We derive and numerically implement an algorithm for estimating the 3D power spectrum of the Lyman-α (Lyα) forest flux fluctuations. The algorithm exploits the unique geometry of Lyα forest data to efficiently measure the cross-spectrum between lines of sight as a function of parallel wavenumber, transverse separation and redshift. We start by approximating the global covariance matrix as block-diagonal, where only pixels from the same spectrum are correlated. We then compute the eigenvectors of the derivative of the signal covariance with respect to cross-spectrum parameters, and project the inverse-covariance-weighted spectra onto them. This acts much like a radial Fourier transform over redshift windows. The resulting cross-spectrum inference is then converted into our final product, an approximation of the likelihood for the 3D power spectrum expressed as second order Taylor expansion around a fiducial model. We demonstrate the accuracy and scalability of the algorithm and comment on possible extensions. Our algorithm will allow efficient analysis of the upcoming Dark Energy Spectroscopic Instrument dataset.

Most efforts to detect signatures of dynamical dark energy (DE) are focused on late times, z ≲ 2, where the DE component begins to dominate the cosmic energy density. Many theoretical models involving dynamical DE exhibit a 'freezing' equation of state however, where w → -1 at late times, with a transition to a 'tracking' behaviour at earlier times (with w ≫ -1 at sufficiently high redshift). In this paper, we study whether constraints on background distance indicators from large-scale structure (LSS) surveys in the post-reionization matter-dominated regime, 2 ≲ z ≲ 6, are sensitive to this behaviour, on the basis that the DE component should remain detectable (despite being strongly subdominant) in this redshift range given sufficiently precise observations. Using phenomenological models inspired by parameter space studies of Horndeski (generalized scalar-tensor) theories, we show how existing CMB and LSS measurements constrain the DE equation of state in the matter-dominated era, and examine how forthcoming galaxy surveys and 21 cm intensity mapping instruments can improve constraints in this regime at the background level. We also find that the combination of existing CMB and LSS constraints with DESI will already come close to offering the best possible constraints on H0 using BAO/galaxy power spectrum measurements, and that either a spectroscopic follow-up of the LSST galaxy sample (e.g. MegaMapper or SpecTel) or a Stage 2/PUMA-like intensity mapping survey, both at z ≳ 2, would offer better constraints on the class of models considered here than a comparable cosmic variance-limited galaxy survey at z ≲ 1.5.

The long wavelength modes lost to bright foregrounds in the interferometric 21-cm surveys can partially be recovered using a forward modeling approach that exploits the non-linear coupling between small and large scales induced by gravitational evolution. In this work, we build upon this approach by considering how adding external galaxy distribution data can help to fill in these modes. We consider supplementing the 21-cm data at two different redshifts with a spectroscopic sample (good radial resolution but low number density) loosely modeled on DESI-ELG at $z=1$ and a photometric sample (high number density but poor radial resolution) similar to LSST sample at $z=1$ and $z=4$ respectively. We find that both the galaxy samples are able to reconstruct the largest modes better than only using 21-cm data, with the spectroscopic sample performing significantly better than the photometric sample despite much lower number density. We demonstrate the synergies between surveys by showing that the primordial initial density field is reconstructed better with the combination of surveys than using either of them individually. Methodologically, we also explore the importance of smoothing the density field when using bias models to forward model these tracers for reconstruction.

We study synergies between three promising methods to measure 22 and suffer from interloper problems even for spectroscopic surveys. Intensity mapping of the 21-cm signal can cover large volumes with exquisite fidelity, but is limited both by loss of information to foreground cleaning and by lack of knowledge of the mean signal. Cosmic microwave background (CMB) lensing is theoretically very clean, but ultimately measures just the projected variations in density. We find that cross-correlation between optical and radio can significantly improve the measurement of growth rate. Combining these with the CMB provides a promising avenue to detecting modified gravity at high redshifts, in particular by independently probing the Weyl and Newtonian potentials and by strengthening control of systematics. We find that cross-correlating a Stage ii 21-cm survey with DESI quasars with a reasonable brightness temperature prior could enable measurements of the growth rate fσ8 at sub 3% and sub 8% levels at z = 3, 4, representing a factor of 4 and 8 improvement over constraints obtainable from DESI quasars alone. Similarly, cross-correlating 21-cm data with a futuristic LBG survey to mUV<24.5 over 1000 square degrees will make possible fσ8 measurements at close to 1% at z = 3 and 3% at z = 4, and improve similar constraints at z = 5 by close to a factor of 3 to sub-10% precision. Combining the above with CMB lensing from a Stage 4 CMB survey and LSST data can additionally constrain the gravitational slip γ parameter to similar precision at these redshifts, enabling us to test the predictions of general relativity at large scales.

