arXiv subjects

Anchal Saxena

Publications and source records attributed to Anchal Saxena.

7 recordsLinked to original sources

arXiv2026

Tracing the Cosmic Origins: Machine Learning Reconstruction of the Primordial Density Field from EoR Observations

Reconstructing the initial conditions of the Universe from late-time tracers would unlock cosmological information buried by non-linear structure formation and astrophysics. We reconstruct the initial density field at $z\sim300$ from simulated 21-cm and CO(1-0) line-intensity maps at $z\sim8$ generated with LIMFAST. Using a three-dimensional U-Net, we reconstruct the initial conditions and evaluate its impact on cosmological parameter constraints. The two tracers probe complementary environments: 21-cm emission traces neutral, low-density regions of the intergalactic medium, while CO traces overdense, star-forming regions. To emulate realistic observations, we model instrumental effects for SKA1-Low- and COMAP-ERA-like surveys, including finite angular resolution and thermal noise. We assess reconstruction performance through the cross-correlation coefficient between reconstructed and true initial density fields, $|C(k)|$. In the noiseless case, combining both tracers delivers the most accurate recovery across ionisation states, with $|C(k)| \gtrsim$ 0.90 for $k \lesssim$ 0.75 Mpc$^{-1}$. With observational effects, small-scale information is degraded, but combining tracers still achieves $|C(k)| \gtrsim$ 0.70 for $k \lesssim$ 0.3 Mpc$^{-1}$. To quantify information gain, we perform simulation-based inference of cosmological parameters from power-spectrum summaries before and after reconstruction. In both noiseless and noisy settings, reconstruction tightens parameter constraints: uncertainties on $\sigma_8$ and $n_{\rm s}$ improve by $\sim2\times$, with smaller but consistent gains for other parameters. This is further confirmed using Kullback-Leibler divergence diagnostics for an ensemble of observations. These results indicate that joint analysis of future 21-cm and CO surveys, combined with such reconstruction, can partially recover otherwise inaccessible cosmological information.

astro-ph.CO↗

arXiv2026

Optimisation of calibration sources for global 21-cm experiments: the REACH case

The spin-flip 21-cm signal from the Cosmic Dawn and the Epoch of Reionization is an essential probe of the conditions that led to the formation of the first luminous objects in the early Universe. However, its detection remains a major challenge owing to its low strength compared to the bright foregrounds and the requirement of precise calibration of the instrument to prevent systematics that could hinder a detection or lead to false inferences. REACH (Radio Experiment for the Analysis of Cosmic Hydrogen) is a radiometer experiment designed to detect this sky-averaged signal in the frequency range of 50--130~MHz. Using a wide-beam antenna, REACH calibration relies on internal reference sources, covering a broad range of temperatures and reflection coefficients. The choice of type and number of calibrators used significantly influences the quality of the calibration. This work investigates these effects and introduces a novel method for selecting an optimal set of calibration sources. With an optimised set, we aim to reduce calibration time, thereby increasing sky integration time while preserving calibration accuracy. We explore two optimisation strategies: one applied across the full receiver band and another performed on a frequency-by-frequency basis. Finally, we demonstrate that, with a total calibration time comparable to the conventional full-calibrator set, an optimised set with fewer calibrators achieves approximately a $15~\%$ reduction in calibrated temperature noise and improved absolute calibration of the instrument. This has implications for better calibration strategies in similar radiometer experiments.

astro-ph.IM↗

arXiv2026

Circuit Modeling for In Situ 21 cm Radiometer Calibration

Recent experiments in cosmology, particularly those aimed at detecting the faint, redshifted, global 21 cm hydrogen line (depth < ~200 mK, z > 7.5), have imposed stringent new requirements on radiometer calibration. In this work, we present a framework for circuit modeling and parameter inference to strengthen these calibration pipelines. This new approach enables in situ characterization of otherwise immeasurable systematics using physically motivated models. A combination of frequentist and Bayesian techniques are employed in a pipeline that supports iterative modeling, robust parameter estimation, and detailed uncertainty quantification. The framework is applied to the REACH telescope, where the precise correction of variations in the radio signal paths arising from component aging or environmental effects is critical. Circuit models of REACH's calibration sources are developed, with the goal of predicting source temperature corrections that are conventionally obtained from laboratory measurements. By fitting the models to measured data using a convolutional cost function, a strong agreement with RMS residuals no worse than -37 dB is obtained. However, Bayesian inference reveals that the resulting temperature corrections can have uncertainties on the order of 1 to 2 K, caused by reflection coefficient degeneracies, measurement noise, and errors in the models. To combat this, posteriors obtained from laboratory measurements are employed as updated priors, reducing correction uncertainties down to 75 mK. Ultimately, the framework provides a means of dynamically accounting for drift in system non-idealities over time, addressing the increasing precision demands of global 21 cm radio astronomy.

astro-ph.IM↗

arXiv2024

Simulation-Based Inference of the sky-averaged 21-cm signal from CD-EoR with REACH

