Dark Energy Survey Year 3 results: Cosmology with peaks using an emulator approach

Abstract

We constrain the matter density Ω m and the amplitude of density fluctuations 𝜎 8 within the ΛCDM cosmological model with shear peak statistics and angular convergence power spectra using mass maps constructed from the first three years of data of the Dark Energy Survey (DES Y3). We use tomographic shear peak statistics, including cross-peaks: peak counts calculated on maps created by taking a harmonic space product of the convergence of two tomographic redshift bins. Our analysis follows a forward-modelling scheme to create a likelihood of these statistics using N-body simulations, using a Gaussian process emulator. We take into account the uncertainty from the remaining, largely unconstrained ΛCDM parameters (Ω b , 𝑛 s and ℎ). We include the following lensing systematics: multiplicative shear bias, photometric redshift uncertainty, and galaxy intrinsic alignment. Stringent scale cuts are applied to avoid biases from unmodelled baryonic physics. We find that the additional non-Gaussian information leads to a tightening of the constraints on the structure growth parameter yielding 𝑆 8 ≡ 𝜎 8 √︁ Ω m /0.3 = 0.797 +0.015 −0.013 (68% confidence limits), with a precision of 1.8%, an improvement of 38% compared to the angular power spectra only case.The results obtained with the angular power spectra and peak counts are found to be in agreement with each other and no significant difference in 𝑆 8 is recorded. We find a mild tension of 1.5 𝜎 between our study and the results from Planck 2018, with our analysis yielding a lower 𝑆 8 . Furthermore, we observe that the combination of angular power spectra and tomographic peak counts breaks the degeneracy between galaxy intrinsic alignment 𝐴 IA and 𝑆 8 , improving cosmological constraints. We run a suite of tests concluding that our results are robust and consistent with the results from other studies using DES Y3 data.

A Full $w$CDM Analysis of KiDS-1000 Weak Lensing Maps using Deep Learning

“…The unique ability of WL to directly observe the matter contents of the Universe makes it an ideal probe to constrain cosmological parameters. This has already been demonstrated by WL surveys such as the Canada France Hawaii Telescope Lensing Survey (CFHTLenS)[3] [4], the Kilo-Degree Survey (KiDS)[5] [6,7], the Dark Energy Survey (DES)[8] [9,10], and the Subaru Hyper Suprime-Cam (HSC) [11] [12]. Future surveys such as Euclid [13], the Vera C. Rubin Observatory [14] or the Wide-Field Infrared Survey Telescope (WFIRST) [15] will be able to provide even more precise measurements.…”

“…Such approaches include weak lensing peak statistics (e.g. [10,[16][17][18][19][20][21][22]), the three-point correlations function (e.g. [23,24]) or machine learning based methods (e.g.…”

$\texttt{matryoshka}$ II: Accelerating Effective Field Theory Analyses of the Galaxy Power Spectrum

“…The use of emulators is becoming commonplace in many forms of cosmological analysis (Chapman et al 2021;Kobayashi et al 2021;Zürcher et al 2021;White et al 2021;. These emulators can be thought of as sophisticated interpolation schemes that aim to approximate a computationally expensive model given a set of example outputs.…”

Dark Energy Survey Year 3 Results: Three-Point Shear Correlations and Mass Aperture Moments

“…The community has followed several approaches to extracting the information contained in higher order shear statistics. For example, non-Gaussian information can be obtained with position-dependent or integrated 2pt lensing signatures (Halder et al 2021, Jung et al 2021, peak statistics (Kacprzak et al 2016, Zürcher et al 2021, density splits of the shear field (Friedrich et al 2018, Gruen et al 2018) as well as with techniques borrowed from artificial intelligence and neural networks (Cheng et al 2020, Fluri et al 2019, Jeffrey et al 2021, Lu et al 2021). Another approach is to directly measure 3rd or higher order statistics of the shear field in the form of ellipticity correlations (Van Waerbeke et al (2002), Benabed & Scoccimarro (2006)), mass aperture moments (Fu et al 2014, Jarvis et al 2004, Semboloni et al 2011 or lensing mass maps (Gatti et al 2021a).…”