arXiv subjects

P. Daniel Meerburg

Publications and source records attributed to P. Daniel Meerburg.

At least 19 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 $σ_8$ 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

Future Parameter Constraints from Weak Lensing CMB and Galaxy Lensing Power- and Bispectra

Upcoming stage 4 surveys, such as the Simons Observatory, LSST, and Euclid, are poised to measure weak gravitational lensing of the Cosmic Microwave Background (CMB) and galaxies with unprecedented precision. While the power spectrum is the standard statistic used to analyze weak lensing data, non-Gaussianity from non-linear structure growth encodes additional cosmological information in higher-order statistics. We forecast the ability of future surveys to constrain cosmological parameters using the weak lensing power spectrum and bispectrum from both CMB and galaxy surveys, including their cross-correlations. We consider an eight-parameter model ($Λ$CDM + $\sum m_ν$ + $w_0$) and assess constraints for stage 4 survey specifications. In the absence of systematics, both the CMB and galaxy lensing bispectra are found to be detectable at high signal-to-noise. We test two priors: a ''strong'' one based on constraints from CMB temperature and $E$-mode polarization anisotropies, and a ''weak'' one with minimal assumptions. With the weak prior, the bispectrum significantly improves parameter constraints by breaking degeneracies. For strong priors, improvements are more limited, especially for the CMB bispectrum. On small scales, where non-linear effects dominate, the bispectrum's constraining power can rival that of the power spectrum. We also find strong synergy between CMB and galaxy lensing; combining both probes leads to tighter constraints, particularly on neutrino mass. It was recently found that the CMB lensing bispectrum is strongly affected by the Born approximation, so we also consider post-Born corrections but find that our main conclusions remain the same. These results highlight the potential of higher-order lensing statistics and motivate further work on neglected effects such as non-Gaussian covariance, instrumental systematics, and baryonic feedback.

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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-$α$ 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.

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arXiv2025

Neural Network Reconstruction of Non-Gaussian Initial Conditions from Dark Matter Halos

We develop a machine learning approach to reconstructing the cosmological initial conditions from late-time dark matter halo number density fields in redshift space, with the goal of improving sensitivity to cosmological parameters, and in particular primordial non-Gaussianity. Using an U-Net architecture, our model achieves a cross-correlation accuracy of 44% for scales out to $k = 0.4 \text{ h}/\text{Mpc}$ between reconstructed and true initial conditions of Quijote 1 Gpc$^3$ simulation boxes with an average halo number density of $\bar{n} = 4\times 10^{-4}$ (h/Mpc)$^{3}$ in the tracer field at $z=0$ . We demonstrate that our reconstruction is likely to be optimal for this setup and that it is highly effective at reducing redshift-space distortions. Using a Fisher analysis, we show that reconstruction improves cosmological parameter constraints derived from the power spectrum and bispectrum. By combining the power spectrum monopole, quadrupole, and bispectrum monopole up to $k_{\rm{max}} = 0.52 \text{ h}/\text{Mpc}$, our joint analysis of pre- and post-reconstructed fields from the Quijote simulation suite finds improved marginalized errors on all cosmological parameters. In particular, reconstruction improves constraints on $f_{\rm{NL}}$ by factors of 1.33, 1.88, and 1.57 for local, equilateral, and orthogonal shapes. Our findings demonstrate the effectiveness of reconstruction in decoupling modes, mitigating redshift-space distortions and maximizing information on cosmology. The results provide important insights into the amount of cosmological information that can be extracted from small scales, and can potentially be used to complement standard analysis of observational data, upon further development.

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arXiv2025

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.

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arXiv2024

Towards detecting Primordial non-Gaussianity in the CMB using Spherical Convolutional Neural Networks

This paper explores a novel application of spherical convolutional neural networks (CNNs) to detect primordial non-Gaussianity in the cosmic microwave background (CMB), a key probe of inflationary dynamics. While effective, traditional estimators encounter computational challenges, especially when considering summary statistics beyond the bispectrum. We propose spherical CNNs as an alternative, directly analysing full-sky CMB maps to overcome limitations in previous machine learning (ML) approaches that relied on data summaries. By training on simulated CMB maps with varying amplitudes of non-Gaussianity, our spherical CNN models show promising alignment with optimal error bounds of traditional methods, albeit at lower-resolution maps. While we explore several different architectures, results from DeepSphere CNNs most closely match the Fisher forecast for Gaussian test sets under noisy and masked conditions. Our study suggests that spherical CNNs could complement existing methods of non-Gaussianity detection in future datasets, provided additional training data and parameter tuning are applied. We discuss the potential for CNN-based techniques to scale with larger data volumes, paving the way for applications to future CMB data sets.

