arXiv subjects

Sambit K. Giri

Publications and source records attributed to Sambit K. Giri.

At least 19 recordsLinked to original sources

arXiv2026

Baryonification IV: Constraining baryonic feedback with X-ray gas fractions

Baryonic feedback redistributes gas around dark matter halos, suppressing the matter power spectrum at scales now probed by weak lensing surveys. X-ray observations directly trace this hot gas, and are one of the main probes of its distribution and properties. We present a forward-modelling framework, built on the baryonification model, linking the three-dimensional gas density and temperature profiles of groups and clusters to observed X-ray surface brightness and luminosity profiles on one side, and to matter power spectrum suppression on the other. We validate the model against independent three-dimensional density reconstructions from the literature, and examine our temperature and metallicity treatment in the group-scale regime. Applying this framework to the SZ-selected CHEX-MATE and X-ray-selected eFEDs samples, we measure gas fractions across the group-to-cluster mass range while accounting for X-ray selection effects, with the first published gas fractions based on CHEX-MATE data. Combining both samples, we derive a joint constraint on the hot gas fraction retained by groups and clusters as a function of mass and on the baryonic suppression of the matter power spectrum. We find $f_{\rm gas} = 0.029 \pm 0.006$ at $M_{500c} = 3\times 10^{13}M_\odot$, $f_{\rm gas} = 0.078 \pm 0.004$ at $M_{500c} = 3\times 10^{14}M_\odot$, and suppression of 6% at $k=1\,h/\rm Mpc$ and 23% at $k=5\,h/\rm Mpc$. Our findings are consistent with recent kinematic Sunyaev-Zel'dovich results, hinting at strong feedback. We also show that the $L_X$-$M$ relation is degenerate with feedback strength, and that different feedback scenarios produce distinct X-ray profile shapes that map onto the same $L_X$-$M$ point. This work is a first step toward extending the framework to forward-model diffuse X-ray emission at the map level for simulation-based inference in upcoming wide-area X-ray surveys such as eROSITA.

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

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

High-Redshift Signatures from the Cosmic Dawn and the Epoch of Reionization

In this chapter, we provide a comprehensive overview of the astrophysical and cosmological processes that shape the 21-cm signal during Cosmic Dawn and the Epoch of Reionization. We investigate both standard and exotic signatures potentially observable with SKA-Low. Standard signatures are those expected within the $\Lambda$CDM framework, including contributions from the first stars, galaxies, and black holes. Exotic signatures are more speculative indicating new physics, such as primordial black holes, modifications to the dark matter sector, non-standard primordial fluctuations, or strongly emitting radio galaxies. The effects of these different sources or scenarios are evaluated in the context of the expected sensitivity of SKA-Low, considering the AA* and AA4 configurations. The chapter aims to provide an overview of the theoretical landscape of 21-cm signatures and to highlight how the forthcoming SKA-Low observations will improve our understanding of astrophysical processes at early times and may open the door towards new physics beyond the $\Lambda$CDM framework.

astro-ph.CO↗

arXiv2026

Square Kilometer Array Synergies for the Epoch of Reionization and Cosmic Dawn

Synergies with other instruments will be essential in making, verifying, and interpreting a detection of the cosmic 21-cm signal from the Epoch of Reionization (EoR) and Cosmic Dawn (CD) with the Square Kilometer Array (SKA) telescope. Such synergies can (i) provide prior information about galaxies and the intergalactic medium (IGM) during the EoR/CD; (ii) pave the road to a first 21cm detection by mitigating foregrounds and systematics through cross-correlations; and (iii) give complimentary physical insights into the galaxy -- IGM connection. Here we review the current state of synergies and discuss what observations will best compliment SKA-low EoR/CD observations.

