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In recent years, a Gaussian process regression (GPR)-based framework has been developed for foreground mitigation from data collected by the LOw-Frequency ARray (LOFAR), to measure the 21-cm signal power spectrum from the Epoch of Reionization (EoR) and cosmic dawn. However, it has been noted that through this method there can be a significant amount of signal loss if the EoR signal covariance is misestimated. To obtain better covariance models, we propose to use a kernel trained on the grizzly simulations using a Variational Auto-Encoder (VAE)-based algorithm. In this work, we explore the abilities of this machine learning-based kernel (VAE kernel) used with GPR, by testing it on mock signals from a variety of simulations, exploring noise levels corresponding to ≈10 nights (≈141 h) and ≈100 nights (≈1410 h) of observations with LOFAR. Our work suggests the possibility of successful extraction of the 21-cm signal within 2σ uncertainty in most cases using the VAE kernel, with better recovery of both shape and power than with previously used covariance models. We also explore the role of the excess noise component identified in past applications of GPR and additionally analyse the possibility of redshift dependence on the performance of the VAE kernel. The latter allows us to prepare for future LOFAR observations at a range of redshifts, as well as compare with results from other telescopes. [ABSTRACT FROM AUTHOR]

Direct detection of the Cosmic Dawn and Epoch of Reionization via the redshifted 21-cm line of neutral Hydrogen will have unprecedented implications for studying structure formation in the early Universe. This exciting goal is challenged by the difficulty of extracting the faint 21-cm signal buried beneath bright astrophysical foregrounds and contaminated by numerous systematics. Here, we focus on improving the Gaussian Process Regression (GPR) signal separation method originally developed for LOFAR observations. We address a key limitation of the current approach by incorporating covariance prior models learnt from 21-cm signal simulations using variational autoencoder (VAE) and interpolatory autoencoder (IAE). Extensive tests are conducted to evaluate GPR, VAE–GPR, and IAE–GPR in different scenarios. Our findings reveal that the new method outperforms standard GPR in component separation tasks. Moreover, the improved method demonstrates robustness when applied to signals not represented in the training set. It also presents a certain degree of resilience to data systematics, highlighting its ability to effectively mitigate their impact on the signal recovery process. However, our findings also underscore the importance of accurately characterizing and understanding these systematics to achieve successful detection. Our generative approaches provide good results even with limited training data, offering a valuable advantage when a large training set is not feasible. Comparing the two algorithms, IAE–GPR shows slightly higher fidelity in recovering power spectra compared to VAE–GPR. These advancements highlight the strength of generative approaches and optimize the analysis techniques for future 21-cm signal detection at high redshifts. [ABSTRACT FROM AUTHOR]

Context. Images of spatially resolved astrophysical objects contain a wealth of morphological and dynamical information, and effectively extracting this information is of paramount importance for understanding the physics and evolution of these objects. The algorithms and methods currently employed for this purpose (such as Gaussian model fitting) often use simplified approaches to describe the structure of resolved objects. Aims. Automated (unsupervised) methods for structure decomposition and tracking of structural patterns are needed for this purpose to be able to treat the complexity of structure and large amounts of data involved. Methods. We developed a new wavelet-based image segmentation and evaluation (WISE) method for multiscale decomposition, segmentation, and tracking of structural patterns in astronomical images. Results. The method was tested against simulated images of relativistic jets and applied to data from long-term monitoring of parsecscale radio jets in 3C 273 and 3C 120. Working at its coarsest resolution, WISE reproduces the previous results of a model-fitting evaluation of the structure and kinematics in these jets exceptionally well. Extending the WISE structure analysis to fine scales provides the first robust measurements of two-dimensional velocity fields in these jets and indicates that the velocity fields probably reflect the evolution of Kelvin-Helmholtz instabilities that develop in the flow. [ABSTRACT FROM AUTHOR]

