arXiv subjects

Satadru Bag

Publications and source records attributed to Satadru Bag.

At least 19 recordsLinked to original sources

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.

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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.

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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.

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arXiv2026

$w_0$-probe: A new diagnostic of dark energy based on $Om$

Recent DESI data suggest that dark energy may be evolving and motivate the use of model-independent diagnostics such as $Om(z)$ and probes of the equation of state (EoS) of dark energy, $w(z)$. Traditional reconstructions of $w(z)$ rely on differentiating the expansion history, $h(z)=H(z)/H_0$, which amplifies noise and systematic uncertainties. In this work, we introduce a new diagnostic, the $w_0$-probe, which is constructed from $Om(z)$, and which enables a direct determination of the current EoS from $h(z)$ without any additional differentiation. While retaining the null-test capability of $Om(z)$ for $\Lambda$CDM, the $w_0$-probe also provides a direct estimate of $w_0$ -- the current EoS of dark energy. We demonstrate that this reconstruction of $w_0$ is robust for any smooth underlying $w(z)$. We apply this method to Gaussian-process (GP) reconstructions of $h(z)$ using current SNe Ia+BAO+CMB data. Both $Om(z)$ and the $w_0$-probe exclude $\Lambda$CDM at the $95\%$ confidence level (C.L.), with the latter favouring $w_0\simeq-0.62 \pm 0.03$ at $95\%$ C.L. To mitigate potential over-constraining from GP priors, we additionally analyze $\chi^2$-limited reconstructions with likelihoods exceeding the $95\%$ CPL threshold. The $w_0$-probe obtained from these high-likelihood samples again predominantly excludes $\Lambda$CDM and yields $w_0\in(-0.8,-0.5)$ at $z\to 0$, demonstrating the robustness of our results. The $w_0$-probe therefore provides a simple, model-independent, and robust diagnostic of the current EoS of dark energy.

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arXiv2026

HOLISMOKES XXI: Detecting strongly lensed type Ia supernovae from time series of multi-band LSST-like imaging data -- Part II

Strong gravitationally lensed supernovae (LSNe) are rare but extremely valuable probes of cosmology and astrophysics. Prompt identification within the alert streams of time-domain surveys such as the Rubin Legacy Survey of Space and Time (LSST) is essential for timely follow-up observations. In our previous study, Bag et al. (2026), we introduced a deep-learning framework for detecting LSNe Ia directly from multi-band, multi-epoch image cutouts. The model employs a convolutional LSTM architecture to capture spatiotemporal correlations in time-series imaging data, enabling classification updates as new observations arrive. In this work, we extend that framework by incorporating greater realism into the simulations. In particular, we present a method to construct realistic image time series from single-epoch observations by introducing epoch-to-epoch point spread function variations with corresponding variance-map corrections. The dataset is based on HSC PDR3 observations and includes simulated lensed host-galaxy arcs, SN light-curve variations, and Poisson noise. We also introduce an additional negative class consisting of SN Ia occurring in the foreground lens galaxy, representing a challenging source of false positives. Despite these additional complexities, the model retains strong performance. The receiver operating characteristic improves rapidly during the first few observations, reaching a true-positive rate of $\sim60\%$ at a false-positive rate of $\mathcal{O}(10^{-4})$ by the seventh observation and $\sim80\%$ by the tenth. We also investigate potential confusion with sibling SNe occurring in LRGs and identify the configurations that best mimic lensed systems. These results demonstrate that the image-time-series approach remains robust under more realistic observing conditions, and is well suited for real-time LSN searches in LSST and other time-domain surveys.

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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.

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arXiv2025

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.

