arXiv subjects

David Prelogović

Publications and source records attributed to David Prelogović.

13 recordsLinked to original sources

arXiv2025

Mapping reionization bubbles in the JWST era II: inferring the position and characteristic size of individual bubbles

The James Webb Space Telescope (JWST) is discovering an increasing number of galaxies well into the early stages of the Epoch of Reionization (EoR). Many of these galaxies are clustered with strong Lyman alpha (Ly$α$) emission, motivating the presence of surrounding cosmic HII regions that would facilitate Ly$α$ transmission through the intergalactic medium (IGM). Detecting these HII "bubbles" would allow us to connect their growth to the properties of the galaxies inside them. Here we develop a new forward-modeling framework to estimate the local HII region size and location from Ly$α$ spectra of galaxy groups in the early stages of the EoR. Our model takes advantage of the complementary information provided by neighboring sightlines through the IGM. Our forward models sample the main sources of uncertainty, including: (i) the global neutral fraction; (ii) EoR morphology; (iii) emergent Ly$α$ emission; and (iv) NIRSpec instrument noise. Depending on the availability of complementary nebular lines, $\sim$ 0.006 $\unicode{x2013}$ 0.01 galaxies per cMpc$^3$, are required to be $\gtrsim$95\% confident that the HII bubble location and size recovered by our method is accurate to within $\sim$ 1 comoving Mpc. This corresponds roughly to tens of galaxies at $z\sim7\unicode{x2013}8$ in $\sim$2x2 tiled pointing with JWST NIRSpec. Such a sample is achievable with a targeted survey with completeness down to $M_{\rm UV}^{\rm min}\lesssim$ -19 $\unicode{x2013}$ -17, depending on the over-density of the field. We test our method on 3D EoR simulations as well as misspecified equivalent width distributions, in both cases accurately recovering the HII region surrounding targeted galaxy groups.

astro-ph.GA↗

arXiv2024

Percent-level timing of reionization: self-consistent, implicit-likelihood inference from XQR-30+ Ly$α$ forest data

The Lyman alpha (Lya) forest in the spectra of z>5 quasars provides a powerful probe of the late stages of the Epoch of Reionization (EoR). With the recent advent of exquisite datasets such as XQR-30, many models have struggled to reproduce the observed large-scale fluctuations in the Lya opacity. Here we introduce a Bayesian analysis framework that forward-models large-scale lightcones of IGM properties, and accounts for unresolved sub-structure in the Lya opacity by calibrating to higher-resolution hydrodynamic simulations. Our models directly connect physically-intuitive galaxy properties with the corresponding IGM evolution, without having to tune "effective" parameters or calibrate out the mean transmission. The forest data, in combination with UV luminosity functions and the CMB optical depth, are able to constrain global IGM properties at percent level precision in our fiducial model. Unlike many other works, we recover the forest observations without evoking a rapid drop in the ionizing emissivity from z~7 to 5.5, which we attribute to our sub-grid model for recombinations. In this fiducial model, reionization ends at $z=5.44\pm0.02$ and the EoR mid-point is at $z=7.7\pm0.1$. The ionizing escape fraction increases towards faint galaxies, showing a mild redshift evolution at fixed UV magnitude, Muv. Half of the ionizing photons are provided by galaxies fainter than Muv~-12, well below direct detection limits of optical/NIR instruments including JWST. We also show results from an alternative galaxy model that does not allow for a redshift evolution in the ionizing escape fraction. Despite being decisively disfavored by the Bayesian evidence, the posterior of this model is in qualitative agreement with that from our fiducial model. We caution however that our conclusions regarding the early stages of the EoR and which sources reionized the Universe are more model-dependent.

astro-ph.CO↗

arXiv2024

Mapping reionization bubbles in the JWST era I: empirical edge detection with Lyman alpha emission from galaxies

