arXiv subjects

Anshuman Acharya

Publications and source records attributed to Anshuman Acharya.

17 recordsLinked to original sources

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

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

POLAR-II: modeling star formation history of galaxies on the 21-cm signal from Epoch of Reionization

Galaxies may suffer some starburst and quenched periods in their history due to e.g. galaxy mergers and feedback. However, semi-numerical simulations of the Epoch of Reionization (EoR) typically do not accurately model the effects of the star formation history (SFH) of galaxies. Keeping the same total ionizing photon budget from galaxies, we investigate how the ionization and heating of the Intergalactic Medium (IGM), as well as the associated 21-cm signal during the EoR, depends on the variations in the modeling of the SFH of galaxies. We adopt the Jiutian-300 N-body dark matter simulation and the semi-analytic model L-Galaxies 2020 to model galaxy formation. Using the galaxy catalog from L-Galaxies 2020 as input, we post-process the Jiutian-300 density field with the one-dimensional radiative transfer code Grizzly to model the reionization process and the 21-cm signal. We find that the ionized regions produced by galaxies with a SFH derived from L-Galaxies 2020 are slightly larger and warmer than the ones obtained with a constant SFR. For a fixed stellar mass, galaxies produce smaller ionized regions with increasing stellar mass weighted stellar age $τ_{\rm age}$. This results in a different topology and timing of the IGM ionization and heating obtained from Grizzly. The SFH of galaxies is highly dependent on $τ_{\rm age}$ and redshift. Different models of the galactic SFH affect the gas heating and ionizing processes during the EoR, and as a consequence also the 21-cm global signal and power spectrum.

astro-ph.GA↗

arXiv2025

Exploring the effect of different cosmologies on the Epoch of Reionization 21-cm signal with POLAR

A detection of the 21-cm signal power spectrum from the Epoch of Reionization is imminent, thanks to consistent advancements from telescopes such as LOFAR, MWA, and HERA, along with the development of SKA. In light of this progress, it is crucial to expand the parameter space of simulations used to infer astrophysical properties from this signal. In this work, we explore the role of cosmological parameters such as the Hubble constant $H_0$ and the matter clustering amplitude $σ_8$, whose values as provided by measurements at different redshifts are in tension. We run $N$-body simulations using GADGET-4, and post-process them with the reionization simulation code POLAR, that uses L-GALAXIES to include galaxy formation and evolution properties and GRIZZLY to execute 1-D radiative transfer of ionizing photons in the intergalactic medium (IGM). We compare our results with the latest JWST observations and explore which astrophysical properties for different cosmologies are necessary to match the observed UV luminosity functions at redshifts $z = 10$ and $9$. Additionally, we explore the impact of these parameters on the observed 21-cm signal power spectrum, focusing on the redshifts within the range of LOFAR 21-cm signal observations ($z \approx 8.5-10$). Despite differences in cosmological and astrophysical parameters, our models cannot be ruled out by the current upper limits. This suggests the need for broader physical parameter spaces for inference modeling to account for all models that agree with observations. However, we also propose stronger constraining power by using a combination of galactic and IGM observables.

