Magnification and evolution biases in large-scale structure surveys

Measurements of galaxy clustering in upcoming surveys such as those planned for the Euclid and Roman satellites, and the SKA Observatory, will be sensitive to distortions from lensing magnification and Doppler effects, beyond the standard redshift-space distortions. The amplitude of these contributions depends sensitively on magnification bias and evolution bias in the galaxy number density. Magnification bias quantifies the change in the observed number of galaxies gained or lost by lensing magnification, while evolution bias quantifies the physical change in the galaxy number density relative to the conserved case. These biases are given by derivatives of the number density, and consequently are very sensitive to the form of the luminosity function. We give a careful derivation of the magnification and evolution biases, clarifying a number of results in the literature. We then examine the biases for a variety of surveys, encompassing galaxy surveys and line intensity mapping at radio and optical/near-infrared wavelengths.

ShapeFit: extracting the power spectrum shape information in galaxy surveys beyond BAO and RSD

In the standard (classic) approach, galaxy clustering measurements from spectroscopic surveys are compressed into baryon acoustic oscillations and redshift space distortions measurements, which in turn can be compared to cosmological models. Recent works have shown that avoiding this intermediate step and fitting directly the full power spectrum signal (full modelling) leads to much tighter constraints on cosmological parameters. Here we show where this extra information is coming from and extend the classic approach with one additional effective parameter, such that it captures, effectively, the same amount of information as the full modelling approach, but in a model-independent way. We validate this new method (ShapeFit) on mock catalogs, and compare its performance to the full modelling approach finding both to deliver equivalent results. The ShapeFit extension of the classic approach promotes the standard analyses at the level of full modelling ones in terms of information content, with the advantages of i) being more model independent; ii) offering an understanding of the origin of the extra cosmological information; iii) allowing a robust control on the impact of observational systematics.

Multi-wavelength spectroscopic probes: biases from neglecting light-cone effects

Next-generation cosmological surveys will observe larger cosmic volumes than ever before, enabling us to access information on the primordial Universe, as well as on relativistic effects. In a companion paper, we applied a Fisher analysis to forecast the expected precision on f NL and the detectability of the lensing magnification and Doppler contributions to the power spectrum. Here we assess the bias on the best-fit values of f NL and other parameters, from neglecting these light-cone effects. We consider forthcoming 21cm intensity mapping surveys (SKAO) and optical galaxy surveys (DESI and Euclid), both individually and combined together. We conclude that lensing magnification at higher redshifts must be included in the modelling of spectroscopic surveys. If lensing is neglected in the analysis, this produces a bias of more than 1σ — not only on f NL, but also on the standard cosmological parameters.

Capturing the Physics of MaNGA Galaxies with Self-supervised Machine Learning

As available data sets grow in size and complexity, advanced visualization tools enabling their exploration and analysis become more important. In modern astronomy, integral field spectroscopic galaxy surveys are a clear example of increasing high dimensionality and complex data sets, which challenges the traditional methods used to extract the physical information they contain. We present the use of a novel self-supervised machine-learning method to visualize the multidimensional information on stellar population and kinematics in the MaNGA survey in a 2D plane. Our framework is insensitive to nonphysical properties such as the size of the integral field unit and is therefore able to order galaxies according to their resolved physical properties. Using the extracted representations, we study how galaxies distribute based on their resolved and global physical properties. We show that even when exclusively using information about the internal structure, galaxies naturally cluster into two well-known categories, rotating main-sequence disks and massive slow rotators, from a purely data-driven perspective, hence confirming distinct assembly channels. Low-mass rotation-dominated quenched galaxies appear as a third cluster only if information about the integrated physical properties is preserved, suggesting a mixture of assembly processes for these galaxies without any particular signature in their internal kinematics that distinguishes them from the two main groups. The framework for data exploration is publicly released with this publication, ready to be used with the MaNGA or other integral field data sets.