Ionized bubble sizes during reionization trace physical properties of the first galaxies. JWST's ability to spectroscopically confirm and measure Lyman-alpha (Ly$α$) emission in sub-L* galaxies opens the door to mapping ionized bubbles in 3D. However, existing Lya-based bubble measurement strategies rely on constraints from single galaxies, which are limited by the large variability in intrinsic Ly$α$ emission. As a first step, we present two bubble size estimation methods using Lya spectroscopy of ensembles of galaxies, enabling us to map ionized structures and marginalize over Ly$α$ emission variability. We test our methods using Gpc-scale reionization simulations of the intergalactic medium (IGM). To map bubbles in the plane of the sky, we develop an edge detection method based on the asymmetry of Ly$α$ transmission as a function of spatial position. To map bubbles along the line-of-sight, we develop an algorithm using the tight relation between Ly$α$ transmission and the line-of-sight distance from galaxies to the nearest neutral IGM patch. Both methods can robustly recover bubbles with radius $\gtrsim$10 comoving Mpc, sufficient for mapping bubbles even in the early phases of reionization, when the IGM is $\sim70-90\%$ neutral. These methods require $\gtrsim$0.002-0.004 galaxies/cMpc$^3$, a $5σ$ Ly$α$ equivalent width upper limit of $\lesssim$30Å for the faintest targets, and redshift precision $Δz \lesssim 0.015$, feasible with JWST spectroscopy. Shallower observations will provide robust lower limits on bubble sizes. Additional constraints on IGM transmission from Ly$α$ escape fractions and line profiles will further refine these methods, paving the way to our first direct understanding of ionized bubble growth.

astro-ph.GA↗

arXiv2024

The importance of stochasticity in determining galaxy emissivities and UV LFs during cosmic dawn and reionization

The stochastic nature of star formation and photon propagation in high-redshift galaxies can result in sizable galaxy-to-galaxy scatter in their properties. Ignoring this scatter by assuming mean quantities can bias estimates of their emissivity and corresponding observables. We construct a flexible, semi-empirical model, sampling scatter around the following mean relations: (i) the conditional halo mass function (CHMF); (ii) the stellar-to-halo mass relation (SHMR); (iii) galaxy star formation main sequence (SFMS); (iv) fundamental metallicity relation (FMR); (v) conditional intrinsic luminosity; and (vi) photon escape fraction. In our fiducial model, ignoring scatter in these galaxy properties overestimates the duration of the EoR, delaying its completion by up to $Δz$ ~ 2. We quantify the relative importance of each of the above sources of scatter in determining the ionizing, soft-band X-ray and Lyman Werner (LW) emissivities as a function of scale and redshift. We find that scatter around the SFMS is important for all bands, especially at the highest redshifts where the emissivity is dominated by the faintest, most "bursty" galaxies. Ignoring this scatter would underestimate the mean emissivity and its standard deviation computed over 5 cMpc regions by factors of up to $\sim$2-10 at $5< z < 15$. Scatter around the X-ray luminosity to star formation rate relation is important for determining X-ray emissivity, accounting for roughly half of its mean and standard deviation. The importance of scatter in the ionizing escape fraction depends on its functional form, while scatter around the SHMR contributes at the level of ~10-20%. Although scatter does flatten the UV luminosity functions, shifting the bright end by 1-2 magnitudes, the level of scatter in our fiducial model is insufficient to fully explain recent estimates from JWST photometry (consistent with previous studies).

astro-ph.CO↗

arXiv2024

How informative are summaries of the cosmic 21-cm signal?