astro-ph.CO↗

arXiv2025

Sample Variance Denoising in Cylindrical 21-cm Power Spectra

State-of-the-art simulations of reionisation-era 21-cm signal have limited volumes, generally orders of magnitude smaller than observations. Consequently, the Fourier modes in common between simulation and observation have limited overlap, especially in cylindrical (2D) k-space that is natural for 21-cm interferometry. This makes sample variance (i.e. the deviation of the simulated sample from the population mean due to finite box size) a potential issue when interpreting upcoming 21-cm observations. We introduce \texttt{21cmPSDenoiser}, a score-based diffusion model that can be applied to a single, forward-modelled realisation of the 21-cm 2D power spectrum (PS), predicting the corresponding \textit{population mean} on-the-fly during Bayesian inference. Individual samples of 2D Fourier amplitudes of wave modes relevant to current 21-cm observations can deviate from the mean by over 50\% for 300 cMpc simulations, even when only considering stochasticity due to sampling of Gaussian initial conditions. \texttt{21cmPSDenoiser} reduces this deviation by an order of magnitude, outperforming current state-of-the-art sample variance mitigation techniques like Fixing \& Pairing by a factor of few at almost no additional computational cost ($\sim6$s per PS). Unlike emulators, the denoiser is not tied to a particular model or simulator since its input is a (model-agnostic) realisation of the 2D 21-cm PS. Indeed, we confirm that it generalises to PS produced with a different 21-cm simulator than those on which it was trained. To quantify the improvement in parameter recovery, we simulate a 21-cm PS detection by the Hydrogen Epoch of Reionization Arrays (HERA) and run different inference pipelines corresponding to commonly-used approximations. We find that using \texttt{21cmPSDenoiser} in the inference pipeline outperforms other approaches, yielding an unbiased posterior that is 50\% narrower.

astro-ph.CO↗

arXiv2025

Extracting the Epoch of Reionization Signal with 3D U-Net Neural Networks Using Data-driven Systematic Effect Model

Neutral hydrogen (HI) serves as a crucial probe for the Cosmic Dawn and the Epoch of Reionization (EoR). Actual observations of the 21-cm signal often encounter challenges such as thermal noise and various systematic effects. To overcome these challenges, we simulate SKA-Low-depth images in South Celestial Pole (SCP) field and process them with a deep learning method. We utilized foreground residuals acquired by LOFAR during actual North Celestial Pole (NCP) field observations, thermal and excess variances calculated via Gaussian process regression (GPR), and 21-cm signals generated with 21cmFAST for signal extraction tests. Our approach to overcome these foreground, thermal noise, and excess variance components employs a 3D U-Net neural network architecture for image analysis. When considering thermal noise corresponding to 1752 hours of integration time, U-Net provides reliable 2D power spectrum predictions, and robustness tests ensure that we get realistic EoR signals. Adding foreground residuals, however, causes inconsistencies below the horizon delay-line. Lastly, evaluating both thermal noise and excess variances with observations up to 4380 hours and 13140 hours ensures reliable power spectrum estimations within the EoR window and across nearly all scales, respectively. The incoherence of excess variances in the frequency direction can greatly affect deep learning to extract 21-cm signals.

astro-ph.IM↗

arXiv2025

Inferring IGM parameters from the redshifted 21-cm Power Spectrum using Artificial Neural Networks

The high redshift 21-cm signal promises to be a crucial probe of the state of the intergalactic medium (IGM). Understanding the connection between the observed 21-cm power spectrum and the physical quantities intricately associated with the IGM is crucial to fully understand the evolution of our Universe. In this study, we develop an emulator using artificial neural network (ANN) to predict the 21-cm power spectrum from a given set of IGM properties, namely, the bubble size distribution and the volume averaged ionization fraction. This emulator is implemented within a standard Bayesian framework to constrain the IGM parameters from a given 21-cm power spectrum. We compare the performance of the Bayesian method to an alternate method using ANN to predict the IGM parameters from a given input power spectrum, and find that both methods yield similar levels of accuracy, while the ANN is significantly faster. We also use this ANN method of parameter estimation to predict the IGM parameters from a test set contaminated with noise levels expected from the SKA-LOW instrument after 1000 hours of observation. Finally, we train a separate ANN to predict the source parameters from the IGM parameters directly, at a redshift of $z=9.1$, demonstrating the possibility of a non-analytic inference of the source parameters from the IGM parameters for the first time. We achieve high accuracies, with R2-scores ranging between $0.898-0.978$ for the ANN emulator and between $0.966-0.986$ and $0.817-0.981$ for the predictions of IGM parameters from 21-cm power spectrum and source parameters from IGM parameters, respectively. The predictions of the IGM parameters from the Bayesian method incorporating the ANN emulator leads to tight constraints with error bars around $\pm{0.14}$ on the IGM parameters.

astro-ph.CO↗

arXiv2025

Constraints on the galaxy formation models during epoch of reionization with high redshift observations