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

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

Imaging the 21-cm Signal from the Cosmic Dawn & Epoch of Reionization and the Connection with the Global Signal

The original baseline design for SKA-Low was motivated by the ability to produce tomographic images of the redshifted 21-cm signal, thus allowing the research field to move beyond the simple statistic of the power spectrum. In this chapter we review the imaging capabilities of SKA-Low, the wide variety of methods proposed for quantatively analysing image data, as well as the connection with the global 21-cm signal.

astro-ph.CO↗

arXiv2026

Impact of 21-cm foreground mitigation strategies on reionization power spectrum constraints

The 21-cm signal probes the intergalactic medium during the Epoch of Reionization (EoR) but is overwhelmed by astrophysical foregrounds orders of magnitude stronger than the cosmological signal. We evaluate two mitigation strategies: (i) Foreground Avoidance, restricting analysis to the EoR window in Fourier space, and (ii) Foreground Removal via Gaussian Process Regression, which exploits spectral smoothness to statistically separate contaminants and reclaim modes within the contaminated wedge. Both introduce systematic biases of up to $\approx$1$\sigma$ in astrophysical parameters such as the minimum star-forming halo mass and ionising escape fraction, with avoidance posteriors consistently broader than removal owing to its restricted visibility coverage. The global reionization history is recovered within the 95% credible interval, though the neutral fraction at late reionization epochs shows a persistent bias reflecting the difficulty of its inference from the power spectrum alone. Multi-redshift inference is susceptible to contamination from poorly mitigated bins. Excluding such bins significantly reduces parameter biases, but identifying them requires independent quality metrics. When restricted to identical length scales, both strategies recover similar power spectra, yielding posteriors in similar regions.

astro-ph.CO↗

arXiv2026

Baryonification III: An accurate analytical model for the dispersion measure probability density function of fast radio bursts

We develop an analytical framework to predict the one-point probability distribution function (PDF) of dispersion measures (DMs) for fast radio bursts (FRBs) within the baryonification (BFC) model. BFC provides a computationally efficient alternative to expensive hydrodynamical simulations for modelling baryonic effects on cosmological scales. By applying the halo mass function and halo bias, we convolve contributions from individual halos across a range of masses and redshifts to derive the large-scale structure contribution to the DM PDF. We validate our analytical predictions against consistency-check simulations and compare them with the IllustrisTNG hydrodynamical simulation over the redshift range $ z = 0$ to $z = 5$, demonstrating excellent agreement. We demonstrate that our model produces consistent results when fitting gas profiles and predicting the PDF, and vice versa. We show that the BFC parameters controlling the gas profile, particularly the halo mass scale ($M_\mathrm{c}$), mass-dependent slope ($\mu$), and outer truncation ($\delta$), are the primary drivers of the PDF shape. Additionally, we investigate the validity of the log-normal approximation commonly used for DM distributions, finding that it provides a sufficient description for a few hundred FRBs. Our work provides a self-consistent model that links gas density profiles to integrated DM statistics, enabling future constraints on baryonic feedback processes from FRB observations.

astro-ph.CO↗

arXiv2025

Implicit inference of the reionization history with higher-order statistics of the 21-cm signal

The Epoch of Reionization (EoR), when the first luminous sources ionised the intergalactic medium, represents a new frontier in cosmology. The Square Kilometre Array Observatory (SKAO) will offer unprecedented insights into this era through observations of the redshifted 21-cm signal, enabling constraints on the Universe's reionization history. We investigate the information content of the average neutral hydrogen fraction ($\bar{x}_{\rm HI}$) in several Gaussian (spherical and cylindrical power spectra) and non-Gaussian (Betti numbers and bispectrum) summary statistics of the 21-cm signal. Mock 21-cm observations are generated using the AA* configuration of SKAO's low-frequency telescope, incorporating noise levels for 100 and 1000 hours. We employ a state-of-the-art implicit inference framework to learn posterior distributions of $\bar{x}_{\rm HI}$ in redshift bins centred at $z=8.0,7.2$ and $6.5$, for each statistic and noise scenario, validating the posteriors through calibration tests. Using the figure of merit to assess constraining power, we find that Betti numbers alone are on average more informative than the power spectra, while the bispectrum provides limited constraints. However, combining higher-order statistics with the cylindrical power spectrum improves the mean figure of merit by $\sim$0.25 dex ($\sim33\%$ reduction in $\sigma(\bar{x}_{\rm HI})$). The relative contribution of each statistic varies with the stage of reionization. With SKAO observations approaching, our results show that combining power spectra with higher-order statistics can significantly increase the information retrieved from the EoR, maximising the scientific return of future 21-cm observations.

