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Roger de Belsunce

Publications and source records attributed to Roger de Belsunce.

At least 19 recordsLinked to original sources

$w_0w_a$ or $bc$: DESI constraints on relative baryon-CDM perturbations

We carry out a search for the relative baryon-cold dark matter (CDM) density and velocity modes in the DESI full-shape (FS) data, and investigate whether DESI's preference for evolving dark energy can be affected by these relative perturbations, which are a known contaminant of the baryon acoustic oscillations. For the first time, we use a holistic effective field theory (EFT) treatment of the power spectrum and bispectrum both for the standard terms (without relative perturbations), modeled in EFT to one-loop order, and for the relative perturbations. For the latter we implement an efficient computation of the one-loop power spectrum that allows us to scan over different cosmologies. We obtain robust constraints on the relative density and velocity bias parameters $b_{δ_{bc}}$, and $b_{v^2_{bc}}$ for all the DESI DR1 tracers, finding e.g. $b_{δ_{bc}}=5 \pm 6$, $b_{v^2_{bc}}=-0.031\pm 0.023$ (at 68% CL) for the LRG2 sample in a DESI FS-only analysis within $Λ$CDM, supplemented by CMB priors on the baryon density and spectral tilt. Assuming instead a $w_0w_a$CDM cosmological model, and combining DESI FS with the CMB primary anisotropies, CMB lensing, and Pantheon+ supernovae, our LRG2 constraints read $b_{δ_{bc}}=2 \pm 5$, $b_{v^2_{bc}}=-0.032\pm 0.022$. Adding the relative velocity divergence biases we find $b_{δ_{bc}}=-17_{-17}^{+19}$, $b_{θ_{bc}}=-760_{-680}^{+700}$, $b_{v^2_{bc}}=-0.032\pm 0.024$ for the same sample. The constraints on the dark energy equation of state parameters are virtually unchanged when marginalizing over the baryon-CDM modes. We conclude that DESI's preference for evolving dark energy is robust with respect to the baryon-CDM relative perturbations.

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Reanalyzing DESI DR1: 4. Percent-Level Cosmological Constraints from Combined Probes and Robust Evidence for the Normal Neutrino Mass Hierarchy

We present cosmological parameter measurements from the full combination of DESI DR1 galaxy clustering data, described with large-scale structure effective field theory. By incorporating photometric galaxies and CMB lensing cross-correlations, and extending the bispectrum likelihood to smaller scales with a consistent one-loop computation, we achieve substantial gains in constraining power. Combined with the latest DESI baryon acoustic oscillation (BAO) data and cosmic microwave background (CMB) priors on the spectral tilt and baryon density, we find, in $Λ$CDM, $H_0=69.08\pm 0.37~\mathrm{km}\,\mathrm{s}^{-1}\mathrm{Mpc}^{-1}$, $Ω_m=0.2974\pm 0.0050$, and $σ_8 = 0.838\pm 0.017$ ($S_8 = σ_8\sqrt{Ω_m/0.3} =0.834\pm 0.018$). Adding the Pantheon+ supernovae (SNe), we find a $2.2σ$ preference for the $w_0w_a$ dynamical dark energy model from low-redshift data alone, rising to $2.7σ$ when exchanging the SNe for \textit{Planck} CMB data. Combining the full-shape, BAO, CMB, and SNe likelihoods improves the dark energy figure-of-merit by $15\%$ and bounds the neutrino mass sum to $M_ν<0.049$ eV ($Λ$CDM) and $M_ν<0.077$ eV ($w_0w_a$CDM) at 95\% CL. This is the strongest $w_0w_a$CDM bound to date, $37\%$ tighter than from the background expansion data alone. The preference for the normal neutrino mass ordering thus holds regardless of the background model: the inverted hierarchy is disfavored at ${\approx}\,3.5σ$ in $Λ$CDM and ${\approx}\,2.4σ$ in $w_0w_a$CDM, with the latter constraint free of the geometric tension between CMB and BAO that is known to sharpen the $Λ$CDM bound.