We use high resolution N-body dark matter simulations and L-Galaxies semi-analytical galaxy formation models to explore the high-$z$ galaxy properties and estimate the budget of ionizing photons. The parameters within L-Galaxies are obtained using a Markov Chain Monte Carlo (MCMC) method with high-$z$ galaxy observations from JWST and other telescopes. We consider two versions of L-Galaxies with and without dust correction on galaxy UV luminosities. With the best-fit parameters, both L-Galaxies 2015 and L-Galaxies 2020 reproduce well observations of UV luminosity functions, stellar mass functions, star formation rate densities and ionizing photon emission efficiency. With the assumption of escape fraction of $20\%$, all models produce more ionizing photons than the number of Hydrogen atoms in the Universe at $z>6$. The inclusion of dust correction within MCMC results in higher star formation efficiency, which predicts $\sim 50\%$ more ionizing photons, with better consistency between the predicted stellar mass functions and observations.

astro-ph.CO↗

arXiv2024

Revised LOFAR upper limits on the 21-cm signal power spectrum at $\mathbf{z\approx9.1}$ using Machine Learning and Gaussian Process Regression

The use of Gaussian Process Regression (GPR) for foregrounds mitigation in data collected by the LOw-Frequency ARray (LOFAR) to measure the high-redshift 21-cm signal power spectrum has been shown to have issues of signal loss when the 21-cm signal covariance is misestimated. To address this problem, we have recently introduced covariance kernels obtained by using a Machine Learning based Variational Auto-Encoder (VAE) algorithm in combination with simulations of the 21-cm signal. In this work, we apply this framework to 141 hours ($\approx 10$ nights) of LOFAR data at $z \approx 9.1$, and report revised upper limits of the 21-cm signal power spectrum. Overall, we agree with past results reporting a 2-$σ$ upper limit of $Δ^2_{21} < (80)^2~\rm mK^2$ at $k = 0.075~h~\rm Mpc^{-1}$. Further, the VAE-based kernel has a smaller correlation with the systematic excess noise, and the overall GPR-based approach is shown to be a good model for the data. Assuming an accurate bias correction for the excess noise, we report a 2-$σ$ upper limit of $Δ^2_{21} < (25)^2~\rm mK^2$ at $k = 0.075~h~\rm Mpc^{-1}$. However, we still caution to take the more conservative approach to jointly report the upper limits of the excess noise and the 21-cm signal components.

astro-ph.CO↗

arXiv2024

Probing the intergalactic medium during the Epoch of Reionization using 21-cm signal power spectra

The redshifted 21-cm signal from the epoch of reionization (EoR) directly probes the ionization and thermal states of the intergalactic medium during that period. In particular, the distribution of the ionized regions around the radiating sources during EoR introduces scale-dependent features in the spherically-averaged EoR 21-cm signal power spectrum. The goal is to study these scale-dependent features at different stages of reionization using numerical simulations and build a source model-independent framework to probe the properties of the intergalactic medium using EoR 21-cm signal power spectrum measurements. Under the assumption of high spin temperature, we modelled the redshift evolution of the ratio of EoR 21-cm brightness temperature power spectrum and the corresponding density power spectrum using an ansatz consisting of a set of redshift and scale-independent parameters. This set of eight parameters probes the redshift evolution of the average ionization fraction and the quantities related to the morphology of the ionized regions. We have tested this ansatz on different reionization scenarios generated using different simulation algorithms and found that it is able to recover the redshift evolution of the average neutral fraction within an absolute deviation $\lesssim 0.1$. Our framework allows us to interpret 21-cm signal power spectra in terms of parameters related to the state of the IGM. This source model-independent framework can efficiently constrain reionization scenarios using multi-redshift power spectrum measurements with ongoing and future radio telescopes such as LOFAR, MWA, HERA, and SKA. This will add independent information regarding the EoR IGM properties.

astro-ph.CO↗

arXiv2024

Cosmic variance suppression in radiation-hydrodynamic modeling of the reionization-era 21-cm signal