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arXiv2024

Inferring the density, spin-temperature and neutral-fraction fields of HI from its 21-cm brightness temperature field using machine learning

The 21-cm brightness-temperature field of neutral hydrogen during the Epoch of Reionization and Cosmic Dawn is a rich source of cosmological and astrophysical information, primarily due to its significant non-Gaussian features. However, the complex, nonlinear nature of the underlying physical processes makes analytical modelling of this signal challenging. Consequently, studies often resort to semi-numerical simulations. Traditional analysis methods, which rely on a limited set of summary statistics, may not adequately capture the non-Gaussian content of the data, as the most informative statistics are not predetermined. This paper explores the application of machine learning (ML) to surpass the limitations of summary statistics by leveraging the inherent non-Gaussian characteristics of the 21-cm signal. We demonstrate that a well-trained neural network can independently reconstruct the hydrogen density, spin-temperature, and neutral-fraction fields with cross-coherence values exceeding 0.95 for $k$-modes below $0.5$ Mpc h$^{-1}$, based on a representative simulation at a redshift of $z \approx 15$. To achieve this, the neural network utilises the non-Gaussian information in brightness temperature images over many scales. We discuss how these reconstructed fields, which vary in their sensitivity to model parameters, can be employed for parameter inference, offering more direct insights into underlying cosmological and astrophysical processes only using limited summary statistics of the brightness temperature field, such as its power spectrum.

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arXiv2024

Improving constraints on primordial non-Gaussianity using neural network based reconstruction

We study the use of U-Nets in reconstructing the linear dark matter density field and its consequences for constraining cosmological parameters, in particular primordial non-Gaussianity. Our network is able to reconstruct the initial conditions of redshift $z=0$ density fields from N-body simulations with $90\%$ accuracy out to $k \leq 0.4$ h/Mpc, competitive with state-of-the-art reconstruction algorithms at a fraction of the computational cost. We study the information content of the reconstructed $z=0$ density field with a Fisher analysis using the QUIJOTE simulation suite, including non-Gaussian initial conditions. Combining the pre- and post-reconstructed power spectrum and bispectrum data up to $k_{\rm max} = 0.52$ h/Mpc, we find significant improvements on all parameters. Most notably, we find a factor $3.65$ (local), $3.54$ (equilateral) and $2.90$ (orthogonal) improvement on the marginalized errors of $f_{\rm NL}$ as compared to only using the pre-reconstructed data. We show that these improvements can be attributed to a combination of reduced data covariance and parameter degeneracy. The results constitute an important step towards more optimal inference of primordial non-Gaussianity from non-linear scales.

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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↗

arXiv2023

No evidence for missing covariance in the Pantheon+ SuperNova sample distance moduli

Inspired by the discussion in the community on possible hidden systematic errors in late universe cosmological probes and non-trivial physical models developed to reduce the Hubble tension, we investigate the Pantheon and Pantheon+ SNe samples for possible deviations from the original $Λ$CDM analysis. To simultaneously account for possible systematics or deviations from $Λ$CDM, we adopt Gaussian processes to model additional covariance while making no further assumptions on their origin. We explore both stationary and non-stationarity corrections to the covariance. While small changes in the inferred cosmological parameters $H_0$ and $Ω_{m}$ can occur, we find no statistically significant evidence for missing covariance. We find an upper limit for the Gaussian processes amplitude $σ< 0.031$ mag with $95\%$ confidence, which corresponds to $20\%$ of the average statistical error in the Pantheon+ sample. The strongest effect we find on the inferred cosmological parameter posterior can reduce the statistical significance of the Hubble tension between Pantheon+ and Planck estimates from 5.3$σ$ to 4.5$σ$. Therefore, we conclude that the SN cosmological parameter inference is robust against the analysis modifications studied in this work.