CHIME/FRB Catalog 1 Results: Statistical Cross-correlations with Large-scale Structure

The CHIME/FRB Project has recently released its first catalog of fast radio bursts (FRBs), containing 492 unique sources. We present results from angular cross-correlations of CHIME/FRB sources with galaxy catalogs. We find a statistically significant (p-value ∼ 10−4, accounting for look-elsewhere factors) cross-correlation between CHIME FRBs and galaxies in the redshift range 0.3 ≲ z ≲ 0.5, in three photometric galaxy surveys: WISE × SCOS, DESI-BGS, and DESI-LRG. The level of cross-correlation is consistent with an order-one fraction of the CHIME FRBs being in the same dark matter halos as survey galaxies in this redshift range. We find statistical evidence for a population of FRBs with large host dispersion measure (∼400 pc cm−3) and show that this can plausibly arise from gas in large halos (M ∼ 1014 M ⊙), for FRBs near the halo center (r ≲ 100 kpc). These results will improve in future CHIME/FRB catalogs, with more FRBs and better angular resolution.

AutoProf – I. An automated non-parametric light profile pipeline for modern galaxy surveys

ABSTRACT We present an automated non-parametric light profile extraction pipeline called autoprof. All steps for extracting surface brightness (SB) profiles are included in autoprof, allowing streamlined analyses of galaxy images. autoprof improves upon previous non-parametric ellipse fitting implementations with fit-stabilization procedures adapted from machine learning techniques. Additional advanced analysis methods are included in the flexible pipeline for the extraction of alternative brightness profiles (along radial or axial slices), smooth axisymmetric models, and the implementation of decision trees for arbitrarily complex pipelines. Detailed comparisons with widely used photometry algorithms (photutils, xvista, and galfit) are also presented. These comparisons rely on a large collection of late-type galaxy images from the PROBES catalogue. The direct comparison of SB profiles shows that autoprof can reliably extract fainter isophotes than other methods on the same images, typically by >2 mag arcsec−2. Contrasting non-parametric elliptical isophote fitting with simple parametric models also shows that two-component fits (e.g. Sérsic plus exponential) are insufficient to describe late-type galaxies with high fidelity. It is established that elliptical isophote fitting, and in particular autoprof, is ideally suited for a broad range of automated isophotal analysis tasks. autoprof is freely available to the community at: https://github.com/ConnorStoneAstro/AutoProf.

improved Master for the LSS: fast and accurate analysis of the two-point power spectra and correlation functions

ABSTRACT We review the methodology for measurements of two-point functions of the cosmological observables, both power spectra and correlation functions. For pseudo-Cℓ estimators, we will argue that the window-weighted overdensity field can yield more optimal measurements as the window acts as an inverse noise weight, an effect that becomes more important for surveys with a variable selection function. We then discuss the impact of approximations made in the Master algorithm and suggest improvements, the iMaster algorithm, which uses the theoretical model to give unbiased results for arbitrarily complex windows provided that the model satisfies weak accuracy conditions. The methodology of iMaster algorithm is also generalized to the correlation functions to reconstruct the binned power spectra, for E/B mode separation, or to properly convolve the correlation functions to account for the scale cuts in the Fourier space model. We also show that the errors in the window estimation lead to both additive and multiplicative effects on the overdensity field. Accurate estimation of window power can be required up to scales of ∼2ℓmax or larger. Mis-estimation of the window power leads to biases in the measured power spectra, which scale as ${\delta C_\ell }\sim M^W_{\ell \ell ^{\prime }}\delta W_{\ell ^{\prime }}$, where the $M^W_{\ell \ell ^{\prime }}$ scales as ∼(2ℓ + 1)Cℓ leading to effects that can be important at high ℓ. While the notation in this paper is geared towards photometric galaxy surveys, the discussion is equally applicable to spectroscopic galaxy, intensity mapping, and Cosmic Microwave Background radiation (CMB) surveys.