Constraining neutrino mass with weak lensing Minkowski Functionals

Abstract

The presence of massive neutrinos affects structure formation, leaving imprints on large-scale structure observables such as the weak lensing field. The common lensing analyses with two-point statistics are insensitive to the large amount of non-Gaussian information in the density field. We investigate non-Gaussian tools, in particular the Minkowski Functionals (MFs)-morphological descriptors including area, perimeter, and genus-in an attempt to recover the higher-order information. We use convergence maps from the Cosmological Massive Neutrino Simulations (MassiveNus) and assume galaxy noise, density, and redshift distribution for an LSST-like survey. We show that MFs are sensitive to the neutrino mass sum, and the sensitivity is redshift dependent and is non-Gaussian. We find that redshift tomography significantly improves the constraints on neutrino mass for MFs, compared to the improvements for the power spectrum. We attribute this to the stronger redshift dependence of neutrino effects on small scales. We then build an emulator to model the power spectrum and MFs, and study the constraints on [M ν , Ω m , A s ] from the power spectrum, MFs, and their combination. We show that MFs significantly outperform the power spectrum in constraining neutrino mass, by more than a factor of four. However, a thorough study of the impact from systematics such as baryon physics and galaxy shape and redshift biases will be important to realize the full potential of MFs.

Neutrino effects on the morphology of cosmic large-scale structure

“…This may be due to that topology (V 3 ) is more susceptible to this artificial smearing of LSS than other V i s, on the premise of noise reduction. From the decline ratios of ∆V i s' amplitudes and the S/N ratios of neutrino signatures on ∆V i s caused by different smoothing in Figure 1 and Figure 2, we can preliminary infer that the sensitivities of MFs to nonGaussianity (and to m ν ) roughly obey V 1 < V 2 < V 3 V 4 , which is consistent with previous studies on 2D MFs of weak lensing and CMB (e.g., [35,45,52]).…”

Weak lensing minima and peaks: Cosmological constraints and the impact of baryons

“…For a 2D field, there are three Minkowski functionals: the area V 0 , perimeter V 1 , and genus V 2 . Previous work by Petri et al (2013Petri et al ( , 2015, Marques et al (2019) found that Minkowski functionals can offer strong cosmological constraints. In this Appendix, we are particularly interested in the genus V 2 -the difference between the number of 'holes' and the number of 'islands', which we think can be closely related to the minimum counts and peaks counts in the field, respectively.…”

“…Further, by definition it only accesses third-order information and is therefore insensitive to information in fourth-and higher-order moments. Therefore, weak lensing summary statistics, which can be easier to measure and also contain information of all orders, have been proposed as simpler alternatives, such as the peak counts (Jain & Van Waerbeke 2000;Marian, Smith & Bernstein 2009;Maturi et al 2010;Yang et al 2011;Marian et al 2013;Liu et al 2015Liu et al , 2014Lin & Kilbinger 2015a, b;Kacprzak et al 2016;Martinet et al 2018;Peel et al 2018;Shan et al 2018;Li et al 2019), Minkowski functionals (Kratochvil et al 2012;Petri et al 2013Petri et al , 2015Shirasaki & Yoshida 2014;Marques et al 2019), clipped field statistics (Giblin et al 2018), and higher-order moments (Bernardeau, van Waerbeke & Mellier 1997;Hui 1999;Van Waerbeke et al 2001;Takada & Jain 2002;Zaldarriaga & Scoccimarro 2003;Kilbinger & Schneider 2005;Petri et al 2015;Peel et al 2018).…”

Optimal void finders in weak lensing maps

“…There are multiple other WL statistics beyond the power spectrum that have been utilized to constrain cosmology, and we briefly mention a few here. The first is Minkowski functionals, which can provide additional constrains on the dark energy equation of state parameter (Kratochvil et al 2012;Petri et al 2013;Ling et al 2015;Marques et al 2019). The WL bispectrum, which is sensitive to non-Gaussianity by definition, has been shown to be a useful statistic for future surveys (Cooray & Hu 2001;Rizzato et al 2019;Munshi & McEwen 2020), and can be used to improve parameter constraints, such as neutrino masses (Coulton et al 2019).…”