astro-ph.CO↗

arXiv2025

Exploring the Early Universe with Deep Learning

Hydrogen is the most abundant element in our Universe. The first generation of stars and galaxies produced photons that ionized hydrogen gas, driving a cosmological event known as the Epoch of Reionization (EoR). The upcoming Square Kilometre Array Observatory (SKAO) will map the distribution of neutral hydrogen during this era, aiding in the study of the properties of these first-generation objects. Extracting astrophysical information will be challenging, as SKAO will produce a tremendous amount of data where the hydrogen signal will be contaminated with undesired foreground contamination and instrumental systematics. To address this, we develop the latest deep learning techniques to extract information from the 2D power spectra of the hydrogen signal expected from SKAO. We apply a series of neural network models to these measurements and quantify their ability to predict the history of cosmic hydrogen reionization, which is connected to the increasing number and efficiency of early photon sources. We show that the study of the early Universe benefits from modern deep learning technology. In particular, we demonstrate that dedicated machine learning algorithms can achieve more than a $0.95$ $R^2$ score on average in recovering the reionization history. This enables accurate and precise cosmological and astrophysical inference of structure formation in the early Universe.

astro-ph.CO↗

arXiv2025

Baryonification: An alternative to hydrodynamical simulations for cosmological studies

We present an improved baryonification (BFC) model that modifies dark-matter-only $N$-body simulations to generate particle-level outputs for gas, dark matter, and stars. Unlike previous implementations, our approach first splits each simulation particle into separate dark matter and baryonic components, which are then displaced individually using the BFC technique. By applying the hydrostatic and ideal gas equations, we assign pressure and temperature values to individual gas particles. The model is validated against hydrodynamical simulations from the FLAMINGO and TNG suites (which feature varied feedback prescriptions) showing good agreement at the level of density and pressure profiles across a wide range of halo masses. As a further step, we calibrate the BFC model parameters to gas and stellar mass ratio profiles from the hydrodynamical simulations. Based on these calibrations, we baryonify $N$-body simulations and compare the resulting total matter power spectrum suppressions to the ones from the same hydrodynamical simulation. Carrying out this test of the BFC method at each redshift individually, we obtain a 2 percent agreement up to $k=5\,h$/Mpc across all tested feedback scenarios. We also define a reduced, 2+1 parameter BFC model that simultaneously accounts for feedback variations (2 parameters) and redshift evolution (1 parameter). The 2+1 parameter model agrees with the hydrodynamical simulations to better than 2.5 percent over the scales and redshifts relevant for cosmological surveys. Finally, we present a map-level comparison between a baryonified $N$-body simulation and a full hydrodynamical run from the TNG simulation suite. Visual inspection of dark matter, gas, and stellar density fields, along with the integrated pressure map, shows promising agreement. Further work is needed to quantify the accuracy at the level of observables.

astro-ph.CO↗

arXiv2025

Baryonification II: Constraining feedback with X-ray and kinematic Sunyaev-Zel'dovich observations

Baryonic feedback alters the matter distribution on small and intermediate scales, posing a challenge for precision cosmology. The new, component-wise baryonification (BFC) approach provides a self-consistent framework to model feedback effects for different observables. In this paper we use this framework to fit kinematic Sunyaev-Zel'dovich (kSZ) observations from the Atacama Cosmology Telescope (ACT) alongside halo X-ray gas fractions from eROSITA, investigating baryonic feedback in a cosmological context. We first show that the kSZ data from ACT is consistent with the gas fractions from eROSITA, both suggesting a feedback model that is stronger than what is assumed in most hydrodynamical simulations. This finding is in contrast to older, pre-eROSITA gas fraction measurements that point towards weaker feedback in tension with the kSZ results. We suspect these discrepancies to be due to selection bias in the pre-eROSITA sample, or differences in halo mass estimation between the two data sets. In a further step, we use the BFC model to predict the baryonic suppression of the matter power spectrum. Based on our combined fit to data from ACT and eROSITA, we find a power spectrum suppression that exceeds the percent-level at modes above $k=0.3-0.6 \,h\,\mathrm{Mpc}^{-1}$, growing to 2-8 percent at $k=1\,h\,\mathrm{Mpc}^{-1}$, and to 20-25 percent at $k=5\,h\,\mathrm{Mpc}^{-1}$, consistent with strong-feedback hydrodynamical simulations. Finally, we compare our best-fitting model to the observed gas density and pressure profiles of massive galaxy clusters from the X-COP sample, finding excellent agreement. These results show that BFC provides a self-consistent picture of feedback across mass- and length scales as well as different cosmological observables, thus making it promising for applications to multiwavelength studies to jointly constrain cosmology and baryonic effects.