The 21-cm line emitted by neutral hydrogen is the most promising probe of the Epoch of Reionization (EoR). Multiple radio interferometric instruments are on the cusp of detecting its power spectrum. It is therefore essential to deliver robust theoretical predictions, enabling sound inference of the coeval Universe properties. The nature of this signal traditionally required the modelling of $\mathcal{O}(10^{7-8} \, {\rm Mpc}^3)$ volumes to suppress the impact of cosmic variance. However, the recently-proposed Fixed & Paired (F&P) approach uses carefully-crafted simulation pairs to achieve equal results in smaller volumes. In this work, we thoroughly test the applicability of and improvement granted by this technique to different observables of the 21-cm signal from the EoR. We employ radiation-magneto-hydrodynamics simulations to ensure the most realistic physical description of this epoch, greatly improving over previous studies using a semi-numerical approach without accurate galaxy formation physics and radiative transfer. We estimate the statistical improvement granted by the F&P technique on predictions of the skewness, power spectrum, bispectrum and ionized regions size distribution of the 21-cm signal at redshift $7 \leq z \leq 10$ (corresponding to $\geq80\%$ of the gas being neutral). We find that the effective volume of F&P simulations is at least 3.5 times larger than traditional simulations. This directly translates into an equal improvement in the computational cost (in terms of time and memory). Finally, we confirm that a combination of different observables like skewness, power spectrum and bispectrum across different redshifts can be utilised to maximise the improvement.

astro-ph.CO↗

arXiv2024

Spectral fit residuals as an indicator to increase model complexity

Spectral fitting of X-ray data usually involves minimizing statistics like the chi-square and the Cash statistic. Here we discuss their limitations and introduce two measures based on the cumulative sum (CuSum) of model residuals to evaluate whether model complexity could be increased: the percentage of bins exceeding a nominal threshold in a CuSum array (pct$_{CuSum}$), and the excess area under the CuSum compared to the nominal (p$_\textit{area}$). We demonstrate their use with an application to a $\textit{Chandra}$ ACIS spectral fit.

astro-ph.IM↗

arXiv2023

21-cm Signal from the Epoch of Reionization: A Machine Learning upgrade to Foreground Removal with Gaussian Process Regression

In recent years, a Gaussian Process Regression (GPR) based framework has been developed for foreground mitigation from data collected by the LOw-Frequency ARray (LOFAR), to measure the 21-cm signal power spectrum from the Epoch of Reionization (EoR) and Cosmic Dawn. However, it has been noted that through this method there can be a significant amount of signal loss if the EoR signal covariance is misestimated. To obtain better covariance models, we propose to use a kernel trained on the {\tt GRIZZLY} simulations using a Variational Auto-Encoder (VAE) based algorithm. In this work, we explore the abilities of this Machine Learning based kernel (VAE kernel) used with GPR, by testing it on mock signals from a variety of simulations, exploring noise levels corresponding to $\approx$10 nights ($\approx$141 hours) and $\approx$100 nights ($\approx$1410 hours) of observations with LOFAR. Our work suggests the possibility of successful extraction of the 21-cm signal within 2$σ$ uncertainty in most cases using the VAE kernel, with better recovery of both shape and power than with previously used covariance models. We also explore the role of the excess noise component identified in past applications of GPR and additionally analyse the possibility of redshift dependence on the performance of the VAE kernel. The latter allows us to prepare for future LOFAR observations at a range of redshifts, as well as compare with results from other telescopes.

astro-ph.CO↗

arXiv2023

X-ray Activity Variations and Coronal Abundances of the Star-Planet Interaction candidate HD 179949

