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DOROTA BAYER S W I N B U R N E U N I V E R S I T Y O F T E C H N - - PowerPoint PPT Presentation

Oct 18, 2023 254 likes •726 views

PROBING THE NATURE OF DARK MATTER WITH GALAXY-GALAXY STRONG GRAVITATIONAL LENSING DOROTA BAYER S W I N B U R N E U N I V E R S I T Y O F T E C H N O L O G Y A S T R O 3 D M E L B O U R N E 1 4 O C T O B E R 2 0 2 0 INTRODUCTION What

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DOROTA BAYER

S W I N B U R N E U N I V E R S I T Y O F T E C H N O L O G Y A S T R O 3 D

PROBING THE NATURE OF DARK MATTER WITH GALAXY-GALAXY STRONG GRAVITATIONAL LENSING

M E L B O U R N E 1 4 O C T O B E R 2 0 2 0

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INTRODUCTION

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SLIDE 3

Image credits: Bertone & Tait (2018), Ben Moore (University of Zurich)

What is the nature of dark matter?

MACHOs, particle dark matter

  • r modified gravity?

phenomenological models based on free-streaming length

COLD ? WARM ? HOT ?

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Sub-galactic mass structure as a probe of DM

Image credit: Lovell et al. (2014)

HOW CLUMPY/SMOOTH IS THE MASS DISTRIBUTION IN GALACTIC HALOES ?

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SLIDE 5

Image credit: Hsueh et al. (2020)

Sub-halo mass function

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SLIDE 6

Image credit: Hsueh et al. (2020)

Sub-halo mass function

LOWER LIMIT 5.3 keV

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SLIDE 7

Galaxy-galaxy strong gravitational lensing

Image credit: ALMA (ESO/NRAO/NAOJ), L. Calçada (ESO), Y. Hezaveh et al.

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SLIDE 8

Image credit: ALMA (ESO/NRAO/NAOJ), L. Calçada (ESO), Y. Hezaveh et al.

Substructure lensing

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SLIDE 9

Gravitational imaging of galactic substructure

Substructure in Lens Galaxy Perturbations to Smooth Lensing Potential Surface-Brightness Anomalies in Lensed Images

Koopmans (2005), Vegetti & Koopmans (2009)

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SLIDE 10

Substructure in Lens Galaxy Perturbations to Smooth Lensing Potential Surface-Brightness Anomalies in Lensed Images

Koopmans (2005), Vegetti & Koopmans (2009)

Gravitational imaging of galactic substructure

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SLIDE 11

Image credits: Vegetti et al. (2010b, 2012)

sub-halo mass = 1.9 * 10^8 redshift = 0.881 sub-halo mass = 2.75 * 10^10 redshift = 0.422

Gravitational imaging of galactic substructure

M ☉ M ☉

SDSS J120602.09+514229.5 SDSS J0946+1006

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SLIDE 12

Image credit: Hsueh et al. (2020)

Sub-halo mass function

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NOVEL STATISTICAL APPROACH

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Our approach

Density Fluctuations in the Lens Galaxy Perturbations to Smooth Lensing Potential Surface-Brightness Anomalies in the Lensed Images

Chatterjee & Koopmans (2018), Bayer et al. (submitted to MNRAS)

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SLIDE 15

Our approach

Density Fluctuations in the Lens Galaxy GRF Perturbations to Smooth Lensing Potential Surface-Brightness Anomalies in the Lensed Images

Chatterjee & Koopmans (2018), Bayer et al. (submitted to MNRAS)

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Our approach

Density Fluctuations in the Lens Galaxy GRF Perturbations to Smooth Lensing Potential Surface-Brightness Anomalies in the Lensed Images

POWER SPECTRUM

Chatterjee & Koopmans (2018), Bayer et al. (submitted to MNRAS)

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Our approach

Density Fluctuations in the Lens Galaxy GRF Perturbations to Smooth Lensing Potential Surface-Brightness Anomalies in the Lensed Images

POWER SPECTRUM

Chatterjee & Koopmans (2018), Bayer et al. (submitted to MNRAS)

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Analysis overview

— Observed surface-brightness anomalies — GRF potential perturbations & mock power spectra — Statistical comparison

Upper-limit constraints on sub-galactic mass structure (1-10 kpc scales)