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arXiv2025

HOLISMOKES XVII: Detecting strongly lensed SNe Ia from time series of multi-band LSST-like imaging data

Strong gravitationally lensed supernovae (LSNe), though rare, are exceptionally valuable probes for cosmology and astrophysics. Upcoming time-domain surveys like the Vera Rubin Observatory's Legacy Survey of Space and Time (LSST) offer a major opportunity to discover them in large numbers. Early identification is crucial for timely follow-up observations. We develop a deep learning pipeline to detect LSNe using multi-band, multi-epoch image cutouts. Our model is based on a 2D convolutional long short-term memory (ConvLSTM2D) architecture, designed to capture both spatial and temporal correlations in time-series imaging data. Predictions are made after each observation in the time series, with accuracy improving as more data arrive. We train the model on realistic simulations derived from Hyper Suprime-Cam (HSC) data, which closely matches LSST in depth and filters. This work focuses exclusively on Type Ia supernovae (SNe Ia). LSNe Ia are injected onto HSC luminous red galaxies (LRGs) at various phases of evolution to create positive examples. Negative examples include variable sources from HSC Transient Survey (including unclassified transients), and simulated unlensed SNe Ia in LRG and spiral galaxies. Our multi-band model shows rapid classification improvements during the initial few observations and quickly reaches high detection efficiency: at a fixed false-positive rate (FPR) of $0.01\%$, the true-positive rate (TPR) reaches $\gtrsim 60\%$ by the 7th observation and exceeds $\gtrsim 70\%$ by the 9th. Among the negative examples, SNe in LRGs remain the primary source of FPR, as they can resemble their lensed counterparts under certain conditions. The model detects quads more effectively than doubles and performs better on systems with larger image separations. Although trained and tested on HSC-like data, our approach applies to any cadenced imaging survey, particularly LSST.

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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.

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arXiv2024

Accelerating lensed quasar discovery and modeling with physics-informed variational autoencoders

Strongly lensed quasars provide valuable insights into the rate of cosmic expansion, the distribution of dark matter in foreground deflectors, and the characteristics of quasar hosts. However, detecting them in astronomical images is difficult due to the prevalence of non-lensing objects. To address this challenge, we developed a generative deep learning model called VariLens, built upon a physics-informed variational autoencoder. This model seamlessly integrates three essential modules: image reconstruction, object classification, and lens modeling, offering a fast and comprehensive approach to strong lens analysis. VariLens is capable of rapidly determining both (1) the probability that an object is a lens system and (2) key parameters of a singular isothermal ellipsoid (SIE) mass model -- including the Einstein radius ($\theta_\mathrm{E}$), lens center, and ellipticity -- in just milliseconds using a single CPU. A direct comparison of VariLens estimates with traditional lens modeling for 20 known lensed quasars within the Subaru Hyper Suprime-Cam (HSC) footprint shows good agreement, with both results consistent within $2\sigma$ for systems with $\theta_\mathrm{E}<3$ arcsecs. To identify new lensed quasar candidates, we begin with an initial sample of approximately 80 million sources, combining HSC data with multiwavelength information from various surveys. After applying a photometric preselection aimed at locating $z>1.5$ sources, the number of candidates was reduced to 710,966. Subsequently, VariLens highlights 13,831 sources, each showing a high likelihood of being a lens. A visual assessment of these objects results in 42 promising candidates that await spectroscopic confirmation. These results underscore the potential of automated deep learning pipelines to efficiently detect and model strong lenses in large datasets.

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arXiv2024

HOLISMOKES XIII: Strong-lens candidates at all mass scales and their environments from the Hyper-Suprime Cam and deep learning

We performed a systematic search for strong gravitational lenses using Hyper Suprime-Cam (HSC) data, focusing on galaxy-scale lenses combined with an environment analysis resulting in the identification of lensing clusters. To identify these lens candidates, we exploited our neural network (NN) from HOLISMOKES VI. During our visual grading, we also simultaneously inspected larger stamps (80'' x 80'') to identify large, extended arcs and also classify their overall environment. Here, we also re-inspected our previous lens candidates with i-Kron radii larger than 0.8''. Using the 546 visually identified lens candidates, we further defined various criteria to select the candidates in overdensities. In total, we identified 24 grade A and 138 grade B candidates that exhibit either spatially-resolved multiple images or extended, distorted arcs in the new sample. Furthermore, combining our different techniques to determine overdensities, we identified a total of 231/546 lens candidates by at least one of our three identification methods for overdensities. This new sample contains only 49 group- or cluster-scale re-discoveries, while 43 systems had been identified by all three procedures. Furthermore, we performed a statistical analysis by using the NN from HOLISMOKES IX to model these systems, making this the largest uniformly modeled sample to date. We find a tendency towards larger Einstein radii for galaxy-scale systems in overdense environments. These results demonstrate the feasibility of applying NNs to hundreds of million cutouts, while resulting in a sample size that can be visually inspected by humans. These deep learning pipelines, with false-positive rates of ~0.01%, are very powerful tools to identify such rare galaxy-scale strong lensing systems, while also aiding in the discovery of new strong lensing clusters.

