Cosmology and neutrino mass with the Minimum Spanning Tree

Abstract

The information content of the minimum spanning tree (MST), used to capture higher-order statistics and other information from the cosmic web, is compared to that of the power spectrum for a 𝜈ΛCDM model. The measurements are made in redshift space using haloes from the Quijote simulation of mass ≥ 3.2 × 10 13 ℎ −1 M in a box of length 𝐿 box = 1 ℎ −1 Gpc. The power spectrum multipoles (monopole and quadrupole) are computed for Fourier modes in the range 0.006 < 𝑘 < 0.5 ℎMpc −1 . For comparison the MST is measured with a minimum length scale of 𝑙 min 13 ℎ −1 Mpc. Combining the MST and power spectrum allows for many of the individual degeneracies to be broken; on its own the MST provides tighter constraints on the sum of neutrino masses 𝑀 𝜈 , Hubble constant ℎ, spectral tilt 𝑛 s , and baryon energy density Ω b but the power spectrum alone provides tighter constraints on Ω m and 𝜎 8 . The power spectrum on its own gives a standard deviation of 0.25 eV on 𝑀 𝜈 while the combination of power spectrum and MST gives 0.11 eV. There is similar improvement of a factor of two for ℎ, 𝑛 s , and Ω b . These improvements appear to be driven by the MST's sensitivity to small scale clustering, where the effect of neutrino free-streaming becomes relevant. The MST is shown to be a powerful tool for cosmology and neutrino mass studies, and therefore could play a pivotal role in ongoing and future galaxy redshift surveys (such as DES, DESI, Euclid, and Rubin-LSST).

Cosmology with One Galaxy?

“…However, we do not know what estimator would allow us to extract the maximum amount of information for non-Gaussian density fields, such as the matter and galaxy distribution on nonlinear scales. Quantifying the information content from different estimators is currently a very active area of research (Coulton et al 2019;Li et al 2019;Marques et al 2019;Ajani et al 2020;Allys et al 2020;Banerjee et al 2020;Dai et al 2020;de la Bella et al 2021;Friedrich et al 2020;Giri & Smith 2020;Hahn et al 2020;Villaescusa-Navarro et al 2020;Uhlemann et al 2020;Zhang et al 2020;Banerjee & Abel 2021aBayer et al 2021aBayer et al , 2021bCheng and Ménard 2021;Gualdi et al 2021aGualdi et al , 2021bHahn & Villaescusa-Navarro 2021;Harnois-Déraps et al 2021;Kuruvilla 2021;Kuruvilla & Aghanim 2021;Massara et al 2021;Naidoo et al 2021;Porth et al 2021;Samushia et al 2021;Valogiannis & Dvorkin 2021;Harnois-Déraps et al 2022).…”

Cosmology with one galaxy?

“…However, we do not know what estimator would allow us to extract the maximum amount of information for non-Gaussian density fields, like the matter and galaxy distribution resembles on non-linear scales. Quantifying the information content from different estimators is currently a very active area of research (Villaescusa-Navarro et al 2020;Samushia et al 2021;Gualdi et al 2021a;Kuruvilla & Aghanim 2021;Banerjee et al 2020;Hahn et al 2020;Uhlemann et al 2020;Friedrich et al 2020;Massara et al 2021;Dai et al 2020;Allys et al 2020;Banerjee & Abel 2021a,b;Gualdi et al 2021b,a;Giri & Smith 2020;de la Bella et al 2020;Hahn & Villaescusa-Navarro 2021;Valogiannis & Dvorkin 2021;Kuruvilla 2021;Naidoo et al 2021;Porth et al 2021;Harnois-Déraps et al 2022;Li et al 2019;Coulton et al 2019;Marques et al 2019;Zhang et al 2020;Ajani et al 2020;Harnois-Déraps et al 2021;Cheng & Ménard 2021;Harnois-Déraps et al 2022).…”

Learning cosmology and clustering with cosmic graphs

“…That information is typically extracted using summary statistics like the power spectrum, that are known to be suboptimal descriptors of the statistical properties of non-Gaussian density fields, like the galaxy density field on non-linear scales. Recently, a large effort has been carried out to compare how much cosmological information can be extracted from pablo.villanueva.domingo@gmail.com fvillaescusa@flatironinstitute.org different summary statistics of the web, such as correlation functions and higher momenta (Villaescusa-Navarro et al 2020a;Samushia et al 2021;Gualdi et al 2020Gualdi et al , 2021Hahn et al 2020;Hahn & Villaescusa-Navarro 2021a), counts in cells (Banerjee & Abel 2021a,b;Uhlemann et al 2020), Minkowski functionals (Vicinanza et al 2019;Liu et al 2022;Marques et al 2019), peak counts (Li et al 2019;Ajani et al 2020;Harnois-Déraps et al 2021, 2022, wavelets (Valogiannis & Dvorkin 2021;Eickenberg et al 2022;Cheng & Ménard 2021), via the kinetic Sunyaev Zel'dovich effect (Kuruvilla & Aghanim 2021;Kuruvilla 2021;Giri & Smith 2020) or through other approaches (Banerjee et al 2020;Bayer et al 2021;Allys et al 2020;Naidoo et al 2021;de la Bella et al 2020;Massara et al 2021;Liu & Madhavacheril 2019;Zhang et al 2020;Simpson et al 2011Simpson et al , 2013Friedrich et al 2020;Dai et al 2020).…”