We carry out detailed spectral and timing analyses of the $Chandra$ X-ray data of HD 179949, a prototypical example of a star with a close-in giant planet with possible star-planet interaction (SPI) effects. We find a low coronal abundance $A({\rm Fe})/A({\rm H}){\approx}0.2$ relative to the solar photospheric baseline of Anders & Grevesse (1989), and significantly lower than the stellar photosphere as well. We further find low abundances of high First Ionization Potential (FIP) elements $A({\rm O})/A({\rm Fe}){\lesssim}1$, $A({\rm Ne})/A({\rm Fe}){\lesssim}0.1$, but with indications of higher abundances of $A({\rm N})/A({\rm Fe}){\gg}1, A({\rm Al})/A({\rm Fe}){\lesssim}10$. We estimate a FIP bias for this star in the range $\approx{-0.3}$ to ${-0.1}$, larger than the ${\lesssim}-$0.5 expected for stars of this type, but similar to stars hosting close-in hot Jupiters. We detect significant intensity variability over time scales ranging from 100 s - 10 ks, and also evidence for spectral variability over time scales of 1-10 ks. We combine the $Chandra$ flux measurements with $Swift$ and XMM-$Newton$ measurements to detect periodicities and determine that the dominant signal is tied to the stellar polar rotational period, consistent with expectations that the corona is rotational-pole dominated. We also find evidence for periodicity at both the planetary orbital frequency and at its beat frequency with the stellar polar rotational period, suggesting the presence of a magnetic connection between the planet and the stellar pole. If these periodicities represent an SPI signal, it is likely driven by a quasi-continuous form of heating (e.g., magnetic field stretching) rather than sporadic, hot, impulsive flare-like reconnections.

astro-ph.SR↗

arXiv2021

How Robust are the Inferred Density and Metallicity of the Circumgalactic Medium?

Quantitative estimates of the basic properties of the circumgalactic medium(CGM), such as its density and metallicity, depend on the spectrum of incident UV background radiation. Models of UV background are known to have large variations, mainly because they are synthesized using poorly constrained parameters, which introduce uncertainty in the inferred properties of the CGM. Here, we quantify this uncertainty using a large set of new UV background models with physically motivated toy models of metal-enriched CGM. We find that, the inferred density and metallicity of low-density ($10^{-5}$ cm$^{-3}$) gas is uncertain by factors of 6.3 and 3.2, whereas high density ($10^{-3}$ cm$^{-3}$) gas by factors of 4 and 1.6, respectively. The variation in the shape of the UV background models is entirely responsible for such a variation in the metallicity while variation in the density arises from both normalization and shape of the UV background. Moreover, we find a harder (softer) UV background infers higher (lower) density and metallicity. We also study warm-hot gas at $T= 10^{5.5}$ K and find that metallicity is robustly estimated but the inferred density is uncertain by a factor of 3 to 5.4 for low to high-density gas. Such large uncertainties in density and metallicity may severely limit the studies of the CGM and demand better observational constraints on the input parameters used in synthesizing UV background.

astro-ph.GA↗

arXiv2021

Properties of loss cone stars in a cosmological galaxy merger remnant

Aims: We investigate the orbital and phase space properties of loss cone stars that interact strongly with a hard, high-redshift binary supermassive black hole (SMBH) system formed in a cosmological scenario. Methods: We use a novel hybrid integration approach that combines the direct N-body code $φ$-GRAPE with ETICS, a collisionless code that employs the self-consistent field method for force calculation. The hybrid approach shows considerable speed-up over direct summation for particle numbers $> 10^6$, while retaining accuracy of direct N-body for a subset of particles. During the SMBH binary evolution we monitor individual stellar interactions with the binary in order to identify stars that noticeably contribute to the SMBH binary hardening. Results: We successfully identify and analyze in detail the properties of stars that extract energy from the binary. We find that the summed energy changes seen in these stars match very well with the overall binary energy change, demonstrating that stellar interactions are the primary drivers of SMBH binary hardening in triaxial, gas-poor systems. The slight triaxiality of our system results in efficient loss cone refilling, avoiding the final parsec problem. We distinguish three different populations of interactions based on their apocenter. We find a clear prevalence of interactions co-rotating with the binary. Nevertheless, retrograde interactions are the most energetic, contributing only slightly less than the prograde population to the overall energy exchange. The most energetic interactions are also likely to result in a change of sign in the angular momentum of the star. We estimate the merger timescale of the binary to be $\approx 20$ $\mathrm{Myr}$, a value larger by a factor of two than the timescale reported in a previous study.

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