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arXiv2024

Model independent approach for calculating galaxy rotation curves for low $S/N$ MaNGA galaxies

Internal kinematics of galaxies, traced through the stellar rotation curve or two dimensional velocity map, carry important information on galactic structure and dark matter. With upcoming surveys, the velocity map may play a key role in the development of kinematic lensing as an astrophysical probe. We improve techniques for extracting velocity information from integral field spectroscopy at low signal-to-noise ($S/N$), without a template, and demonstrate substantial advantages over the standard Penalized PiXel-Fitting method (pPXF) approach. Robust rotation curves can be derived down to $S/N\approx 2$ using our method.

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arXiv2024

Detecting unresolved lensed SNe Ia in LSST using blended light curves

Strongly gravitationally lensed supernovae (LSNe) are promising probes for providing absolute distance measurements using gravitational-lens time delays. Spatially unresolved LSNe offer an opportunity to enhance the sample size for precision cosmology. We predict that there will be approximately three times as many unresolved as resolved LSNe Ia in the Legacy Survey of Space and Time (LSST) by the {\it Rubin} Observatory. In this article, we explore the feasibility of detecting unresolved LSNe Ia from a pool of preclassified SNe Ia light curves using the shape of the blended light curves with deep-learning techniques. We find that $\sim 30\%$ unresolved LSNe Ia can be detected with a simple 1D convolutional neural network (CNN) using well-sampled $rizy$-band light curves (with a false-positive rate of $\sim 3\%$). Even when the light curve is well observed in only a single band among $r$, $i$, and $z$, detection is still possible with false-positive rates ranging from $\sim 4$ to $7\%$ depending on the band. Furthermore, we demonstrate that these unresolved cases can be detected at an early stage using light curves up to $\sim20$ days from the first observation with well-controlled false-positive rates, providing ample opportunity to trigger follow-up observations. Additionally, we demonstrate the feasibility of time-delay estimations using solely LSST-like data of unresolved light curves, particularly for doubles, when excluding systems with low time delays and magnification ratios. However, the abundance of such systems among those unresolved in LSST poses a significant challenge. This approach holds potential utility for upcoming wide-field surveys, and overall results could significantly improve with enhanced cadence and depth in the future surveys.

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arXiv2024

The morphology of the redshifted 21-cm signal from the Cosmic Dawn

The spatial fluctuations in the tomographic maps of the redshifted 21-cm signal from the Cosmic Dawn (CD) crucially depend on the size and distribution of the regions with gas temperatures larger than the radio background temperature. In this article, we study the morphological characteristics of such emission regions and their absorption counterparts using the shape diagnostic tool SURFGEN2. Using simulated CD brightness temperature cubes of the 21-cm signal, we find that the emission regions percolate at stages with the filling factor of the emission regions $FF_{\rm emi}\gtrsim 0.15$. Percolation of the absorption regions occurs for $FF_{\rm abs}\gtrsim 0.05$. The largest emission and absorption regions are topologically complex and highly filamentary for most parts of the CD. The number density of these regions as a function of the volume shows the power-law nature with the power-law indexes $\approx -2$ and $-1.6$ for the emission and absorption regions, respectively. Overall, the planarity, filamentarity and genus increase with the increase of the volume of both emission and absorption regions.