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arXiv2023

Local non-Gaussianities from cross-correlations between the CMB and 21-cm

The 21-cm brightness temperature fluctuation from the Dark Ages ($z \simeq 30-100$) will allow us to probe the inflationary epoch on very small scales ($>0.1 \, \mbox{Mpc}^{-1}$), inaccessible to cosmic microwave background experiments. Combined with the possibility to collect information from different redshift slices, the 21-cm bispectrum has the potential to significantly improve constraints on primordial non-Gaussianity. However, recent work has shown secondary effects source off-diagonal terms in the covariance matrix which can significantly affect forecasted constraints, especially in signals that peak in the squeezed configuration, such as the local bispectrum. In this paper we propose the three-point $\langle 21-21-\rm CMB \rangle$ bispectrum cross-correlation as a new independent observational channel sensitive to local primordial non-Gaussianity. We find that, contrary to the 21-cm bispectrum, secondary contributions are subdominant to the primordial signal for values $f_{\rm NL}^{\rm loc} \sim 1$, resulting in negligible effects from off-diagonal terms in the covariance matrix. We forecast that CMB $T$ and $E$ modes cross-correlated with an ideal cosmic variance-limited 21-cm experiment with a $0.1$ MHz frequency and $0.1$ arc-minute angular resolution could reach $f_{\rm NL}^{\rm loc} \sim 6 \times 10^{-3}$. This forecast suggests cross-correlation between CMB and 21-cm experiments could provide a viable alternative to 21-cm auto-spectra in reaching unprecedented constraints on primordial local non-Gaussianities.

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arXiv2023

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↗

arXiv2023

CMB-S4: Forecasting Constraints on $f_\mathrm{NL}$ Through $μ$-distortion Anisotropy

Diffusion damping of the cosmic microwave background (CMB) power spectrum results from imperfect photon-baryon coupling in the pre-recombination plasma. At redshift $5 \times 10^4 < z < 2 \times 10^6$, the plasma acquires an effective chemical potential, and energy injections from acoustic damping in this era create $μ$-type spectral distortions of the CMB. These $μ$ distortions trace the underlying photon density fluctuations, probing the primordial power spectrum in short-wavelength modes $k_\mathrm{S}$ over the range $50 \ \mathrm{Mpc}^{-1} \lesssim k \lesssim 10^4 \ \mathrm{Mpc}^{-1}$. Small-scale power modulated by long-wavelength modes $k_\mathrm{L}$ from squeezed-limit non-Gaussianities introduces cross-correlations between CMB temperature anisotropies and $μ$ distortions. Under single-field inflation models, $μ\times T$ correlations measured from an observer in an inertial frame should vanish up to a factor of $(k_\mathrm{L}/k_\mathrm{S})^2 \ll 1$. Thus, any measurable correlation rules out single-field inflation models. We forecast how well the next-generation ground-based CMB experiment CMB-S4 will be able to constrain primordial squeezed-limit non-Gaussianity, parameterized by $f_\mathrm{NL}$, using measurements of $C_{\ell}^{μT}$ as well as $C_{\ell}^{μE}$ from CMB $E$ modes. Using current experimental specifications and foreground modeling, we expect $σ(f_\mathrm{NL}) \lesssim 1000$. This is roughly four times better than the current limit on $f_\mathrm{NL}$ using $μ\times T$ and $μ\times E$ correlations from Planck and is comparable to what is achievable with LiteBIRD, demonstrating the power of the CMB-S4 experiment. This measurement is at an effective scale of $k \simeq 740 \ \text{Mpc}^{-1}$ and is thus highly complementary to measurements at larger scales from primary CMB and large-scale structure.

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arXiv2022

The reconstructed CMB lensing bispectrum

Weak gravitational lensing by the intervening large-scale structure (LSS) of the Universe is the leading non-linear effect on the anisotropies of the cosmic microwave background (CMB). The integrated line-of-sight mass that causes the distortion -- known as lensing convergence -- can be reconstructed from the lensed temperature and polarization anisotropies via estimators quadratic in the CMB modes, and its power spectrum has been measured from multiple CMB experiments. Sourced by the non-linear evolution of structure, the bispectrum of the lensing convergence provides additional information on late-time cosmological evolution complementary to the power spectrum. However, when trying to estimate the summary statistics of the reconstructed lensing convergence, a number of noise-biases are introduced, as previous studies have shown for the power spectrum. Here, we explore for the first time the noise-biases in measuring the bispectrum of the reconstructed lensing convergence. We compute the leading noise-biases in the flat-sky limit and compare our analytical results against simulations, finding excellent agreement. Our results are critical for future attempts to reconstruct the lensing convergence bispectrum with real CMB data.