Upcoming 21-cm intensity surveys will use the hyperfine transition in emission to map out neutral hydrogen in large volumes of the universe. Unfortunately, large spatial scales are completely contaminated with spectrally smooth astrophysical foregrounds which are orders of magnitude brighter than the signal. This contamination also leaks into smaller radial and angular modes to form a foreground wedge, further limiting the usefulness of 21-cm observations for different science cases, especially cross-correlations with tracers that have wide kernels in the radial direction. In this paper, we investigate reconstructing these modes within a forward modeling framework. Starting with an initial density field, a suitable bias parameterization and non-linear dynamics to model the observed 21-cm field, our reconstruction proceeds by {combining} the likelihood of a forward simulation to match the observations (under given modeling error and a data noise model) {with the Gaussian prior on initial conditions and maximizing the obtained posterior}. For redshifts z=2 and 4, we are able to reconstruct 21cm field with cross correlation, rc > 0.8 on all scales for both our optimistic and pessimistic assumptions about foreground contamination and for different levels of thermal noise. The performance deteriorates slightly at z=6. The large-scale line-of-sight modes are reconstructed almost perfectly. We demonstrate how our method also provides a technique for density field reconstruction for baryon acoustic oscillations, outperforming standard methods on all scales. We also describe how our reconstructed field can provide superb clustering redshift estimation at high redshifts, where it is otherwise extremely difficult to obtain dense spectroscopic samples, as well as open up a wealth of cross-correlation opportunities with projected fields (e.g. lensing) which are restricted to modes transverse to the line of sight.

Despite two decades of tremendous experimental and theoretical progress, the riddle of the accelerated expansion of the Universe remains to be solved. On the experimental side, our understanding of the possibilities and limitations of the major dark energy probes has evolved; here we summarize the major probes and their crucial challenges. On the theoretical side, the taxonomy of explanations for the accelerated expansion rate is better understood, providing clear guidance to the relevant observables. We argue that: i) improving statistical precision and systematic control by taking more data, supporting research efforts to address crucial challenges for each probe, using complementary methods, and relying on cross-correlations is well motivated; ii) blinding of analyses is difficult but ever more important; iii) studies of dark energy and modified gravity are related; and iv) it is crucial that R&D for a vibrant dark energy program in the 2030s be started now by supporting studies and technical R&D that will allow embryonic proposals to mature. Understanding dark energy, arguably the biggest unsolved mystery in both fundamental particle physics and cosmology, will remain one of the focal points of cosmology in the forthcoming decade.

Interpreting observations of the Lyman-α forest flux power spectrum requires interpolation between a small number of expensive simulations. We present a Gaussian process emulator modelling the 1D flux power spectrum as a function of the amplitude and slope of the small-scale linear matter power spectrum, and the state of the intergalactic medium at the epoch of interest (2 < z < 4). This parameterisation enables the prediction of the flux power spectrum in extended cosmological models that are not explicitly included in the training set, eliminating the need to construct bespoke emulators for a number of extensions to ΛCDM. Our emulator is appropriate for cosmologies in which the linear matter power spectrum is described to percent level accuracy by just an amplitude and slope across the epoch of interest, and in the regime probed by eBOSS/DESI data. We demonstrate this for massive neutrino cosmologies, where the emulator is able to predict the flux power spectrum in a Σ mν=0.3 eV neutrino cosmology to sub-percent accuracy, without including massive neutrinos in the training simulations. Further parameters would be required to describe models with sharp features in the linear power, such as warm or light axion dark matter. This work will facilitate the combination of upcoming DESI data with observations of the cosmic microwave background, to obtain constraints on neutrino mass and other extensions to ΛCDM cosmology.

Using a suite of hydrodynamical simulations with cold dark matter, baryons, and neutrinos, we present a detailed study of the effect of massive neutrinos on the 1-D and 3-D flux power spectra of the Lyman-α (Lyα) forest. The presence of massive neutrinos in cosmology induces a scale- and time-dependent suppression of structure formation that is strongest on small scales. Measuring this suppression is a key method for inferring neutrino masses from cosmological data, and is one of the main goals of ongoing and future surveys like eBOSS, DES, LSST, Euclid or DESI . The clustering in the Lyα forest traces the quasi-linear power at late times and on small scales. In combination with observations of the cosmic microwave background, the forest therefore provides some of the tightest constraints on the sum of the neutrino masses. However there is a well-known degeneracy between Σmν and the amplitude of perturbations in the linear matter power spectrum. We study the corresponding degeneracy in the 1-D flux power spectrum of the Lyα forest, and for the first time also study this degeneracy in the 3-D flux power spectrum. We show that the non-linear effects of massive neutrinos on the Lyα forest, beyond the effect of linear power amplitude suppression, are negligible, and this degeneracy persists in the Lyα forest observables to a high precision. We discuss the implications of this degeneracy for choosing parametrisations of the Lyα forest for cosmological analysis.

The tightest and most robust cosmological results of the next decade will be achieved by bringing together multiple surveys of the Universe. This endeavor has to happen across multiple layers of the data processing and analysis, e.g., enhancements are expected from combining Euclid, Rubin, and Roman (as well as other surveys) not only at the level of joint processing and catalog combination, but also during the post-catalog parts of the analysis such as the cosmological inference process. While every experiment builds their own analysis and inference framework and creates their own set of simulations, cross-survey work that homogenizes these efforts, exchanges information from numerical simulations, and coordinates details in the modeling of astrophysical and observational systematics of the corresponding datasets is crucial.