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The cross-correlation between 21-cm intensity mapping (IM) experiments and photometric surveys of galaxies (or any other cosmological tracer with a broad radial kernel) is severely degraded by the loss of long-wavelength radial modes due to Galactic foreground contamination. Higher-order correlators are able to restore some of these modes due to the non-linear coupling between them and the local small-scale clustering induced by gravitational collapse. We explore the possibility of recovering information from the bispectrum between a photometric galaxy sample and an IM experiment, in the context of the clustering-redshifts technique. We demonstrate that the bispectrum is able to calibrate the redshift distribution of the photometric sample to the required accuracy of future experiments such as the Rubin Observatory, using future single-dish and interferometric 21-cm observations, in situations where the two-point function is not able to do so due to foreground contamination. We also show how this calibration is affected by the photometric redshift width σ z ,0 and maximum scale k max. We find that it is important to reach scales |$k \gtrsim 0.3\, h\, {\rm Mpc}^{-1}$|⁠ , with the constraints saturating at around |$k\sim 1\, h\, {\rm Mpc}^{-1}$| for next-generation experiments. [ABSTRACT FROM AUTHOR]

21-cm intensity mapping has emerged as a promising technique to map the large-scale structure of the Universe. However, the presence of foregrounds with amplitudes orders of magnitude larger than the cosmological signal constitutes a critical challenge. Here, we test the sparsity-based algorithm generalized morphological component analysis (GMCA) as a blind component separation technique for this class of experiments. We test the GMCA performance against realistic full-sky mock temperature maps that include, besides astrophysical foregrounds, also a fraction of the polarized part of the signal leaked into the unpolarized one, a very troublesome foreground to subtract, usually referred to as polarization leakage. To our knowledge, this is the first time the removal of such component is performed with no prior assumption. We assess the success of the cleaning by comparing the true and recovered power spectra, in the angular and radial directions. In the best scenario looked at, GMCA is able to recover the input angular (radial) power spectrum with an average bias of |${\sim} 5{{\ \rm per\ cent}}$| for ℓ > 25 (⁠|$20\!-\!30 {{\ \rm per\ cent}}$| for |$k_{\parallel } \gtrsim 0.02 \, h^{-1}$|  Mpc), in the presence of polarization leakage. Our results are robust also when up to |$40{{\ \rm per\ cent}}$| of channels are missing, mimicking a radio-frequency interference (RFI) flagging of the data. Having quantified the notable effect of polarization leakage on our results, in perspective we advocate the use of more realistic simulations when testing 21-cm intensity mapping capabilities. [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]

We uncover two mechanisms that can render Kerr black holes unstable in scalar-tensor gravity, both associated with the presence of matter in the vicinity of the black hole and the fact that this introduces an effective mass for the scalar. Our results highlight the importance of understanding the structure of spacetime in realistic, astrophysical black holes in scalar-tensor theories. [ABSTRACT FROM AUTHOR]

Radio frequency interference (RFI) is emitted from various sources, terrestrial or orbital, and creates a nuisance for ground-based 21-cm experiments. In particular, single-dish observations will be highly susceptible to RFI due to their wide primary beam and sensitivity. This work aimed to simulate the contamination effects from the Radio Navigational Satellite System (RNSS) within the 1100–1350 (MHz) frequency band. The simulation can be divided into two parts: (i) satellite positioning, emission power, and the beam response on the telescope, and (ii) calibration of the satellite signals to data to improve the original model. We utilize previously observed single-dish L -band data from the Meer-Karoo Array Telescope (MeerKAT), which requires special calibration to account for regions contaminated by satellite-based RFI. We find that we can recreate the satellite contamination with high accuracy around its peak frequencies provided the satellite is not too close to the telescope's pointing direction. The simulation can predict satellite movements and signals for past and future observations, aiding in RFI avoidance and testing novel cleaning methods. The predicted signal sits below the noise in the target cosmology window in the L band (970–1015 MHz) making it difficult to confirm any out-of-band emission from satellites. However, in our simulations, this contamination still overwhelmed the 21-cm auto-power spectrum. Nevertheless, it is possible to detect the signal in cross-correlations after mild foreground cleaning. Whether such out of band contamination does exist will require further characterization of the satellite signals far away from their peak frequencies. [ABSTRACT FROM AUTHOR]

