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We present constraints on primordial B modes from large angular scale cosmic microwave background polarisation anisotropies measured with the Planck satellite. To remove Galactic polarised foregrounds, we use a Bayesian parametric component separation method, modelling synchrotron radiation as a power law and thermal dust emission as a modified blackbody. This method propagates uncertainties from the foreground cleaning into the noise covariance matrices of the maps. We construct two likelihoods: (i) a semi-analytical cross-spectrum-based likelihood-approximation scheme (momento) and (ii) an exact polarisation-only pixel-based likelihood (pixlike). Since momento is based on cross-spectra it is statistically less powerful than pixlike, but is less sensitive to systematic errors correlated across frequencies. Both likelihoods give a tensor-to-scalar ratio, r, that is consistent with zero from low multipole (2 <= ell < 30) Planck polarisation data. From full-mission maps we obtain r_0.05<0.274, at 95 per cent confidence, at a pivot scale of k = 0.05 Mpc^-1, using pixlike. momento gives a qualitatively similar but weaker 95 per cent confidence limit of r_0.05<0.408.

We present a Bayesian parametric component separation method for polarised microwave sky maps. We solve jointly for the primary cosmic microwave background (CMB) signal and the main Galactic polarised foreground components. For the latter, we consider electron-synchrotron radiation and thermal dust emission, modelled in frequency as a power law and a modified blackbody respectively. We account for inter-pixel correlations in the noise covariance matrices of the input maps and introduce a spatial correlation length in the prior matrices for the spectral indices beta. We apply our method to low-resolution polarised Planck 2018 Low and High Frequency Instrument (LFI/HFI) data, including the SRoll2 re-processing of HFI data. We find evidence for spatial variation of the synchrotron spectral index, and no evidence for depolarisation of dust. Using the HFI SRoll2 maps, and applying wide priors on the spectral indices, we find a mean polarised synchrotron spectral index over the unmasked sky of beta-sync = -2.833 +- 0.620. For polarised dust emission, we obtain beta-dust = 1.429 +- 0.236. Our method returns correlated uncertainties for all components of the sky model. Using our recovered CMB maps and associated uncertainties, we constrain the optical depth to reionization, tau, using a cross-spectrum-based likelihood-approximation scheme (momento) to be tau = 0.0598 +- 0.0059. We confirm our findings using a pixel-based likelihood (pixlike). In both cases, we obtain a result that is consistent with, albeit a fraction of a sigma higher than, that found by subtracting spatially uniform foreground templates. While the latter method is sufficient for current polarisation data from Planck, next-generation space-borne CMB experiments will need more powerful schemes such as the one presented here.

We compute the tree-level bispectrum of dark matter in the presence of massive neutrinos in the mildly non-linear regime in the context of the effective field theory of large-scale structure (EFTofLSS). For neutrinos, whose typical free streaming wavenumber (\\(k_{\\rm fs}\\)) is longer than the non-linear scale (\\(k_{\\mathrm{NL}}\\)), we solve a Boltzmann equation coupled to the effective fluid equation for dark matter. We solve perturbatively the coupled system by expanding in powers of the neutrino density fraction (\\(f_{\\nu}\\)) and the ratio of the wavenumber of interest over the non-linear scale (\\(k/k_{\\mathrm{NL}}\\)) and add suitable counterterms to remove the dependence from short distance physics. For equilateral configurations, we find that the total-matter tree-level bispectrum is approximately \\(16f_{\\nu}\\) times the dark matter one on short scales (\\(k > k_{\\rm fs}\\)). The largest contribution stems from the back-reaction of massive neutrinos on the dark matter growth factor. On large scales (\\(k < k_{\\rm fs}\\)) the contribution of neutrinos to the bispectrum is smaller by up to two orders of magnitude.

We present a novel maximum a posteriori estimator to jointly estimate band-powers and the covariance of the three-dimensional power spectrum (P3D) of Lyman-alpha forest flux fluctuations, called MAPLE. Our Wiener-filter based algorithm reconstructs a window-deconvolved P3D in the presence of complex survey geometries typical for Lyman-alpha surveys that are sparsely sampled transverse to and densely sampled along the line-of-sight. We demonstrate our method on idealized Gaussian random fields with two selection functions: (i) a sparse sampling of 30 background sources per square degree designed to emulate the currently observing the Dark Energy Spectroscopic Instrument (DESI); (ii) a dense sampling of 900 background sources per square degree emulating the upcoming Prime Focus Spectrograph Galaxy Evolution Survey. Our proof-of-principle shows promise, especially since the algorithm can be extended to marginalize jointly over nuisance parameters and contaminants, i.e.offsets introduced by continuum fitting. Our code is implemented in JAX and is publicly available on GitHub.