The cosmic 21-cm signal will bring data-driven advances to studies of the Cosmic Dawn (CD) and Epoch of Reionization (EoR). Radio telescopes such as the SKA will eventually map the HI fluctuations over the first billion years - the majority of our observable Universe. With such large data volumes, it becomes increasingly important to develop "optimal" summary statistics, allowing us to learn as much as possible about the CD and EoR. In this work we compare the constraining power of several 21-cm summary statistics, using the determinant of the Fisher information matrix, $\det F$. Since we do not have an established "fiducial" model for the astrophysics of the first galaxies, we compute the distribution of $\det F$ across the prior volume. Using a large database of cosmic 21-cm lightcones that include realizations of telescope noise, we compare the following summaries: (i) the spherically-averaged power spectrum (1DPS), (ii) the cylindrically-averaged power spectrum (2DPS), (iii) the 2D Wavelet scattering transform (WST), (iv) a recurrent neural network (RNN), (v) an information-maximizing neural network (IMNN), and (vi) the combination of 2DPS and IMNN. Our best performing individual summary is the 2DPS, having relatively high Fisher information throughout parameter space. Although capable of achieving the highest Fisher information for some parameter choices, the IMNN does not generalize well, resulting in a broad distribution. Our best results are achieved with the concatenation of the 2DPS and IMNN. The combination of only these two complimentary summaries reduces the recovered parameter variances on average by factors of $\sim$6.5 - 9.5, compared with using each summary independently. Finally, we point out that that the common assumption of a constant covariance matrix when doing Fisher forecasts using 21-cm summaries can significantly underestimate parameter constraints.

astro-ph.CO↗

arXiv2024

Inferring astrophysical parameters using the 2D cylindrical power spectrum from reionisation

Enlightening our understanding of the first galaxies responsible for driving reionisation requires detecting the 21-cm signal from neutral hydrogen. Interpreting the wealth of information embedded in this signal requires Bayesian inference. Parameter inference from the 21-cm signal is primarily restricted to the spherically averaged power spectrum (1D PS) owing to its relatively straightforward derivation of an analytic likelihood function enabling traditional Monte-Carlo Markov-Chain (MCMC) approaches. However, in recent years, simulation-based inference (SBI) has become feasible which removes the necessity of having an analytic likelihood, enabling more complex summary statistics of the 21-cm signal to be used for Bayesian inference. In this work, we use SBI, specifically marginal neural ratio estimation to learn the likelihood-to-evidence ratio with Swyft, to explore parameter inference using the cylindrically averaged 2D PS. Since the 21-cm signal is anisotropic, the 2D PS should yield more constraining information compared to the 1D PS which isotropically averages the signal. For this, we consider a mock 1000 hr observation of the 21-cm signal using the SKA and compare the performance of the 2D PS relative to the 1D PS. Additionally, we explore two separate foreground mitigation strategies, perfect foreground removal and wedge avoidance. We find the 2D PS outperforms the 1D PS by improving the marginalised uncertainties on individual astrophysical parameters by up to $\sim30-40$ per cent irrespective of the foreground mitigation strategy. Primarily, these improvements stem from how the 2D PS distinguishes between the transverse, $k_{\perp}$, and redshift dependent, $k_{\parallel}$ information which enables greater sensitivity to the complex reionisation morphology.

astro-ph.CO↗

arXiv2024

Exploring the role of the halo mass function for inferring astrophysical parameters during reionisation

The detection of the 21-cm signal at $z\gtrsim6$ will reveal insights into the properties of the first galaxies responsible for driving reionisation. To extract this information, we perform parameter inference which requires embedding 3D simulations of the 21-cm signal within a Bayesian inference pipeline. Presently, when performing inference we must choose which sources of uncertainty to sample and which to hold fixed. Since the astrophysics of galaxies are much more uncertain than those of the underlying halo-mass function (HMF), we usually parameterise and model the former while fixing the latter. However, in doing so we may bias our inference of the properties of these first galaxies. In this work, we explore the consequences of assuming an incorrect choice of HMF and quantify the relative biases in our inferred astrophysical model parameters when considering the wrong HMF. We then relax this assumption by constructing a generalised five parameter model for the HMF and simultaneously recover these parameters along with our underlying astrophysical model. For this analysis, we use 21cmFAST and perform Simulation-Based Inference by applying marginal neural ratio estimation to learn the likelihood-to-evidence ratio using Swyft. Using a mock 1000 hour observation of the 21-cm power spectrum from the forthcoming Square Kilometre Array, conservatively assuming foreground wedge avoidance, we find assuming the incorrect HMF can bias the recovered astrophysical parameters by up to $\sim3-4σ$ even when including independent information from observed luminosity functions. When considering our generalised HMF model, we recover constraints on our astrophysical parameters with a factor of $\sim2-4$ larger marginalised uncertainties. Importantly, these constraints are unbiased, agnostic to the underlying HMF and therefore more conservative.