The redshifted 21-cm signal from the Cosmic Dawn and Epoch of Reionization carries invaluable information about the cosmology and astrophysics of the early Universe. Analyzing data from a sky-averaged 21-cm signal experiment requires navigating through an intricate parameter space addressing various factors such as foregrounds, beam uncertainties, ionospheric distortions, and receiver noise for the search of the 21-cm signal. The traditional likelihood-based sampling methods for modeling these effects could become computationally demanding for such complex models, which makes it infeasible to include physically motivated 21-cm signal models in the analysis. Moreover, the inference is driven by the assumed functional form of the likelihood. We demonstrate how Simulation-Based Inference through Truncated Marginal Neural Ratio Estimation (TMNRE) can naturally handle these issues at a reduced computational cost. We estimate the posterior distribution on our model parameters with TMNRE for simulated mock observations, incorporating beam-weighted foregrounds, physically motivated 21-cm signal, and radiometric noise. We find that maximizing information content by analyzing data from multiple time slices and antennas significantly improves the parameter constraints and enhances the exploration of the cosmological signal. We discuss the application of TMNRE for the current configuration of the REACH experiment and demonstrate its potential for exploring new avenues.

astro-ph.CO↗

arXiv2023

Constraining the X-ray heating and reionization using 21-cm power spectra with Marginal Neural Ratio Estimation

Cosmic Dawn (CD) and Epoch of Reionization (EoR) are epochs of the Universe which host invaluable information about the cosmology and astrophysics of X-ray heating and hydrogen reionization. Radio interferometric observations of the 21-cm line at high redshifts have the potential to revolutionize our understanding of the universe during this time. However, modeling the evolution of these epochs is particularly challenging due to the complex interplay of many physical processes. This makes it difficult to perform the conventional statistical analysis using the likelihood-based Markov-Chain Monte Carlo (MCMC) methods, which scales poorly with the dimensionality of the parameter space. In this paper, we show how the Simulation-Based Inference (SBI) through Marginal Neural Ratio Estimation (MNRE) provides a step towards evading these issues. We use 21cmFAST to model the 21-cm power spectrum during CD-EoR with a six-dimensional parameter space. With the expected thermal noise from the Square Kilometre Array (SKA), we are able to accurately recover the posterior distribution for the parameters of our model at a significantly lower computational cost than the conventional likelihood-based methods. We further show how the same training dataset can be utilized to investigate the sensitivity of the model parameters over different redshifts. Our results support that such efficient and scalable inference techniques enable us to significantly extend the modeling complexity beyond what is currently achievable with conventional MCMC methods.

astro-ph.CO↗

arXiv2022

Sky-averaged 21-cm signal extraction using multiple antennas with an SVD framework: the REACH case

In a sky-averaged 21-cm signal experiment, the uncertainty on the extracted signal depends mainly on the covariance between the foreground and 21-cm signal models. In this paper, we construct these models using the modes of variation obtained from the Singular Value Decomposition of a set of simulated foreground and 21-cm signals. We present a strategy to reduce this overlap between the 21-cm and foreground modes by simultaneously fitting the spectra from multiple different antennas, which can be used in combination with the method of utilizing the time dependence of foregrounds while fitting multiple drift scan spectra. To demonstrate this idea, we consider two different foreground models (i) a simple foreground model, where we assume a constant spectral index over the sky, and (ii) a more realistic foreground model, with a spatial variation of the spectral index. For the simple foreground model, with just a single antenna design, we are able to extract the signal with good accuracy if we simultaneously fit the data from multiple time slices. The 21-cm signal extraction is further improved when we simultaneously fit the data from different antennas as well. This improvement becomes more pronounced while using the more realistic mock observations generated from the detailed foreground model. We find that even if we fit multiple time slices, the recovered signal is biased and inaccurate for a single antenna. However, simultaneously fitting the data from different antennas reduces the bias and the uncertainty by a factor of 2-3 on the extracted 21-cm signal.

astro-ph.CO↗

arXiv2020

Impact of dark matter models on the EoR 21-cm signal bispectrum

The nature of dark matter sets the timeline for the formation of first collapsed halos and thus affects the sources of reionization. Here, we consider two different models of dark matter: cold dark matter (CDM) and thermal warm dark matter (WDM), and study how they impact the epoch of reionization (EoR) and its 21-cm observables. Using a suite of simulations, we find that in the WDM scenarios, the structure formation on small scales gets suppressed resulting in a smaller number of low mass dark matter halos compared to the CDM scenario. Assuming that the efficiency of sources in producing ionizing photons remain the same, this leads to a lower number of total ionizing photons produced at any given cosmic time and thus in a delay in the reionization process. We also find visual differences in the neutral hydrogen (HI) topology and in 21-cm maps in case of the WDM compared to the CDM. However, differences in the 21-cm power spectra, at the same neutral fraction, are found to be small. Thus, we focus on the non-Gaussianity in the EoR 21-cm signal, quantified through its bispectrum. We find that the 21-cm bispectra (driven by the HI topology) are significantly different in WDM models when compared with CDM, even for same mass averaged neutral fractions. This establishes that the 21-cm bispectrum is a unique and promising way to differentiate between different dark matter models, and can be used to constrain the nature of the dark matter in the future EoR observations.

astro-ph.CO↗