Testing Gravity with Cosmic Voids

Transcription

1 Testing Gravity with Cosmic Voids Nelson Padilla, Enrique Paillas, Joaquin Armijo (PUC, Chile), Yan-Chuan Cai, Andy Taylor, John Peacock (Edinburgh), John Helly, Matthieu Schaller, Baojiu Li (Durham, UK) Claudia Lagos (ICRAR, Australia), Patricia Tissera (UNAB, Chile) Dante Paz, Laura Ceccarelli, Diego García Lambas (IATE, Argentina) Cai, NP, Li 2015, MNRAS, 451, 1036 Cai, Taylor, Peacock, NP, 2016, MNRAS, 462, 2465 Paillas, Lagos, NP, Tissera, Helly, Schaller, arxiv: Armijo, NP, Cai, Paz et al., in prep.

2 Outline MoG-void connection MoG simulations GR or MoG? void abundances and profiles: density and lensing. RSD of voids Cai, NP, Li 2015, MNRAS, 451, 1036 Cai, Taylor, Peacock, NP, 2016, MNRAS, 462, 2465 Paillas, Lagos, NP, Tissera, Helly, Schaller, arxiv: Armijo, NP, Cai, Paz et al., in prep. 2

3 MoG: no dark energy Courtesy Baojiu Li

4 MoG: no dark energy Courtesy Baojiu Li

5 MoG-void connection Clampitt et al calculate the fifth and newtonian forces for a top-hat void. void FN Negative fifth force inside voids acting in opposite direction to newtonian. Stronger for lower internal density, and for small voids. different void radius

6 MoG-void connection Clampitt et al calculate the fifth and newtonian forces for a top-hat void. void FN F5 Negative fifth force inside voids acting in opposite direction to newtonian. Stronger for lower internal density, and for small voids. different void radius

7 Voids in f(r) gravity in Vertigo (1958) by A. Hitchcock

8 Voids in f(r) gravity in Vertigo (1958) by A. Hitchcock

9 Non-linear problem N. Padilla, #KIAScosm16

10 MoG simulations: f(r) WMAP7 cosmology: Analysis centred on 1Gpc^3 volume (SDSS LRG size) F4: No-Ch F5: F6: Gong-Bo Zhao Full-Ch

11 GR N. Padilla, #KIAScosm16 KDUST workshop, IHEP, Beijing Nov 9, 2011 Gong-Bo Zhao

12 f(r) N. Padilla, #KIAScosm16 KDUST workshop, IHEP, Beijing Nov 9, 2011 Gong-Bo Zhao

13 Identifying voids mp05 N. Padilla, #KIAScosm16 Zivick+15 ZOBOV, Watershed, VIDE, : slow transition to average density. mp05 (modified Padilla et al. 2005, MNRAS 363, 977): largest spheres with integrated density Fast transition to average density.

14 Void abundances (mass) mp05 void abundances in the mass in f(r) simulations and GR. cumulative abundance 25% difference between F6 and GR errors for LRG volume (highly significant), and up to x3 factor for F4. Practical difficulties to measure. CPL, arxiv: , see also Zivick et al., 2015

15 Void abundances for biased tracers mp05 void abundances in f(r) simulations and GR, using SDSS-LRG volume and space density. cumulative abundance Practical. Behaviour is reversed. Differences are smaller and depend on radius of void when tracers are used. errors for LRG volume 2-sigma for F6 CPL, arxiv:

16 Haloes and the fifth N. Padilla, #KIAScosm16 force F5 halo FN Positive fifth force outside haloes acting in addition to newtonian. Effect present at low density environments where fifth force is stronger.

17 Stacked void profiles Differential profiles traced by haloes. Voids identified using haloes Because of the way halos form in f(r) models, the stacked profiles only show mild differences CPL, arxiv:

18 Stacked void profiles Differential profiles traced by DM. Voids identified using haloes DM profiles confirm emptier voids in f(r) models even if halo density is the same. CPL, arxiv:

19 Stacked void profiles Differential profiles traced by DM. Voids identified using haloes DM profiles confirm emptier voids in f(r) models even if halo density is the same. But how to measure DM profiles around halo defined voids? CPL, arxiv:

20 Stacked void profiles Differential profiles traced by DM. sharp density rise Voids identified using haloes DM profiles confirm emptier voids in f(r) models even if halo density is the same. But how to measure DM profiles around halo defined voids? CPL, arxiv:

21 Lensing profiles Derivative of the 3D density profile around mp05 voids as analog of the tangential shear profile. CLP15 full covariance matrix errors for LRG volume See Amendola et al. 1999; Krause et al. 2013; Higuchi et al. 2013

22 Lensing profiles Derivative of the 3D density profile around mp05 voids as analog of the tangential shear profile. CLP15 3-sigma detection expected for F5. full covariance matrix errors for LRG volume See Amendola et al. 1999; Krause et al. 2013; Higuchi et al. 2013

23 Lensing profiles Derivative of the 3D density profile around mp05 voids as analog of the tangential shear profile. CLP15 3-sigma detection expected for F5. full covariance matrix errors for LRG volume Paillas+16: no effect from baryons See Amendola et al. 1999; Krause et al. 2013; Higuchi et al. 2013

24 Testing gravity with cosmic voids N. Padilla, #KIAScosm16 Voids expand ~10-50km/s faster in f(r) (test done with 1 Gpc/h a side volume) CTPP16, see also Li et al. 2016, Song et al. 2016, Chaung et al. 2016

25 Testing gravity with cosmic voids N. Padilla, #KIAScosm16 Voids expand ~10-50km/s faster in f(r) (test done with 1 Gpc/h a side volume) CTPP16, see also Li et al. 2016, Song et al. 2016, Chaung et al. 2016

26 Testing gravity with cosmic voids N. Padilla, #KIAScosm16 Voids expand ~10-50km/s faster in f(r) (test done with 1 Gpc/h a side volume) CTPP16, see also Li et al. 2016, Song et al. 2016, Chaung et al Paillas et al. (2016): effects of not considering baryons ~5km/s

27 Contracting region Virialised region

28 Contracting region Virialised region Void region

29 Contracting region Virialised region Void region

30 Contracting region Virialised region Void region Voids: µ = cos

31 s (r, 1) = (1 + First RSD model with no input phenomenological profile needed (cf. Hamaus+16) s (r, µ) = (r)+ 1 3 (r)+ µ 2 [ (r) (r)] ) (r) s (r, 0) = (r)+ 1 3 (r) (r) = 3 Z r r 3 (r 0 )r 02 dr (r) (line of sight) (transverse) = f/b Cai et al N. Padilla, #KIAScosm16 Simulation vs. linear model linear RSD

32 s (r, 1) = (1 + First RSD model with no input phenomenological profile needed (cf. Hamaus+16) s (r, µ) = (r)+ 1 3 (r)+ µ 2 [ (r) (r)] ) (r) s (r, 0) = (r)+ 1 3 (r) (r) = 3 Z r r 3 (r 0 )r 02 dr (r) (line of sight) (transverse) = f/b Cai et al N. Padilla, #KIAScosm16 Simulation vs. linear model linear RSD

33 Extracting the growth (GR) 2 CTPP16

34 Extracting the growth (GR) 2 CTPP16

35 Extracting the growth (GR) 9% in 3 cubic Gpc volume CTPP16

36 Extracting the growth (GR) 9% 5% in 3 cubic Gpc volume CTPP16

37 Conclusions Voids can provide high signal-to-noise to detect f(r) gravity. Void-tracer RSD is well described by linear theory! Voids provide many plausible tests involving: abundance of tracer voids for large void sizes. density profiles of voids if mass is used. lensing by voids is a good way to trace profiles using the mass. Redshift-space distortions for 5% estimate of growth rate.

38 Conclusions Voids can provide high signal-to-noise to detect f(r) gravity. Void-tracer RSD is well described by linear theory! Voids provide many plausible tests involving: abundance of tracer voids for large void sizes. density profiles of voids if mass is used. lensing by voids is a good way to trace profiles using the mass. Redshift-space distortions for 5% estimate of growth rate. Cai, NP, Li 2015, MNRAS, 451, 1036 Cai, Taylor, Peacock, NP, 2016, MNRAS, 462, 2465 Paillas, Lagos, NP, Tissera, Helly, Schaller, arxiv: Armijo, NP, Cai, Paz et al., in prep.

39 Hitchcock et al., 1958, Paramount Pictures Cai, NP, Li 2015, MNRAS, 451, 1036 Cai, Taylor, Peacock, NP, 2016, MNRAS, 462, 2465 Paillas, Lagos, NP, Tissera, Helly, Schaller, arxiv: Armijo, NP, Cai, Paz et al., in prep.