astro-ph.CO↗

arXiv2025

Mapping neutral islands during end stages of reionization with photometric intergalactic medium tomography

During the epoch of reionization (EoR), the first generation of luminous sources in our Universe emitted ionizing photons that almost completely ionized the gas in the intergalactic medium (IGM). The growth of ionized bubbles and the persistence of neutral islands within the IGM hold vital clues to understanding the morphology and timeline of cosmic reionization. We explore the potential of photometric IGM tomography using deep narrow-band (NB) imaging to observe the Lyman-$\alpha$ forest transmission in background galaxies with the Subaru/Hyper-Suprime Cam (HSC). Based on our simulations, we find that the currently available NB filter is suitable for mapping the IGM at $z\simeq 5.7$, corresponding to the late stages of reionization. Our findings indicate that over $\sim$500 background galaxies are needed to accurately reconstruct the IGM at scales greater than 200 Mpc, achieving more than a 40 per cent correlation with the true distribution. This technique can help detect final remaining neutral islands that span more than 20 Mpc lengths. Using the superpixel method built to identify physical patterns in noisy image data, we find that the neutral island size distribution can be recovered with an accuracy of $\sim$0.3 dex. Furthermore, we demonstrate that these reconstructed maps are correlated with the galaxy distribution and anti-correlated with the cosmological 21-cm signal from neutral hydrogen in the IGM. Lastly, we find that these reconstructed maps are anti-correlated with the patchy optical depth to the cosmic microwave background. As such, multiple measurements can be employed for confirmed detection of neutral islands during the end stages.

astro-ph.GA↗

arXiv2025

Impact of anisotropic photon emission from sources during the epoch of reionisation

The reionisation of the intergalactic medium (IGM) was driven by the first stars, galaxies, and accreting black holes. However, the relative importance of these sources and the efficiency by which ionising photons escape into the IGM remain poorly understood. Most reionisation modelling frameworks assume idealised, isotropic emissions. We investigate this assumption by examining a suite of simulations incorporating directed, anisotropic photon emissions. We find that such anisotropic emissions of ionising photons yield a different reionisation geometry compared to the standard, isotropic, case. During the early stages of reionisation (when less than 30 per cent of the Universe is ionised), simulations with narrow photon leakage channels produce smaller ionised bubbles on average. However, these bubbles grow to similar sizes during the middle stages of reionisation. This anisotropy not only produces a distinctive evolution of the size distribution of the ionised regions, but also imprints a feature onto the spherically averaged power spectra of the 21-cm signal throughout reionisation. We observe a suppression in power by about 10-40 per cent at scales corresponding to wavenumbers $k = 0.1-1 \, h \, \mathrm{Mpc}^{-1}$, corresponding to the range in which current radio interferometers are most likely to measure the power spectrum. The simulation with the narrowest channel of ionisation emission shows the strongest suppression. However, this anisotropic emission process does not introduce any measurable anisotropy in the 21-cm signal.

astro-ph.CO↗

arXiv2025

AstronomyCalc: A python toolkit for teaching Astronomical Calculations and Data Analysis methods

Understanding astrophysical and cosmological processes can be challenging due to their complexity and lack of intuitive analogies. To address this, we present \texttt{AstronomyCalc}, a Python package specifically designed to aid university-level teaching by integrating theoretical physics with practical astronomical data analysis methods. The package enables students to solve key cosmological calculations, such as the Friedmann equations, and explore various models while visualizing how parameter variations affect cosmic dynamics. It includes tools for generating synthetic astronomical data, such as Type Ia supernova measurements, and supports analysis of publicly available datasets, including Pantheon+ and the SPARC galaxy database. Simplified implementations of advanced algorithms, such as Monte Carlo Markov Chains, allow students to engage with data analysis techniques used in contemporary research. Additionally, \texttt{AstronomyCalc} will be consistently updated with more tools and user-friendly Jupyter notebooks, making it a continually evolving educational resource for developing conceptual understanding and practical skills in astrophysics and cosmology.

physics.ed-ph↗

arXiv2024

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↗