astro-ph.CO↗

arXiv2024

Deep learning approach for identification of HII regions during reionization in 21-cm observations -- II. foreground contamination

The upcoming Square Kilometre Array Observatory (SKAO) will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as noise and galactic foreground contamination which pose a challenge for accurate detection. In this study, we present the SegU-Net v2 framework, an enhanced version of our convolutional neural network, built to identify neutral and ionized regions in the 21-cm signal contaminated with foreground emission. We trained our neural network on 21-cm image data processed by a foreground removal method based on Principal Component Analysis achieving an average classification accuracy of 71 per cent between redshift $z=7$ to $11$. We tested SegU-Net v2 against various foreground removal methods, including Gaussian Process Regression, Polynomial Fitting, and Foreground-Wedge Removal. Results show comparable performance, highlighting SegU-Net v2's independence on these pre-processing methods. Statistical analysis shows that a perfect classification score with $AUC=95\%$ is possible for $8 (10\, {\rm cMpc})^3$ at $z>9$, for follow-up studies with infrared/optical telescopes to detect these sources.

astro-ph.IM↗

arXiv2023

Are there more galaxies than we see around high-$z$ quasars?

Whether or not $z \gtrsim 6$ quasars lie in the most massive dark-matter halos of the Universe is still a subject of dispute. While most theoretical studies support this scenario, current observations yield discordant results when they probe the halo mass through the detection rate of quasar companion galaxies. Feedback processes from supermassive black holes and dust obscuration have been blamed for this discrepancy, but the impact of these effects is complex and far from being clearly understood. This paper aims to improve the interpretation of current far-infrared observations by taking into account the cosmological volume probed by the Atacama Large Millimeter/submillimeter Array Telescope and to explain the observational discrepancies. We statistically investigate the detection rate of quasar companions in current observations and verify if they match the expected distribution from various theoretical models, once convolved with the ALMA field-of-view, through the use of Monte Carlo simulations. We demonstrate that the telescope geometrical bias is fundamental and can alone explain the scatter in the number of detected satellite galaxies in different observations. We conclude that the resulting companion densities depend on the chosen galaxy distributions. According to our fiducial models, current data favour a density scenario where quasars lie in dark-matter halos of viral mass $M_{\rm vir} \gtrsim 10^{12}~{\rm M_{\odot}}$, in agreement with most theoretical studies. According to our analysis, each quasar has about 2 companion galaxies, with a [CII] luminosity $L_{\rm [CII]} \gtrsim 10^8~{\rm L}_{\odot}$, within a distance of about 1~Mpc from the quasar.

astro-ph.GA↗

arXiv2023

Exploring the likelihood of the 21-cm power spectrum with simulation-based inference

Observations of the cosmic 21-cm power spectrum (PS) are starting to enable precision Bayesian inference of galaxy properties and physical cosmology, during the first billion years of our Universe. Here we investigate the impact of common approximations about the likelihood used in such inferences, including: (i) assuming a Gaussian functional form; (ii) estimating the mean from a single realization; and (iii) estimating the (co)variance at a single point in parameter space. We compare "classical" inference that uses an explicit likelihood with simulation based inference (SBI) that estimates the likelihood from a training set. Our forward-models include: (i) realizations of the cosmic 21-cm signal computed with 21cmFAST by varying UV and X-ray galaxy parameters together with the initial conditions; (ii) realizations of the telescope noise corresponding to a 1000 h integration with SKA1-Low; (iii) the excision of Fourier modes corresponding to a foreground-dominated, horizon "wedge". We find that the 1D PS likelihood is well described by a Gaussian accounting for covariances between wavemodes and redshift bins (higher order correlations are small). However, common approaches of estimating the forward-modeled mean and (co)variance from a random realization or at a single point in parameter space result in biased and over-constrained posteriors. Our best results come from using SBI to fit a non-Gaussian likelihood with a Gaussian mixture neural density estimator. Such SBI can be performed with up to an order of magnitude fewer simulations than classical, explicit likelihood inference. Thus SBI provides accurate posteriors at a comparably low computational cost.

