capability for early science results. This chapter forms part of a comprehensive set detailing the Epoch of Reionisation and Cosmic Dawn science case for the SKA-Low telescope.

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

Universal Topological Power Transfer with Arbitrarily Large Chern Number in Driven Quantum Spin Chains

Topological frequency converters exploit a quantized transfer of power between two driving fields in a quantum system, a phenomenon topologically protected by the Chern number of the associated fiber bundle. While realizations with few-spin systems have theoretically demonstrated this effect, the conversion factors have typically been restricted to small integer values. Here, we investigate an interacting $XXZ$ Heisenberg spin-$1/2$ chain driven adiabatically by two magnetic drives with incommensurate frequencies. The Chern number, determined by the degeneracy points enclosed by the adiabatic trajectory, increases systematically with the chain length and can be tuned through the exchange anisotropy, providing direct control of the topological pumping strength. We reveal a universal dependency of the anisotropy and magnetic field strength for odd and even chain lengths of the quantum critical points. This provides a mechanism for a topological frequency converter with an arbitrarily large, quantized conversion ratio in interacting quantum spin chains. The mechanism remains the same for arbitrary coupling regions of the drives.

cond-mat.str-el↗

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↗

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

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

Generalized Josephson effect with arbitrary periodicity in quantum magnets

Easy-plane quantum magnets are strikingly similar to superconductors, allowing for spin supercurrent and an effective superconducting phase stemming from their $U(1)$ rotation symmetry around the $z$-axis. We uncover a generalized fractional Josephson effect with a periodicity that increases linearly with system size in one-dimensional spin-$1/2$ chains at selected anisotropies and phase-fixing boundary fields. The effect combines arbitrary integer periodicities in a single system, exceeding the $4π$ and $8π$ periodicity of superconducting Josephson effects of Majorana zero modes and other exotic quasiparticles. We reveal a universal energy-phase relation and connect the effect to the recently discovered phantom helices.

cond-mat.str-el↗

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

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

Clustering and Mining Accented Speech for Inclusive and Fair Speech Recognition

Modern automatic speech recognition (ASR) systems are typically trained on more than tens of thousands hours of speech data, which is one of the main factors for their great success. However, the distribution of such data is typically biased towards common accents or typical speech patterns. As a result, those systems often poorly perform on atypical accented speech. In this paper, we present accent clustering and mining schemes for fair speech recognition systems which can perform equally well on under-represented accented speech. For accent recognition, we applied three schemes to overcome limited size of supervised accent data: supervised or unsupervised pre-training, distributionally robust optimization (DRO) and unsupervised clustering. Three schemes can significantly improve the accent recognition model especially for unbalanced and small accented speech. Fine-tuning ASR on the mined Indian accent speech using the proposed supervised or unsupervised clustering schemes showed 10.0% and 5.3% relative improvements compared to fine-tuning on the randomly sampled speech, respectively.

cs.SD↗

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↗