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 2 compared with the other architectures studied here, and a factor of 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 R2 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

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

Detecting the 21-cm signal at z ≳ 6 will reveal insights into the properties of the first galaxies responsible for driving reionization. To extract this information, we perform parameter inference with three-dimensional simulations of the 21-cm signal embedded 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 is much more uncertain than that of the underlying halo-mass function (HMF), we typically parametrize and model the former while fixing the latter. However, doing so may bias our inference of the galaxy properties. In this work, we explore the consequences of assuming an incorrect HMF and quantify the relative biases on our inferred astrophysical model parameters when considering the wrong HMF. We then relax this assumption by constructing a generalized five parameter HMF model and simultaneously recover it with our underlying astrophysical model. For this, we use 21CMFAST and perform simulation-based inference using marginal neural ratio estimation to learn the likelihood-to-evidence ratio with SWYFT. Using a mock 1000-h observation of the 21-cm power spectrum from the forthcoming Square Kilometre Array, conservatively assuming foreground wedge avoidance, we find that assuming the incorrect HMF can bias the recovered astrophysical parameters by up to ∼ 3–4σ even when including independent information from observed luminosity functions. Using our generalized HMF model, although we recover our astrophysical parameters with a factor of ∼ 2–4 larger marginalized uncertainties, the constraints are unbiased, agnostic to the underlying HMF and therefore more conservative

Characterizing Beam Errors for Radio Interferometric Observations of Reionization

A limiting systematic effect in 21-cm interferometric experiments is the chromaticity due to the coupling between the sky and the instrument. This coupling is sourced by the instrument primary beam; therefore it is important to know the beam to extremely high precision. Here we demonstrate how known beam uncertainties can be characterized using databases of beam models. In this introductory work, we focus on beam errors arising from physically offset and/or broken antennas within a station. We use the public code OSKAR to generate an "ideal" SKA beam formed from 256 antennas regularly-spaced in a 35-m circle, as well as a large database of "perturbed" beams sampling distributions of broken/offset antennas. We decompose the beam errors ("ideal" minus "perturbed") using Principal Component Analysis (PCA) and Kernel PCA (KPCA). Using 20 components, we find that PCA/KPCA can reduce the residual of the beam in our datasets by 60-90% compared with the assumption of an ideal beam. Using a simulated observation of the cosmic signal plus foregrounds, we find that assuming the ideal beam can result in 1% error in the EoR window and 10% in the wedge of the 2D power spectrum. When PCA/KPCA is used to characterize the beam uncertainties, the error in the power spectrum shrinks to below 0.01% in the EoR window and <1% in the wedge. Our framework can be used to characterize and then marginalize over uncertainties in the beam for robust next-generation 21-cm parameter estimation

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

The Lyman alpha (Lyα) forest in the spectra of z &gt; 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 Lyα opacity. Here we introduce a Bayesian analysis framework that forward-models large-scale lightcones of intergalactic medium (IGM) properties, and accounts for unresolved sub-structure in the Lyα 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 invoking 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 ± 0.02 and the EoR mid-point is at z = 7.7 ± 0.1. 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

Emergence of problem areas in the urban structure of post-socialist Zagreb

The period of economic transition has resulted in complex social, functional and morphological transformations which have left their mark in the urban structure of Zagreb. At the beginning of 2000' fundamental planning acts have been passed - Zagreb spatial plan and the City Master Plan - to serve as concrete strategies and guidelines in developing the city area. However, none of the regulatory rules and acts have been completely successful in managing the city development. Significant changes and problems the city is facing in the post-socialist era serve as a research framework and are discussed in this paper. The main goal is to register and explain crucial causes of these spatial transformations. Based on the research of cartographic sources and conducted fieldwork four representative types of problem areas of the city are recognized. According to their functional and morphological characteristics, they are: converted urban land areas, derelict areas, newly built areas and densified areas. Each of these four types of problem areas is individually analyzed in the context of possible negative consequences on the urban environment.</jats:p

