Euclid: Forecasts from the void-lensing cross-correlation

Author:

Bonici M.ORCID,Carbone C.,Davini S.,Vielzeuf P.,Paganin L.,Cardone V.,Hamaus N.,Pisani A.,Hawken A. J.,Kovacs A.,Nadathur S.,Contarini S.,Verza G.,Tutusaus I.,Marulli F.,Moscardini L.,Aubert M.,Giocoli C.,Pourtsidou A.,Camera S.,Escoffier S.,Caminata A.,Di Domizio S.,Martinelli M.,Pallavicini M.,Pettorino V.,Sakr Z.,Sapone D.,Testera G.,Tosi S.,Yankelevich V.,Amara A.,Auricchio N.,Baldi M.,Bonino D.,Branchini E.,Brescia M.,Brinchmann J.,Capobianco V.,Carretero J.,Castellano M.,Cavuoti S.,Cledassou R.,Congedo G.,Conversi L.,Copin Y.,Corcione L.,Courbin F.,Cropper M.,Da Silva A.,Degaudenzi H.,Douspis M.,Dubath F.,Duncan C. A. J.,Dupac X.,Dusini S.,Ealet A.,Farrens S.,Ferriol S.,Fosalba P.,Frailis M.,Franceschi E.,Fumana M.,Gómez-Alvarez P.,Garilli B.,Gillis B.,Grazian A.,Grupp F.,Guzzo L.,Haugan S. V. H.,Holmes W.,Hormuth F.,Hornstrup A.,Jahnke K.,Kümmel M.,Kermiche S.,Kiessling A.,Kilbinger M.,Kunz M.,Kurki-Suonio H.,Laureijs R.,Ligori S.,Lilje P. B.,Lloro I.,Maiorano E.,Mansutti O.,Marggraf O.,Markovic K.,Massey R.,Medinaceli E.,Melchior M.,Meneghetti M.,Meylan G.,Moresco M.,Munari E.,Niemi S. M.,Padilla C.,Paltani S.,Pasian F.,Pedersen K.,Percival W. J.,Pires S.,Polenta G.,Poncet M.,Popa L.,Raison F.,Rebolo R.,Renzi A.,Rhodes J.,Rossetti E.,Saglia R.,Sartoris B.,Scodeggio M.,Secroun A.,Seidel G.,Sirignano C.,Sirri G.,Stanco L.,Starck J.-L.,Surace C.,Tallada-Crespí P.,Tavagnacco D.,Taylor A. N.,Tereno I.,Toledo-Moreo R.,Torradeflot F.,Valentijn E. A.,Valenziano L.,Wang Y.,Weller J.,Zamorani G.,Zoubian J.,Andreon S.

Abstract

The Euclid space telescope will survey a large dataset of cosmic voids traced by dense samples of galaxies. In this work we estimate its expected performance when exploiting angular photometric void clustering, galaxy weak lensing, and their cross-correlation. To this aim, we implemented a Fisher matrix approach tailored for voids from the Euclid photometric dataset and we present the first forecasts on cosmological parameters that include the void-lensing correlation. We examined two different probe settings, pessimistic and optimistic, both for void clustering and galaxy lensing. We carried out forecast analyses in four model cosmologies, accounting for a varying total neutrino mass, Mν, and a dynamical dark energy (DE) equation of state, w(z), described by the popular Chevallier-Polarski-Linder parametrization. We find that void clustering constraints on h and Ωb are competitive with galaxy lensing alone, while errors on ns decrease thanks to the orthogonality of the two probes in the 2D-projected parameter space. We also note that, as a whole, with respect to assuming the two probes as independent, the inclusion of the void-lensing cross-correlation signal improves parameter constraints by 10 − 15%, and enhances the joint void clustering and galaxy lensing figure of merit (FoM) by 10% and 25%, in the pessimistic and optimistic scenarios, respectively. Finally, when further combining with the spectroscopic galaxy clustering, assumed as an independent probe, we find that, in the most competitive case, the FoM increases by a factor of 4 with respect to the combination of weak lensing and spectroscopic galaxy clustering taken as independent probes. The forecasts presented in this work show that photometric void clustering and its cross-correlation with galaxy lensing deserve to be exploited in the data analysis of the Euclid galaxy survey and promise to improve its constraining power, especially on h, Ωb, the neutrino mass, and the DE evolution.

Publisher

EDP Sciences

Subject

Space and Planetary Science,Astronomy and Astrophysics

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3