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arXiv2023

Model Independent Reconstruction of Galaxy Stellar Velocity Map

We develop a model independent, robust method for determining galaxy rotation velocities across a 2D array of spaxels from an integral field spectrograph. Simulations demonstrate the method is accurate down to lower spectral signal-to-noise than standard methods: 99\% accurate when median $S/N=4$. We apply it to MaNGA data to construct the galaxy velocity map and galaxy rotation curve. We also develop a highly efficient cubic smoothing approach that is $25\times$ faster computationally and only slightly less accurate. Such model independent methods could be useful in studying dark matter properties without assuming a galaxy model.

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arXiv2023

Interpreting the HI 21-cm cosmology maps through Largest Cluster Statistics -- I: Impact of the synthetic SKA1-Low observations

We analyse the evolution of the largest ionized region using the topological and morphological evolution of the redshifted 21-cm signal coming from the neutral hydrogen distribution during the different stages of reionization. For this analysis, we use the "Largest Cluster Statistics" - LCS. We mainly study the impact of the array synthesized beam on the LCS analysis of the 21-cm signal considering the upcoming low-frequency Square Kilometer Array (SKA1-Low) observations using a realistic simulation for such observation based on the 21cmE2E-pipeline using OSKAR. We find that bias in LCS estimation is introduced in synthetic observations due to the array beam. This in turn shifts the apparent percolation transition point towards the later stages of reionization. The biased estimates of LCS, occurring due to the effect of the lower resolution (lack of longer baselines) and the telescope synthesized beam will lead to a biased interpretation of the reionization history. This is important to note while interpreting any future 21-cm signal images from upcoming or future telescopes like the SKA, HERA, etc. We conclude that one may need denser $uv$-coverage at longer baselines for a better deconvolution of the array synthesized beam from the 21-cm images and a relatively unbiased estimate of LCS from such images.

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arXiv2023

Harnessing Unresolved Lensed Quasars: The Mathematical Foundation of the Fluctuation Curve

Strong gravitational lensed quasars (QSOs) have emerged as powerful and novel cosmic probes as they can deliver crucial cosmological information, such as a measurement of the Hubble constant, independent of other probes. Although the upcoming LSST survey is expected to discover $10^3-10^4$ lensed QSOs, a large fraction will remain unresolved due to seeing. The stochastic nature of the quasar intrinsic flux makes it challenging to identify lensed ones and measure the time delays using unresolved light curve data only. In this regard, Bag et al (2022) introduced a data-driven technique based on the minimization of the fluctuation in the reconstructed image light curves. In this article, we delve deeper into the mathematical foundation of this approach. We show that the lensing signal in the fluctuation curve is dominated by the auto-correlation function (ACF) of the derivative of the joint light curve. This explains why the fluctuation curve enables the detection of the lensed QSOs only using the joint light curve, without making assumptions about QSO flux variability, nor requiring any additional information. We show that the ACF of the derivative of the joint light curve is more reliable than the ACF of the joint light curve itself because intrinsic quasar flux variability shows significant auto-correlation up to a few hundred days (as they follow a red power spectrum). In addition, we show that the minimization of fluctuation approach provides even better precision and recall as compared to the ACF of the derivative of the joint light curve when the data have significant observational noise.

astro-ph.CO↗

arXiv2023

The shape distribution of superclusters in SDSS DR 12

Galaxy superclusters, the largest galaxy structures in the cosmic web, are formed due to the gravitational collapse (although they are not usually gravitationally bound). Their geometrical properties can shed light on the structure formation process on cosmological scales, hence on the fundamental properties of gravity itself. In this work we study the distributions of the shape, topology and morphology of the superclusters extracted from SDSS DR 12 main galaxy sample and defined in two different ways - using fixed and adaptive density threshold in the luminosity-density field. To assess the geometry and topology of each individual supercluster, we employ Minkowski functionals and Shapefinders, precisely calculated by the shape diagnostic tool SURFGEN2. Both supercluster samples produce similar shape distributions. Not surprisingly, most superclusters are spherical in shape with trivial topology. However, large superclusters with volumes $V \gtrsim 10^{4}$ Mpc$^{3}$ are statistically found to be filamentary with non-zero genus values. The results, shape distributions and catalogues have been made publicly available.

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