Gravitational lensing science with CSS-OS Weak lensing: Zuhui Fan - - PowerPoint PPT Presentation

Gravitational lensing science with CSS-OS

Weak lensing: Zuhui Fan (PKU) Shear measurement: Jun Zhang (SJTU) Strong lensing: Ran Li (NAOC)

Outline

• Organization of working groups
• Weak lensing science
• Plan of weak lensing simulation
• Shear measurement
• Strong lensing science
• Strong lensing simulation

Weak lensing working group

Memebers: Zuhui Fan (PKU), Jun Zhang (SJTU), Ran Li (NAOC), Dezi Liu (YNU), Xiangkun Liu (YNU), Chuzhong Pan (PKU), Chunxiang Wang (NAOC), Qiao Wang (NAOC), Liping Fu (SHNU), Xi Kang (PMO) Guoliang Li (PMO), Wentao Luo (SJTU)，Yu Yu (SJTU), Shan Huanyuan (Bonn Uni.)，Shuo Yuan(PKU) Aims: Mock weak lensing maps based on cosmological simulation Imaging simulations 2-3 shear measurement pipelines Fast statistical analysis tools and theoretical analysis codes Gravitational lensing workshop every 6 months, next meeting in Yun Nan on 4th-7th December 2017.

Strong lensing working group

• Member: Ran Li (NAOC), Nan Li (Nottingham) , Dezi Liu (YNU), Guoliang

Li (PMO), Xiaoyue Cao (NAOC), Ye Cao (NAOC), Yun Chen (NAOC), Yiping Shu (NAOC), Xin Wang (UCLA), Xiaolei Meng (Tsinghua)….

• Email list CSST_SLWG@googlegroups.com
• Collaboration tool: https://tiangongslwg.slack.com/
• Regular telecon every 2-3 weeks
• A meeting planned for next spring
• Currently, aim to construct a set of strong lensing simulations and

produce some forecast papers.

Gravitational lensing effects – gravitational in origin – everywhere in the universe

Excellent cosmological probe

Statistical tools

• 2-pt correlation
• 3-pt correlation
• Peak statistics
• Minkowski functionals
• Nature of dark energy , deviation from w=-1 (1 errors on w0 & wa of 0.07

and 0.1 respectively)

• Precise measurement of expansion history of the universe
• Dark matter mass distribution to redshift 2.
• neutrino mass
• Test General relativity
• Primordial non-Gaussianity

Cosmological model Parameters

Cosmic shear (signal ~ a few percent)

HS f(R) theory – fR0 parameter with fR0=0 for GR Example: Using Peaks statistics to constrain the law of gravity Modified gravity theories f(R) gravity theory with chameleon effect give rise to the late-time cosmic accelerating expansion satisfy the solar system gravity test However, the formation and evolution of LSS are different With priors from WMAP9 or Planck15, fR0 can be constrained tightly

Liu, Fan et al. 2016

Using CFHTLens data

Weak lensing of galaxy formation

• Galaxy-dark matter halo co-

evolution

• Mass and structure of subhalo
• f satellite galaxies
• Mass, shape and profile of

clusters

• Mass distribution in

superclusters, filaments and voids

• Mass of dwarf galaxies, UDGs …

NASA, ESA, D. Coe

Weak lensing analyses

Stage I: first detections of cosmic shear, is around the year of 2000 Stage II: CFHTLenS as the best representative survey 154 degree^2, mag=24.5, seeing ~0.7” Stage III: present (KiDS, DES, HSC, ~1000 degree^2) Stage IV: in the future (CSS-OS, LSST, EUCLID)

CSS-OS: Area = 15000 deg^2 resolution= 80% light within 0.15” NUV, u, g, r, i, z, Y multi-band photometry, with average limit 25.2 (g=26.3) for point source. ~300 times more galaxy shapes than stage II survey.

Challenges

Observationally：

• reconstruct the PSF, and

measure accurately the shapes of billions faint galaxies

• redshift information of individual

galaxies Theoretically :

• fully explore different statistical

quantities

• accuracy and speed of

theoretical tools Thorough understanding about potential systematics, both theoretical and observational

Great10 handbook

Shan et al. arXiv: 1709.07651

(1+m) degenerates with cosmological parameters CSS-OS like survey requires dm<0.002 !

Liu, X.K., Pan, C.Z. et al. 2017 CFHTLenS data : m~-0.05 from CFHTLenS simulation calibration

• ur results: m~-0.2 (**should be understood as an effective bias,

not necessarily shear measurement bias) m Prior [-0.2, 0.2]

Self-calibration

N-Body Cosmology simulation Lens galaxy Source galaxy

SAM

Dark matter Density Lensing potential

Ray- tracing

Hubble HUDF, COSMOS

Ideal Images

Optical design CCD properties Instrumental systematics

PSF

Filter response. Detector noise, pixel effects, Exposure strategy Filter arrangement Stray light Other detector systematics

Final image

+ +

Shear measurement pipeline Theoretical tools

Recovered Shapes of galaxies

?

Ideal Images

By Dezi Liu

Mock Star catalogue

Plan of Cosmological simulations for CSS-OS

• Hyper-Millennium, by Key laboratory for Computational

Astrophysics, CAS

• 3 Gpc box, ~10000 degree^2 light-cone to z=3, ~100

degree^2 light cone to z=5.