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SURFACE-BRIGHTNESS ANOMALIES

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SLACS lenses

Image credit: A. Bolton, SLACS team

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SLACS lens systems

Image credit: A. Bolton, SLACS team

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Step 1: Subtraction of lens-galaxy light

SDSS J0252+0039 HST/WFC3/F390W MASK

Bayer et al. (submitted to MNRAS)

LENS-LIGHT MODEL (GALFIT) GALAXY-SUBTRACTED

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Step 2: Smooth lens modeling

SMOOTH LENS MODEL: POWER-LAW ELLIPTICAL MASS DISTRIBUTION

  • Einstein radius
  • center of mass
  • position angle
  • axis ratio
  • mass-density slope
  • external shear strength & position angle

Adaptive, grid-based, Bayesian lens modeling technique by Vegetti & Koopmans (2009)

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Step 3: Residuals

SMOOTH LENS MODEL: POWER-LAW ELLIPTICAL MASS DISTRIBUTION

  • Einstein radius
  • center of mass
  • position angle
  • axis ratio
  • mass-density slope
  • external shear strength & position angle

Adaptive, grid-based, Bayesian lens modeling technique by Vegetti & Koopmans (2009)

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Step 4: Residual power spectrum

SMOOTH LENS MODEL: POWER-LAW ELLIPTICAL MASS DISTRIBUTION

  • Einstein radius
  • center of mass
  • position angle
  • axis ratio
  • mass-density slope
  • external shear strength & position angle

Adaptive, grid-based, Bayesian lens modeling technique by Vegetti & Koopmans (2009)

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Step 4: Surface-brightness anomalies

Adaptive, grid-based, Bayesian lens modeling technique by Vegetti & Koopmans (2009)

5 KPC 0.5 KPC NOISE ANOMALIES RESIDUALS

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UNDERLYING MASS STRUCTURE

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GRF potential perturbations

Bayer et al. (submitted to MNRAS)

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Best-fitting smooth lens model

Image credit: ALMA (ESO/NRAO/NAOJ), L. Calçada (ESO), Y. Hezaveh et al.

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Effect of GRF potential perturbations

Image credit: ALMA (ESO/NRAO/NAOJ), L. Calçada (ESO), Y. Hezaveh et al.

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Mock perturbed lens systems

Bayer et al. (submitted to MNRAS)

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Template of mock power spectra

Bayer et al. (submitted to MNRAS)

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Template of mock power spectra

LENS POTENTIAL PERTURBATIONS

Bayer et al. (submitted to MNRAS)

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Template of mock power spectra

SB ANOMALIES IN LENSED IMAGES LENS POTENTIAL PERTURBATIONS

Bayer et al. (submitted to MNRAS)

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Comparison with data

SB ANOMALIES IN LENSED IMAGES LENS POTENTIAL PERTURBATIONS

Bayer et al. (submitted to MNRAS)

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Exclusion probability

Bayer et al. (submitted to MNRAS)

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DOUBLE RING LENS SYSTEM

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Constraints from the Double Ring system

Bayer et al. (in prep)

excluded

SDSS J0946+1006 HST/ACS/814W

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Constraints from the Double Ring system

Bayer et al. (in prep)

EXCLUDED

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Constraints from the Double Ring system

Bayer et al. (in prep)

EXCLUDED

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Constraints from the Double Ring system

Bayer et al. (in prep)

EXCLUDED

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Constraints from the Double Ring system

Bayer et al. (in prep)

EXCLUDED

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Constraints from the Double Ring system

Bayer et al. (in prep)

UPPER LIMIT

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Constraints from the Double Ring system

Bayer et al. (in prep)

ALLOWED

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Collaborators

Leon Koopmans Saikat Chatterjee Simona Vegetti John McKean Chris Fassnacht Tomasso Treu

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Summary & Future work

Summary

— Novel approach to modelling small-scale structure in galactic haloes — Gaussian random fields density fluctuations — Power spectrum of surface-brightness anomalies — First constraints on sub-galactic mass power spectrum (1-10 kpc)

Future work

— Larger sample — Machine-learning approach — Comparison to hydrodynamical simulations

  • alternative dark matter models
  • various galaxy formation scenarios

Bayer et al. (submitted to MNRAS)