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Cosmological Concordance in an Especially Opaque Universe: A Tentative Cosmological Detection of Physical Neutrino Mass in $Λ$CDM

The measurement of the sum of neutrino masses is among the primary promises of precision cosmology, achievable by combining complementary early- and late-Universe probes. However, these datasets currently exhibit mild-to-strong disagreements within $Λ$CDM and its simplest extensions, giving rise to multiple tensions, including the Hubble tension, the preference for "negative" neutrino mass, and indications of evolving dark energy. It has recently been shown that these tensions can be alleviated by adopting a higher value of the optical depth to reionization parameter, $τ$, when large-scale cosmic microwave background (CMB) polarization data are ignored. We extend this proposal and show that an especially high prior on $τ= 0.11 \pm 0.006$ simultaneously addresses all three of these tensions, significantly reducing the need for new physics beyond $Λ$CDM. We determine the "concordance" value of $τ$ by requiring physical neutrino mass and consistency of the Hubble constant, $H_0$, inferred from the CMB and that preferred by the Dark Energy Spectroscopic Instrument (DESI) baryon acoustic oscillation (BAO) and full-shape measurements. Within this high-$τ$ Universe, we obtain the first $2σ$ detection of a positive neutrino mass, $Σm_ν = 0.10^{+0.04}_{-0.05}$~eV at 68\% C.L., while restoring cosmological concordance between datasets within $Λ$CDM. In particular, low-redshift distance predictions are consistent with DESI BAO observations and the inferred dark-energy equation-of-state parameters are consistent with a cosmological constant, both with and without supernovae data. The concordance power of our $τ$ prior further motivates new measurements of $τ$, e.g., through large angular scale CMB polarization observations with the \textit{LiteBIRD}, CLASS, or proposed PICO experiments. (Abridged)

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Lyman-Alpha Forest and its Cross-Correlation with High-Redshift Galaxies in Effective Field Theory at the Field Level

We present a field-level perturbative forward model for the Lyman-alpha (Lya) forest flux decrement. We validate it on two simulation suites: large-volume AbacusSummit N-body simulations with the Lya forest painted onto the dark matter field, and the Sherwood hydrodynamic simulations. Across the redshift range of the simulations (z=2.0-3.2), the 3D and 1D power spectra of the model match the simulated Lya fields at the 1% (5%) level up to k <= 0.3 (1.0) h/Mpc, with similar performance for the cross-correlation with massive dark matter halos. The counts-in-cells statistic shows excellent agreement down to cell radii of 2 Mpc/h. Leveraging cosmic variance cancellation, the model enables precision measurements of Lya bias parameters and robustly detects the full set of quadratic line-of-sight bias operators, consistent with the notion of naturalness in effective field theory (EFT). We quantify the stochasticity of the Lya forest (the analog to the one-halo term), and find it to be white (scale- and orientation-independent) on large scales, matching EFT predictions. We further find that phenomenological flux power spectrum models, based on modulations of the linear-theory power spectrum, fail at the field level even on quasi-linear scales. For the currently observing Dark Energy Spectroscopic Instrument (DESI), we generate large-scale clustering mocks of the Lya forest to validate cosmological parameter inference pipelines. Looking ahead to its successor, DESI-II, we produce large-volume mocks of representative samples of Lyman-break galaxies (LBGs) and Lya emitters (LAEs), calibrated on Astrid hydrodynamic simulations and matched to observations at z=3, enabling joint analyses of Lya forest and high-redshift galaxy data.

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Constraining primordial non-Gaussianity from DESI DR1 quasars and Planck PR4 CMB Lensing

We present the first measurement of local-type primordial non-Gaussianity from the cross-correlation between $1.2$ million spectroscopically confirmed quasars from the first data release (DR1) of the Dark Energy Spectroscopic Instrument (DESI) and the Planck PR4 CMB lensing reconstructions. The analysis is performed in three tomographic redshift bins covering $0.8 < z < 3.5$, covering a sky fraction of $\sim 20\%$. We adopt a catalog-based pseudo-$C_\ell$ estimator and apply linear imaging weights validated on noiseless mocks. Compared to previous analyses using photometric quasar samples, our results benefit from the high purity of the DESI spectroscopic sample, the reduced noise of PR4 lensing, and the absence of excess large-scale power in the spectroscopic quasar auto-correlation. Fitting simultaneously for the non-Gaussianity parameter $f_{\mathrm{NL}}$ and the linear bias amplitude in each redshift bin, we obtain $f_{\mathrm{NL}} = 2^{+28}_{-34}$ for a response parameter $p=1.6$, and $f_{\mathrm{NL}} = 6^{+20}_{-24}$ for $p=1.0$. These results improve the constraints on $f_{\mathrm{NL}}$ by $\sim 35\%$ compared to the previous analysis based on the Legacy Imaging Survey DR9. Additionally, we derive an optimal weighting scheme to maximize the constraining power. In this case, and assuming $p=1.6$, we obtain $f_\mathrm{NL}=19^{+25}_{-31}$. Our results demonstrate the statistical power of DESI quasars for probing inflationary physics, and highlight the promise of future DESI data releases.