The upcoming Square Kilometre Array Observatory 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 that 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 and 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 |${\rm AUC}=95~{{\ \rm per\ cent}}$| is possible for 8 < z < 10. While the network prediction lacks the ability to correctly identify ionized regions at higher redshift and differentiate well the few remaining neutral regions at lower redshift due to low contrast between 21-cm signal, noise, and foreground residual in images. Moreover, as the photon sources driving reionization are expected to be located inside ionized regions, we show that SegU-Net v2 can be used to correctly identify and measure the volume of isolated bubbles with |$V_{\rm ion}\gt (10\, {\rm cMpc})^3$| at z > 9, for follow-up studies with infrared/optical telescopes to detect these sources. [ABSTRACT FROM AUTHOR]

The light-cone (LC) anisotropy arises due to cosmic evolution of the cosmic dawn (CD) 21-cm signal along the line-of-sight (LoS) axis of the observation volume. The LC effect makes the signal statistically non-ergodic along the LoS axis. The multifrequency angular power spectrum (MAPS) provides an unbiased alternative to the popular three-dimensional (3D) power spectrum as it does not assume statistical ergodicity along every direction in the signal volume. Unlike the 3D power spectrum which mixes the cosmic evolution of the 21-cm signal along the LoS k modes, MAPS keeps the evolution information disentangled. Here, we first study the impact of different underlying physical processes during CD on the behaviour of the 21-cm MAPS using simulations of various different scenarios and models. We also make error predictions in 21-cm MAPS measurements considering only the system noise and cosmic variance for mock observations of Hydrogen Epoch of Reionization Array (HERA), NenuFAR , and SKA-Low. We find that 100 h of HERA observations will be able to measure 21-cm MAPS at ≥3σ for ℓ ≲ 1000 with |$0.1\, {\rm MHz}$| channel-width. The better sensitivity of SKA-Low allows reaching this sensitivity up to ℓ ≲ 3000. Note that due to the difference in the frequency coverage of the various experiments, the CD–epoch of reionization model considered for NenuFAR is different than those used for the HERA and SKA-Low predictions. Considering NenuFAR with the new model, measurements ≥2σ are possible only for ℓ ≲ 600 with |$0.2\, {\rm MHz}$| channel-width and for a 10 times longer observation time of t obs = 1000 h. However, for the range 300 ≲ ℓ ≲ 600 and t obs = 1000 h more than 3σ measurements are still possible for NenuFAR when combining consecutive frequency channels within a 5 MHz band. [ABSTRACT FROM AUTHOR]

3C 84 (NGC 1275) is the bright radio core of the Perseus cluster. Even in the absence of strong relativistic effects, the source has been detected at γ-rays up to TeV energies. Despite its intensive study, the physical processes responsible for the high-energy emission in the source remain unanswered. We present a detailed kinematics study of the source and its connection to γ-ray emission. The subparsec-scale radio structure is dominated by slow-moving features in both the eastern and western lanes of the jet. The jet appears to have accelerated to its maximum speed within less than 125,000 gravitational radii. The fastest reliably detected speed in the jet was ∼0.9c. This leads to a minimum viewing angle to the source of ≳42° and a maximum Doppler factor of ≲1.5. Our analysis suggests the presence of multiple high-energy sites in the source. If γ-rays are associated with kinematic changes in the jet, they are being produced in both eastern and western lanes in the jet. Three γ-ray flares are contemporaneous with epochs where the slowly moving emission region splits into two subregions. We estimate the significance of these events being associated to be ∼2σ–3σ. We tested our results against theoretical predictions for magnetic-reconnection-induced mini-jets and turbulence and find them compatible. [ABSTRACT FROM AUTHOR]

The Dark Ages and Cosmic Dawn are largely unexplored windows on the infant Universe (z ~ 200–10). Observations of the redshifted 21-cm line of neutral hydrogen can provide valuable new insight into fundamental physics and astrophysics during these eras that no other probe can provide, and drives the design of many future ground-based instruments such as the Square Kilometre Array (SKA) and the Hydrogen Epoch of Reionization Array (HERA). We review progress in the field of high-redshift 21-cm Cosmology, in particular focussing on what questions can be addressed by probing the Dark Ages at z > 30. We conclude that only a space- or lunar-based radio telescope, shielded from the Earth's radio-frequency interference (RFI) signals and its ionosphere, enable the 21-cm signal from the Dark Ages to be detected. We suggest a generic mission design concept, CoDEX, that will enable this in the coming decades. [ABSTRACT FROM AUTHOR]