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arXiv2022

Inflation: Theory and Observations

Cosmic inflation provides a window to the highest energy densities accessible in nature, far beyond those achievable in any realistic terrestrial experiment. Theoretical insights into the inflationary era and its observational probes may therefore shed unique light on the physical laws underlying our universe. This white paper describes our current theoretical understanding of the inflationary era, with a focus on the statistical properties of primordial fluctuations. In particular, we survey observational targets for three important signatures of inflation: primordial gravitational waves, primordial non-Gaussianity and primordial features. With the requisite advancements in analysis techniques, the tremendous increase in the raw sensitivities of upcoming and planned surveys will translate to leaps in our understanding of the inflationary paradigm and could open new frontiers for cosmology and particle physics. The combination of future theoretical and observational developments therefore offer the potential for a dramatic discovery about the nature of cosmic acceleration in the very early universe and physics on the smallest scales.

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arXiv2022

Snowmass Theory Frontier: Astrophysics and Cosmology

We summarize progress made in theoretical astrophysics and cosmology over the past decade and areas of interest for the coming decade. This Report is prepared as the TF09 "Astrophysics and Cosmology" topical group summary for the Theory Frontier as part of the Snowmass 2021 process.

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arXiv2022

Primordial non-Gaussianity and non-Gaussian Covariance

In the pursuit of primordial non-Gaussianities, we hope to access smaller scales across larger comoving volumes. At low redshift, the search for primordial non-Gaussianities is hindered by gravitational collapse, to which we often associate a scale $k_{\rm NL}$. Beyond these scales, it will be hard to reconstruct the modes sensitive to the primordial distribution. When forecasting future constraints on the amplitude of primordial non-Gaussianity, $f_{\rm NL}$, off-diagonal components are usually neglected in the covariance because these are small compared to the diagonal. We show that the induced non-Gaussian off-diagonal components in the covariance degrade forecast constraints on primordial non-Gaussianity, even when all modes are well within what is usually considered the linear regime. As a testing ground, we examine the effects of these off-diagonal components on the constraining power of the matter bispectrum on $f_{\rm NL}$ as a function of $k_{\rm max}$ and redshift, confirming our results against N-body simulations out to redshift $z=10$. We then consider these effects on the hydrogen bispectrum as observed from a PUMA-like 21-cm intensity mapping survey at redshifts $2<z<6$ and show that not including off-diagonal covariance over-predicts the constraining power on $f_{\rm NL}$ by up to a factor of $5$. For future surveys targeting even higher redshifts, such as Cosmic Dawn and the Dark Ages, which are considered ultimate surveys for primordial non-Gaussianity, we predict that non-Gaussian covariance would severely limit prospects to constrain $f_{\rm NL}$ from the bispectrum.

astro-ph.CO↗

arXiv2022

The Dark Ages' 21-cm Trispectrum

We investigate tomography of 21-cm brightness temperature fluctuations during the Dark Ages as a probe for constraining primordial non-Gaussianity. We expand the 21- cm brightness temperature up to cubic order in perturbation theory and improve previous models of the signal by including the effect of the free electron fraction. Using modified standard perturbation theory methods that include baryonic pressure effects we derive an improved secondary bispectrum and for the first time derive the secondary trispectrum of 21-cm brightness temperature fluctuations. We then forecast the amount of information available from the Dark Ages to constrain primordial non-Gaussianity, including the imprints of massive particle exchange during inflation and we determine how much signal is lost due to secondary non-Gaussianity. We find that although secondary non-Gaussianity swamps the primordial signal, primordial non-Gaussianity can still be extracted with signal-to-noise ratios that surpass current and future CMB experiments by several orders of magnitude, depending on the experimental setup. Furthermore, we conclude that for the bi- and trispectra of massive particle exchange marginalizing over other primordial shapes affects signal-to-noise ratios more severely than secondary shapes. Baryonic pressure effects turn out to have a negligible impact on our forecasts, even at scales close to the Jeans scale. The results of this work reinforce the prospects of 21-cm brightness temperature fluctuations from the Dark Ages as the ultimate probe for primordial non-Gaussianity.

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