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Bridging Simulations and EFT: A Hybrid Model of the Lyman-Alpha Forest Field

The Lyman-alpha (Lya) forest is a unique probe of cosmology and the intergalactic medium at high redshift and small scales. The statistical power of the ongoing Dark Energy Spectroscopic Instrument (DESI) demands precise theoretical tools to model the Lya forest. We present a hybrid effective field theory (HEFT) forward model in redshift space that leverages the accuracy of non-linear particle displacements computed using the N-body simulation suite AbacusSummit with the predictive power of an analytical, perturbative bias forward model in the framework of the effective field theory (EFT). The residual noise between the model and the simulated Lya field has a nearly white (scale-and orientation-independent) power spectrum on quasi-linear scales, substantially simplifying its modeling compared to a purely perturbative description. As a consequence of the improved control over the 3D Lya forest stochasticity, we find agreement between the modeled and the true power spectra at the 5 per cent level down to scales of k <= 1 h/Mpc. This procedure offers a promising path toward constructing efficient and accurate emulators to predict large-scale clustering summary statistics for full-shape cosmological analyses of Lya forest data from both DESI and its successor, DESI-II.

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DESI DR1 Lyα 1D power spectrum: The Fast Fourier Transform estimator measurement

We present the one-dimensional Lyman-$α$ forest power spectrum measurement derived from the data release 1 (DR1) of the Dark Energy Spectroscopic Instrument (DESI). The measurement of the Lyman-$α$ forest power spectrum along the line of sight from high-redshift quasar spectra provides information on the shape of the linear matter power spectrum, neutrino masses, and the properties of dark matter. In this work, we use a Fast Fourier Transform (FFT)-based estimator, which is validated on synthetic data in a companion paper. Compared to the FFT measurement performed on the DESI early data release, we improve the noise characterization with a cross-exposure estimator and test the robustness of our measurement using various data splits. We also refine the estimation of the uncertainties and now present an estimator for the covariance matrix of the measurement. Furthermore, we compare our results to previous high-resolution and eBOSS measurements. In another companion paper, we present the same DR1 measurement using the Quadratic Maximum Likelihood Estimator (QMLE). These two measurements are consistent with each other and constitute the most precise one-dimensional power spectrum measurement to date, while being in good agreement with results from the DESI early data release.

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The Compressed 3D Lyman-Alpha Forest Bispectrum

Cosmological studies of the Lyman-Alpha (Lya) forest typically constrain parameters using two-point statistics. However, higher-order statistics, such as the three-point function (or its Fourier counterpart, the bispectrum) offer additional information and help break the degeneracy between the mean flux and power spectrum amplitude, albeit at a significant computational cost. To address this, we extend an existing highly informative compression of the bispectrum, the skew spectra, to the Lya forest. We derive the tree-level bispectrum of Lya forest fluctuations in the framework of effective field theory (EFT) directly in redshift space and validate our methodology on synthetic Lya forest data. We measure the anisotropic cross-spectra between the transmitted flux fraction and all quadratic operators arising in the bispectrum, yielding a set of 26 skew spectra. Using idealized 3D Gaussian smoothing (R=10 Mpc/h), we find good agreement (1-2 sigma level based on the statistical errors of the mocks) with the theoretical tree-level bispectrum prediction for monopole and quadrupole up to k <= 0.17 h/Mpc. To enable the cosmological analysis of Lya forest data from the currently observing Dark Energy Spectroscopic Instrument (DESI), where we cannot do 3D smoothing, we use a line-of-sight smoothing and introduce a new statistic, the shifted skew spectra. These probe non-squeezed bispectrum triangles and avoid locally applying quadratic operators to the field by displacing one copy of the field in the radial direction. Using a fixed displacement of 40 Mpc/h (and line-of-sight smoothing of 10 Mpc/h) yields a similar agreement with the theory prediction. For the special case of correlating the squared (and displaced) field with the original one, we analytically forward model the window function making this approach readily applicable to DESI data.

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A Gigaparsec-Scale Hydrodynamic Volume Reconstructed with Deep Learning