Using the latest upper limits on the 21-cm power spectrum at z ≈ 9.1 from the Low Frequency Array (LOFAR), we explore the regions of parameter space which are inconsistent with the data. We use 21cmmc , a Monte Carlo Markov chain sampler of 21cmfast which directly forward models the three dimensional (3D) cosmic 21-cm signal in a fully Bayesian framework. We use the astrophysical parametrization from 21cmfast , which includes mass-dependent star formation rates and ionizing escape fractions as well as soft-band X-ray luminosities to place limits on the properties of the high- z galaxies. Further, we connect the disfavoured regions of parameter space with existing observational constraints on the Epoch of Reionization such as ultra-violet (UV) luminosity functions, background UV photoionization rate, intergalactic medium (IGM) neutral fraction, and the electron scattering optical depth. We find that all models exceeding the 21-cm signal limits set by LOFAR at z ≈ 9.1 are excluded at ≳2 σ by other probes. Finally, we place limits on the IGM spin temperature from LOFAR, disfavouring at 95 per cent confidence spin temperatures below ∼2.6 K across an IGM neutral fraction range of |$0.15 \lesssim \bar{x}_{\rm H\, \rm {\small I}} \lesssim 0.6$|⁠. Note, these limits are only obtained from 141 h of data in a single redshift bin. With tighter upper limits, across multiple redshift bins expected in the near future from LOFAR, more viable models will be ruled out. Our approach demonstrates the potential of forward modelling tools such as 21cmmc in combining 21-cm observations with other high- z probes to constrain the astrophysics of galaxies. [ABSTRACT FROM AUTHOR]

Context. Very long baseline interferometry (VLBI) observations at 86 GHz (wavelength, λ = 3 mm) reach a resolution of about 50 μas, probing the collimation and acceleration regions of relativistic outflows in active galactic nuclei (AGN). The physical conditions in these regions can be studied by performing 86 GHz VLBI surveys of representative samples of compact extragalactic radio sources. Aims. To extend the statistical studies of compact extragalactic jets, a large global 86 GHz VLBI survey of 162 compact radio sources was conducted in 2010–2011 using the Global Millimeter VLBI Array (GMVA). Methods. The survey observations were made in a snapshot mode, with up to five scans per target spread over a range of hour angles in order to optimize the visibility coverage. The survey data attained a typical baseline sensitivity of 0.1 Jy and a typical image sensitivity of 5 mJy beam−1, providing successful detections and images for all of the survey targets. For 138 objects, the survey provides the first ever VLBI images made at 86 GHz. Gaussian model fitting of the visibility data was applied to represent the structure of the observed sources and to estimate the flux densities and sizes of distinct emitting regions (components) in their jets. These estimates were used for calculating the brightness temperature (Tb) at the jet base (core) and in one or more moving regions (jet components) downstream from the core. These model-fit-based estimates of Tb were compared to the estimates of brightness temperature limits made directly from the visibility data, demonstrating a good agreement between the two methods. Results. The apparent brightness temperature estimates for the jet cores in our sample range from 2.5 × 109 K to 1.3 × 1012 K, with the mean value of 1.8 × 1011 K. The apparent brightness temperature estimates for the inner jet components in our sample range from 7.0 × 107 K to 4.0 × 1011 K. A simple population model with a single intrinsic value of brightness temperature, T0, is applied to reproduce the observed distribution. It yields T0 = (3.77−0.14+0.10) × 1011 K T 0 = (3. 77 − 0.14 + 0.10) × 10 11 K $ T_{\mathrm{0}} = (3.77^{+0.10}_{-0.14}) \times 10^{11} {\rm K} $ for the jet cores, implying that the inverse Compton losses dominate the emission. In the nearest jet components, T0 = (1.42−0.19+0.16) × 1011 K T 0 = (1. 42 − 0.19 + 0.16) × 10 11 K $ T_{\mathrm{0}} = (1.42^{+0.16}_{-0.19}) \times 10^{11} {\rm K} $ is found, which is slightly higher than the equipartition limit of ∼5 × 1010 K expected for these jet regions. For objects with sufficient structural detail detected, the adiabatic energy losses are shown to dominate the observed changes of brightness temperature along the jet. [ABSTRACT FROM AUTHOR]