This paper explores methods for constructing low multipole temperature and polarisation likelihoods from maps of the cosmic microwave background anisotropies that have complex noise properties and partial sky coverage. We use Planck 2018 High Frequency Instrument (HFI) and updated SRoll2 temperature and polarisation maps to test our methods. We present three likelihood approximations based on quadratic cross spectrum estimators: (i) a variant of the simulation-based likelihood (SimBaL) techniques used in the Planck legacy papers to produce a low multipole EE likelihood; (ii) a semi-analytical likelihood approximation (momento) based on the principle of maximum entropy; (iii) a density-estimation `likelihood-free' scheme (DELFI). Approaches (ii) and (iii) can be generalised to produce low multipole joint temperature-polarisation (TTTEEE) likelihoods. We present extensive tests of these methods on simulations with realistic correlated noise. We then analyse the Planck data and confirm the robustness of our method and likelihoods on multiple inter- and intra-frequency detector set combinations of SRoll2 maps. The three likelihood techniques give consistent results and support a low value of the optical depth to reoinization, tau, from the HFI. Our best estimate of tau comes from combining the low multipole SRoll2 momento (TTTEEE) likelihood with the CamSpec high multipole likelihood and is tau = 0.0627+0.0050-0.0058. This is consistent with the SRoll2 team's determination of tau, though slightly higher by 0.5 sigma, mainly because of our joint treatment of temperature and polarisation.

We measure the three-dimensional power spectrum (P3D) of the transmitted flux in the Lyman-a (Ly-a) forest using the complete extended Baryon Oscillation Spectroscopic Survey data release 16 (eBOSS DR16). This sample consists of 205,012 quasar spectra in the redshift range 2 <= z <= 4 at an effective redshift z=2.334. We propose a pair-count spectral estimator in configuration space, weighting each pair by exp(ikr), for wave vector k and pixel pair separation r, effectively measuring the anisotropic power spectrum without the need for fast Fourier transforms. This accounts for the window matrix in a tractable way, avoiding artifacts found in Fourier-transform based power spectrum estimators due to the sparse sampling transverse to the line-of-sight of Ly-a skewers. We extensively test our pipeline on two sets of mocks: (i) idealized Gaussian random fields with a sparse sampling of Ly-a skewers, and (ii) log-normal LyaCoLoRe mocks including realistic noise levels, the eBOSS survey geometry and contaminants. On eBOSS DR16 data, the Kaiser formula with a non-linear correction term obtained from hydrodynamic simulations yields a good fit to the power spectrum data in the range 0.02 <= k <= 0.35 h/Mpc at the 1-2 sigma level with a covariance matrix derived from LyaCoLoRe mocks. We demonstrate a promising new approach for full-shape cosmological analyses of Ly-a forest data from cosmological surveys such as eBOSS, the currently observing Dark Energy Spectroscopic Instrument and future surveys such as the Prime Focus Spectrograph, WEAVE-QSO and 4MOST.

We present precision measurements of the bias parameters of the one-loop power spectrum model of the Lyman-alpha (Lya) forest, derived within the effective field theory of large-scale structure (EFT). We fit our model to the three-dimensional flux power spectrum measured from the ACCEL2 hydrodynamic simulations. The EFT model fits the data with an accuracy of below 2 percent up to a wavenumber of k = 2 h/Mpc. Further, we analytically derive how non-linearities in the three-dimensional clustering of the Lya forest introduce biases in measurements of the Baryon Acoustic Oscillations (BAO) scaling parameters in radial and transverse directions. From our EFT parameter measurements, we obtain a theoretical error budget of -0.2 (-0.3) percent for the radial (transverse) parameters at redshift two. This corresponds to a shift of -0.3 (0.1) percent for the isotropic (anisotropic) distance measurements. We provide an estimate for the shift of the BAO peak for Lya-quasar cross-correlation measurements assuming analytical and simulation-based scaling relations for the non-linear quasar bias parameters resulting in a shift of -0.2 (-0.1) percent for the radial (transverse) dilation parameters, respectively. This analysis emphasizes the robustness of Lya forest BAO measurements to the theory modeling. We provide informative priors and an error budget for measuring the BAO feature -- a key science driver of the currently observing Dark Energy Spectroscopic Instrument (DESI). Our work paves the way for full-shape cosmological analyses of Lya forest data from DESI and upcoming surveys such as the Prime Focus Spectrograph, WEAVE-QSO, and 4MOST.

We have updated and applied a convolutional neural network (CNN) machine learning model to discover and characterize damped Ly\\(\\alpha\\) systems (DLAs) based on Dark Energy Spectroscopic Instrument (DESI) mock spectra. We have optimized the training process and constructed a CNN model that yields a DLA classification accuracy above 99\\(\\%\\) for spectra which have signal-to-noise (S/N) above 5 per pixel. Classification accuracy is the rate of correct classifications. This accuracy remains above 97\\(\\%\\) for lower signal-to-noise (S/N) \\(\\approx1\\) spectra. This CNN model provides estimations for redshift and HI column density with standard deviations of 0.002 and 0.17 dex for spectra with S/N above 3 per pixel. Also, this DLA finder is able to identify overlapping DLAs and sub-DLAs. Further, the impact of different DLA catalogs on the measurement of Baryon Acoustic Oscillation (BAO) is investigated. The cosmological fitting parameter result for BAO has less than \\(0.61\\%\\) difference compared to analysis of the mock results with perfect knowledge of DLAs. This difference is lower than the statistical error for the first year estimated from the mock spectra: above \\(1.7\\%\\). We also compared the performance of CNN and Gaussian Process (GP) model. Our improved CNN model has moderately 14\\(\\%\\) higher purity and 7\\(\\%\\) higher completeness than an older version of GP code, for S/N \\(>\\) 3. Both codes provide good DLA redshift estimates, but the GP produces a better column density estimate by \\(24\\%\\) less standard deviation. A credible DLA catalog for DESI main survey can be provided by combining these two algorithms.