astro-ph.CO↗

arXiv2022

Machine learning astrophysics from 21 cm lightcones: impact of network architectures and signal contamination

Imaging the cosmic 21 cm signal will map out the first billion years of our Universe. The resulting 3D lightcone (LC) will encode the properties of the unseen first galaxies and physical cosmology. Here, we build on previous work using neural networks (NNs) to infer astrophysical parameters directly from 21 cm LC images. We introduce recurrent neural networks (RNNs), capable of efficiently characterizing the evolution along the redshift axis of 21 cm LC images. Using a large database of simulated cosmic 21 cm LCs, we compare the relative performance in parameter estimation of different network architectures. These including two types of RNNs, which differ in their complexity, as well as a more traditional convolutional neural network (CNN). For the ideal case of no instrumental effects, our simplest and easiest to train RNN performs the best, with a mean squared parameter estimation error (MSE) that is lower by a factor of $\ge 2$ compared with the other architectures studied here, and a factor of $\ge 8$ lower than the previously-studied CNN. We also corrupt the cosmic signal by adding noise expected from a 1000 h integration with the Square Kilometre Array, as well as excising a foreground-contaminated 'horizon wedge'. Parameter prediction errors increase when the NNs are trained on these contaminated LC images, though recovery is still good even in the most pessimistic case (with $R^2 \ge 0.5-0.95$). However, we find no notable differences in performance between network architectures on the contaminated images. We argue this is due to the size of our data set, highlighting the need for larger data sets and/or better data augmentation in order to maximize the potential of NNs in 21 cm parameter estimation.

astro-ph.CO↗

arXiv2020

Modulation instability in the nonlinear Schrödinger equation with a synthetic magnetic field: gauge matters

We theoretically investigate the phenomenon of modulation instability for systems obeying nonlinear Schrödinger equation, which are under the influence of an external homogeneous synthetic magnetic field. For an initial condition, the instability is detected numerically by comparing dynamics with and without a small initial perturbation; the perturbations are characterized in a standard fashion by wavevectors in momentum space. We demonstrate that the region of (in)stability in momentum space, as well as time-evolution in real space, for identical initial conditions, depend on the choice of the gauge (i.e., vector potential) used to describe the homogeneous synthetic magnetic field. This superficially appears as if the gauge invariance is broken, but this is not true. When the system is evolved from an identical initial condition in two different gauges, it is equivalent to suddenly turning on the synthetic magnetic field at $t=0$. This gives rise, via Faraday's law, to an initial instantaneous kick of a synthetic electric field to the wavepacket, which can differ for gauges yielding an identical uniform magnetic field at $t>0$.

cond-mat.quant-gas↗

arXiv2018

Magnetically aligned straight depolarisation canals and the rolling Hough transform

Aims. We aim to characterize the properties of the straight depolarization canals detected in the Low Frequency Array (LOFAR) polarimetric observations of a field centered on the extragalactic source 3C 196. We also compare the canal orientations with magnet- ically aligned Hi filaments and the magnetic field probed by polarized dust emission. Methods. We used the rolling Hough transform (RHT) to identify and characterize the orientation of the straight depolarization canals in radio polarimetric data and the filaments in Hi data. Results. The majority of the straight depolarization canals and the Hi filaments are inclined by 10deg with respect to the Galactic plane and are aligned with the plane-of-sky magnetic field orientation probed by the Planck dust polarization data. The other distinct orientation, of 65deg with respect to the Galactic plane, is associated with the orientation of a bar-like structure observed in the 3C 196 field at 350 MHz. Conclusions. An alignment between three distinct tracers of the (local) interstellar medium (ISM) suggests that an ordered magnetic field plays a crucial role in confining different ISM phases. The majority of the straight depolarization canals are a result of a projection of the complicated 3D distribution of the ISM. The RHT analysis is a robust method for identifying and characterizing the straight depolarization canals observed in radio-polarimetric data.

astro-ph.GA↗