The success of H  i intensity mapping is largely dependent on how well 21-cm foreground contamination can be controlled. In order to progress our understanding further, we present a range of simulated foreground data from two different ∼3000 deg2 sky regions, with varying effects from polarization leakage. Combining these with cosmological H  i simulations creates a range of intensity mapping test cases that require different foreground treatments. This allows us to conduct the most generalized study to date into 21-cm foregrounds and their cleaning techniques for the post-reionization era. We first provide a pedagogical review of the most commonly used blind foreground removal techniques [principal component analysis (PCA)/singular value decomposition (SVD), fast independent component analysis (FASTICA), and generalized morphological component analysis (GMCA)]. We also trial a non-blind parametric fitting technique and discuss potential hybridization of methods. We highlight the similarities and differences in these techniques finding that the blind methods produce near equivalent results, and we explain the fundamental reasons for this. Our results demonstrate that polarized foreground residuals should be generally subdominant to H  i on small scales (⁠|$k\gtrsim 0.1\, h\, \text{Mpc}^{-1}$|⁠). However, on larger scales, results are more case dependent. In some cases, aggressive cleans severely damp H  i power but still leave dominant foreground residuals. We find a changing polarization fraction has little impact on results within a realistic range (0.5–2 per cent); however, a higher level of Faraday rotation does require more aggressive cleaning. We also demonstrate the gain from cross-correlations with optical galaxy surveys, where extreme levels of residual foregrounds can be circumvented. However, these residuals still contribute to errors and we discuss the optimal balance between overcleaning and undercleaning. [ABSTRACT FROM AUTHOR]

Blind cleaning methods are currently the preferred strategy for handling foreground contamination in single-dish H  i intensity mapping surveys. Despite the increasing sophistication of blind techniques, some signal loss will be inevitable across all scales. Constructing a corrective transfer function using mock signal injection into the contaminated data has been a practice relied on for H  i intensity mapping experiments. However, assessing whether this approach is viable for future intensity mapping surveys, where precision cosmology is the aim, remains unexplored. In this work, using simulations, we validate for the first time the use of a foreground transfer function to reconstruct power spectra of foreground-cleaned low-redshift intensity maps and look to expose any limitations. We reveal that even when aggressive foreground cleaning is required, which causes |${\gt }\, 50~{{\ \rm per\ cent}}$| negative bias on the largest scales, the power spectrum can be reconstructed using a transfer function to within sub-per cent accuracy. We specifically outline the recipe for constructing an unbiased transfer function, highlighting the pitfalls if one deviates from this recipe, and also correctly identify how a transfer function should be applied in an autocorrelation power spectrum. We validate a method that utilizes the transfer function variance for error estimation in foreground-cleaned power spectra. Finally, we demonstrate how incorrect fiducial parameter assumptions (up to |${\pm }100~{{\ \rm per\ cent}}$| bias) in the generation of mocks, used in the construction of the transfer function, do not significantly bias signal reconstruction or parameter inference (inducing |${\lt }\, 5~{{\ \rm per\ cent}}$| bias in recovered values). [ABSTRACT FROM AUTHOR]

We present a detection of correlated clustering between MeerKAT radio intensity maps and galaxies from the WiggleZ Dark Energy Survey. We find a 7.7σ detection of the cross-correlation power spectrum, the amplitude of which is proportional to the product of the |${\rm H}\, {\small I}$| density fraction (⁠|$\Omega _{{\rm H}\, {\small I}}$|⁠), |${\rm H}\, {\small I}$| bias (⁠|$b_{{\rm H}\, {\small I}}$|⁠), and the cross-correlation coefficient (r). We therefore obtain the constraint |$\Omega _{{\rm H}\, {\small I}}b_{{\rm H}\, {\small I}}r\, {=}\, [0.86\, {\pm }\, 0.10\, ({\rm stat})\, {\pm }\, 0.12\, ({\rm sys})]\, {\times }\, 10^{-3}$|⁠ , at an effective scale of |$k_{\rm eff}\ {\sim }\ 0.13\, h\, \text{Mpc}^{-1}$|⁠. The intensity maps were obtained from a pilot survey with the MeerKAT telescope, a 64-dish pathfinder array to the SKA Observatory (SKAO). The data were collected from 10.5 h of observations using MeerKAT 's L -band receivers over six nights covering the 11 h field of WiggleZ, in the frequency range 1015–973 MHz (0.400 |$\, {\lt }\, z\, {\lt }\,$| 0.459 in redshift). This detection is the first practical demonstration of the multidish autocorrelation intensity mapping technique for cosmology. This marks an important milestone in the roadmap for the cosmology science case with the full SKAO. [ABSTRACT FROM AUTHOR]