The next generation of cosmological spectroscopic sky surveys will probe the distribution of matter across several Gigaparsecs (Gpc) or many billion light-years. In order to leverage the rich data in these new maps to gain a better understanding of the physics that shapes the large-scale structure of the cosmos, observed matter distributions must be compared to simulated mock skies. Small mock skies can be produced using precise, physics-driven hydrodynamical simulations. However, the need to capture small, kpc-scale density fluctuations in the intergalactic medium (IGM) places tight restrictions on the necessary minimum resolution of these simulations. Even on the most powerful supercomputers, it is impossible to run simulations of such high resolution in volumes comparable to what will be probed by future surveys, due to the vast quantity of data needed to store such a simulation in computer memory. However, it is possible to represent the essential features of these high-resolution simulations using orders of magnitude less memory. We present a hybrid approach that employs a physics-driven hydrodynamical simulation at a much lower-than-necessary resolution, followed by a data-driven, deep-learning Enhancement. This hybrid approach allows us to produce hydrodynamic mock skies that accurately capture small, kpc-scale features in the IGM but which span hundreds of Megaparsecs. We have produced such a volume which is roughly one Gigaparsec in diameter and examine its relevant large-scale statistical features, emphasizing certain properties that could not be captured by previous smaller simulations. We present this hydrodynamic volume as well as a companion n-body dark matter simulation and halo catalog which we are making publically available to the community for use in calibrating data pipelines for upcoming survey analyses.

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Modeling the Cosmological Lyman-$α$ Forest at the Field Level

The distribution of absorption lines in the spectra of distant quasars, called the Lyman-$α$ (Ly-$α$) forest, is a unique probe of cosmology and the intergalactic medium at high redshifts and small scales. The statistical power of ongoing redshift surveys demands precise theoretical tools to model the Ly-$α$ forest. We address this challenge by developing an analytic, perturbative forward model to predict the Ly-$α$ forest at the field level for a given set of cosmological initial conditions. Our model shows a remarkable performance when compared with the Sherwood hydrodynamic simulations: it reproduces the flux distribution, the Ly-$α$ - dark matter halo cross-correlations, and the count-in-cell statistics at the percent level down to scales of a few Mpc. Our work provides crucial tools that bridge analytic modeling on large scales with simulations on small-scales, enabling field-level inference from Ly-$α$ forest data and simulation-based priors for cosmological analyses. This is especially timely for realizing the full scientific potential of the Ly-$α$ forest measurements by the Dark Energy Spectroscopic Instrument.

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Measuring and unbiasing the BAO shift in the Lyman-Alpha forest with AbacusSummit

The currently observing Dark Energy Spectroscopic Instrument (DESI) places sub-percent constraints on the Baryon Acoustic Oscillations (BAO) scaling parameters from the Lyman-$α$ (Ly-$α$) forest. However, no systematic error budget stemming from non-linearities in the 3D clustering of the Ly-$α$ forest is included in the DESI-Ly-$α$ analysis. In this work, we measure the size of the shift of the BAO peak using large Ly-$α$ forest mocks produced on the $N$-body simulation suite \textsc{AbacusSummit}. Specifically, we measure the Ly-$α$ auto-correlation and the Ly-$α$-quasar cross-correlation functions. We use the DESI Ly-$α$ forest fitting pipeline, \textsc{Vega}, with the publicly available covariance matrix from eBOSS DR16. To mitigate the noise, we adopt a linear control variates (LCV) technique, reducing the error bars by a factor of up to $\sim \sqrt{50}$ on large scales. From the auto-correlation, we detect a small positive shift in radial direction of $Δα_\parallel = 0.35\%$ at the 3$σ$ level and virtually no shift in the transverse direction, $α_\perp$. From the cross-correlation, we see a similar shift to $Δα_\parallel$, albeit with larger error bars, and a small negative shift, $Δα_\perp=\sim$0.25\%, at the 2$σ$ level. We also make a connection with the Ly-$α$ forest effective field theory (EFT) framework and find that the one-loop EFT power spectrum yields unbiased measurements of the BAO shift parameters in radial and transverse direction for Ly-$α$ auto and the Ly-$α$-quasar cross-correlation measurements. When using the one-loop EFT framework, we find that we can recover the BAO parameters without a shift, which has important implications for future Ly-$α$ forest analyses based on EFT. This work paves the way for the full-shape analysis of DESI and future surveys.

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The ACCEL2 Project: Precision Measurements of EFT Parameters and BAO Peak Shifts for the Lyman-$α$ Forest

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.

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The 3D Lyman-$α$ Forest Power Spectrum from eBOSS DR16

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.

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Maximum A Posteriori Ly-alpha Estimator (MAPLE): Band-power and covariance estimation of the 3D Ly-alpha forest power spectrum

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.

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B-mode constraints from Planck low multipole polarisation data

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.

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Testing for spectral index variations in polarised CMB foregrounds

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.

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Deep Learning of DESI Mock Spectra to Find Damped Lyα Systems

We have updated and applied a convolutional neural network (CNN) machine learning model to discover and characterize damped Ly$α$ 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.

astro-ph.GA

Inference of the optical depth to reionization from low multipole temperature and polarisation Planck data

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.

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