• Mass resolution 2.5x10^8 Msun, Resolve all the dark

matter haloes that source galaxies reside in.

• Semi-analytical galaxy catalogue
• Data size ~ 2 PGb, might run this year.

PSF reconstruction and shear measurement pipelines

• Correction for PSF model-independently;
• Noise Effects are removed;
• Accurate to the second order in shear;
• Clarify the requirement on the pixel size;
• No need to fix the centroid of the image;
• Fast image processing< 10-2 CPU sec/Gal;
• Immune to misidentification of stars as

galaxies.

Jun Zhang’s group has solved a number of bottleneck problems in cosmic shear measurement, including

References:

JZ, 2008, MNRAS, 383, 113 JZ, 2010, MNRAS, 403, 673 JZ & Komatsu, 2011, MNRAS, 414, 1047 JZ, 2011, JCAP, 11, 041 JZ, Luo, Foucaud, 2015, JCAP, 1, 24 JZ, Zhang, Luo, 2017, ApJ, 834, 8 Lu, JZ, Dong, et al., 2017, AJ, 153, 197

The new method is carried out in Fourier space. In real space, it corresponds to measuring the spatial gradients of the surface brightness field.

We have built up an image processing pipeline that includes background removal, source selection, PSF reconstruction, shear measurement, etc.. It has been successfully applied to the CFHTlens data for a series of studies regarding lensing physics.

Raw Data from CFHT Selected Galaxy Images Cosmic Shear by Galaxy Clusters

Shear measurement pipeline

• Jun Zhang’s group: Shear measurement in Fourier space.
• Zuhui Fan, Liping Fu: pipeline based on forward-modeling
• Wentao Luo: Pipeline base on re-Gaussian method

Synergy with LSST, Euclid

CSS-OS: 15000 deg^2, Space mission, NUV, u, g, r, i, z, Y multi-band photometry, with average limit 25.2 (g=26.3 for point source), ~30 galaxies/arcmin^2, operation 2022 Euclid: 15000 deg^2, Space mission, VIS, Y, J ,H, similar galaxy density as CSS-OS, operation 2020 LSST: ~20000 deg^2, u,g,r,i,z bands, r=27.5, fast survey mode, operation 2022 Complementary to each other: Photo-z, shape measurement calibration ~2000 degree common area in the first 2 years ?

,

Summary I

• Weak lensing with CSS-OS may answer several most important

questions of the universe.

• Challenges in both theoretical and observational sides.
• Need imaging simulations as realistic as possible.
• Information about CSST designs and performance are critical to

construct the simulation.

• Welcome to join us.

Strong lensig with CSS-OS

• ~150000 galaxy scale strong

lens systems（Including ~1000 double lens system)

• ~1000 lensed QSO
• Hundreds of massive clusters

with many multiple images

• Accurate photo-z for both lens

and source.

Provide by Yiping Shu Hubble HFF

DM on small scales: Substructure detection

Vegetti et al. 2012

Identity of Dark matter

COCO simulations Bose+ 2016

Li et al. 2016 arxiv 1512.06507 CSS-OS

Self-interacting dark matter

David Harvey et al. 2015

DM on small scales: Center offset

Shu et al. 2016

Massey et al. 2015

Galaxy cluster Abell 3827

• ffset is 1.62+0.47 kpc ?

Density profile at the cluster center

Newman et al . 2012

−15 −10 −5 5 10 15 −10 −5 5 10

1 Mpc 5% shear

MS2137

Velocity dispersion à Dynamical mass Gravitational mass

2 2

(1 2 ) (1 2 ) Newtonian dynamical potential space curvature potential : :

i j ij

ds dt dx dx d F

• Y

= - + Y + F

In GR, F = Y

Slides by Wei Du

Testing gravity

Cosmological constraints from double source plane strong lensing (DSPL)

The observable:

In the ideal case of neglecting the effect of the intermediate source (source 1)

• n the background source (source 2):

The factor α depends on the lens mass model The factor α is cancelled

• ut, that alleviates the

dependence on the lens model to some extent.

Prediction: ~ 103 galaxy-

scale DSPL systems (based on Gavazzi et at. 2008 , about

• ne lens galaxy in ~ 40 - 80

could be a DSPL)

In SIS lens model, the stellar velocity dispersion is invariable with radius, that leads to

Slides by Yun Chen

Galaxy science (SL+SSP+Kinematics)

• Galaxy mass and structure
• Dark matter fraction
• Dark matter shape at center
• Evolution of Early type galaxies
• IMF variation of late type lenses
• ……

Galaxy lensing as a telescope

Shu et al. 2016

Abell 2744, magnification map by CATS team

Credits: LSST OpSim Group

Mining more than 10000 lenses from one billion objects

BY Nan Li

Lens search: ML

Yes/No

Yes Feature Extractor

Deep Learning Module Trained Deep Neural Network

Feature Extractor

Training Phase Prediction Phase

BY Nan Li

Completeness 80% Purity 80%

BY Nan Li

Completeness 90% Purity 90%

BY Nan Li

Source Light Cone Hubble UDF Luminosit y of Lens Galaxies Surface density Deflection Fields Lensed Images Halos from C- eagle

Ray Tracing

Final Lensed Images

Mock Observing Inputs Outputs

Simulation Plan

Strong + Weak lensing simulation

Thank you!