Geodesy, tectonics and geodynamics of Dinnarides

This paper summarises recent aetivities on merging the geodetic, geologic and neotectonic evidence of geodynamics in Croatian part of Dinnarides. The area of the City of Zagreb, which is the boundary zone of Eastern Alps, Dinnarides and Pannonian Basin is incIuded as well. It is shown here that the evidence for fractures of Eastern Adriatic differs from the previous hypotheses. This concIusion is derived from the results of various geodetic measurements: satellite positioning (GPS), astro-geodetic measurements of detlections of the vertical. These results are combined with geologic measurements and results of seismic activity studies in order to give more detailed and more accurate picture of the current situation in the tectonically very active region of Dinnarides. Several GPS-campaigns performed in the City of Zagreb area are examined as well. Due to the proximity of Croatian capitol, special attention has been paid to the effects of possible hazard on construction code

Characterizing beam errors for radio interferometric observations of reionization

ABSTRACT A limiting systematic effect in 21-cm interferometric experiments is the chromaticity due to the coupling between the sky and the instrument. This coupling is sourced by the instrument primary beam; therefore it is important to know the beam to extremely high precision. Here, we demonstrate how known beam uncertainties can be characterized using data bases of beam models. In this introductory work, we focus on beam errors arising from physically offset and/or broken antennas within a station. We use the public code oskar to generate an ‘ideal’ SKA beam formed from 256 antennas regularly spaced in a 35-m circle, as well as a large data base of ‘perturbed’ beams sampling distributions of broken/offset antennas. We decompose the beam errors (‘ideal’ minus ‘perturbed’) using principal component analysis (PCA) and Kernel PCA (KPCA). Using 20 components, we find that PCA/KPCA can reduce the residual of the beam in our data sets by $60\!-\!90{{\ \rm per\ cent}}$ compared with the assumption of an ideal beam. Using a simulated observation of the cosmic signal plus foregrounds, we find that assuming the ideal beam can result in $1{{\ \rm per\ cent}}$ error in the epoch of reionization (EoR) window and $10{{\ \rm per\ cent}}$ in the wedge of the 2D power spectrum. When PCA/KPCA is used to characterize the beam uncertainties, the error in the power spectrum shrinks to below $0.01{{\ \rm per\ cent}}$ in the EoR window and $\le 1{{\ \rm per\ cent}}$ in the wedge. Our framework can be used to characterize and then marginalize over uncertainties in the beam for robust next-generation 21-cm parameter estimation.</jats:p

Final Report on research activities within the project CERGOP2/Environment in Croatia

Researchers from the Faculty of Geodesy, University of Zagreb, organized and performed various research activities within the project CERGOP2/Environment. The participation in both CEGRN campaigns was ensured with two epoch stations: Brusnik and Hvar. Activities related to the working package 10.4: International geodynamic test area Plitvice Lakes included several hydrographic measurements with the combination of GPS positioning and echosounder bathymetry. Repeated measurements with two frequencies yielded new insight about the sediments on the lake bottom. A structural map of the Lakes has been prepared, too. Geodynamic measurements has been performed on several special points. The foundation for future permanent station in the area of the Plitvice Lakes has been prepared

21CMEMU: an emulator of 21CMFAST summary observables

ABSTRACT Recent years have witnessed rapid progress in observations of the epoch of reionization (EoR). These have enabled high-dimensional inference of galaxy and intergalactic medium (IGM) properties during the first billion years of our Universe. However, even using efficient, seminumerical simulations, traditional inference approaches that compute 3D lightcones on-the-fly can take 105 core hours. Here we present 21cmemu: an emulator of several summary observables from the popular 21cmfast simulation code. 21cmemu takes as input nine parameters characterizing EoR galaxies, and outputs the following summary statistics: (i) the IGM mean neutral fraction; (ii) the 21-cm power spectrum; (iii) the mean 21-cm spin temperature; (iv) the sky-averaged (global) 21-cm signal; (vi) the ultraviolet (UV) luminosity functions (LFs); and (vii) the Thomson scattering optical depth to the cosmic microwave background (CMB). All observables are predicted with sub- per cent median accuracy, with a reduction of the computational cost by a factor of over 104. After validating inference results, we showcase a few applications, including: (i) quantifying the relative constraining power of different observational data sets; (ii) seeing how recent claims of a late EoR impact previous inferences; and (iii) forecasting upcoming constraints from the sixth observing season of the Hydrogen Epoch of Reionization Array (HERA) telescope. 21cmemu is publicly available, and is included as an alternative simulator in the public 21cmmc sampler