SgrA*M87*

13 March 2023, ISCO2023@OISTAbout myself•Mar.1996 Ph. D., Univ. of Tokyo•Apr.1996 Brain Science Institute, RIKEN•Apr.2001 Kyushu Institute of Technology•Feb.2003 The Institute of Statistical Mathematics•Information Geometry•Signal Processing•Information Theory•Brain Data Analysis•Fisheries Resource•Astronomical Data Analysis
3 March 2023, ISCO2023@OIST

3 March 2023, ISCO2023@OIST

13 Stakeholder Institutions
EHT Collaboration Meeting 2019@Hilo, Hawaii
EHT Collaboration Meeting 2016@Boston

3 March 2023, ISCO2023@OISTM87*
3 March 2023, ISCO2023@OIST

Credi: ESO, NAOJ

3 March 2023, ISCO2023@OISTM87*
Credit: H.Falcke/Radboud University/L.Krüger, EHT Collaboration

Credit: EHT Collaboration
3 March 2023, ISCO2023@OIST

3 March 2023, ISCO2023@OIST

Credit: From Kip Thorneʼs talk at Cardiff Univ., on youtube

3 March 2023, ISCO2023@OIST

Credit: Jordy Davelaar et al./Radboud University/BlackHoleCam

3 March 2023, ISCO2023@OIST

Credit: ESO/M. Kornmesser/L. Calçada
3 March 2023, ISCO2023@OIST

3 March 2023, ISCO2023@OIST

3 March 2023, ISCO2023@OISTEHT M87 Paper IV
3 March 2023, ISCO2023@OIST

3 March 2023, ISCO2023@OIST

3 March 2023, ISCO2023@OISTMaximum Entropy Method (MEM)

Sparse Modeling

Chael et al. 2016, Fish et al. 2014,Lu et al. 2014, 2016

Akiyama et al. 2017a, 2017b
Ikeda et al. 2016, Honma et al. 2014

Two New Imaging Libraries
eht-imaging (Chael+2016,2018) : https://github.com/achael/eht-imaging
SMILI (Akiyama+2017a,b) : https://github.com/astrosmili/smili
3 March 2023, ISCO2023@OISTEHT M87Paper IV, Fig.2
3 March 2023, ISCO2023@OISTTeam 4

Team 1Americas

East Asians

US & Chile

Korea, Japan & Taiwan(SAO, U. Arizona, U. Conception)
Leader: K. Bouman & A. Chael(ASIAA, KASI, NAOJ)
Leader: S. Koyama

Team 3

Team 2Global

Cross Atlantic

US, Japan, Netherland

US, Spain, Germany, Finland(MIT, NAOJ, Hiroshima U., Radboud U.)
Leader: K. Akiyama & S. Issaoun(Boston U, MPIfR, IAA, Aalto)
Leader: A. Marscher

3 March 2023, ISCO2023@OISTTeam 1

April 11
Americas

Team 4
April 11

East Asians

US & Chile

Korea, Japan & Taiwan(SAO, U. Arizona, U. Conception)
Leader: K. Bouman & A. Chael(ASIAA, KASI, NAOJ)
Leader: S. Koyama

Team 3Team 2Global

Cross Atlantic

US, Japan, Netherland

US, Spain, Germany, Finland(MIT, NAOJ, Hiroshima U., Radboud U.)
Leader: K. Akiyama & S. Issaoun

Leader: T. Krichibaum & A. Marscher(Boston U, MPIfR, IAA, Aalto)

3 March 2023, ISCO2023@OIST

3 March 2023, ISCO2023@OIST

3 March 2023, ISCO2023@OIST

3 March 2023, ISCO2023@OIST

Traditional Method
CLEAN
DIFMAP (Shepherd+97, 98, 03)

New Imaging Methods
Regularized Maximum Likelihood (RML)
eht-imaging (Chael+16, 18)

SMILI (Akiyama+17a, b)

ModelModel

Model

General Pixelated Images
Residual Calibration Gains

General Pixelated Images

General Pixelated Images
Residual Calibration Gains

Hyper Parameters

Hyper Parameters

Hyper Parameters

Sparse reconstruction

SparseSmoothMaximum Entropy

SparseSmoothCompact flux
Size

Compact flux
Size

Compact flux
Size

Credit: K. Akiyama, MIT
3 March 2023, ISCO2023@OISTEHT M87 Paper IV
3 March 2023, ISCO2023@OIST

Synthetic
Observations

Training Images

Synthetic
Data Sets

50 kParametersets

ReconstructedImages

3 Imaging
PipelinesFidelitycheck

Real M87 Data

OptimalParameters

Fiducial M87
Images3 Imaging
Pipelines+ chisq cut off
3 March 2023, ISCO2023@OISTEHT M87 Paper IV
3 March 2023, ISCO2023@OISTEHT M87 Paper IV
3 March 2023, ISCO2023@OISTEHT M87 Paper IV
3 March 2023, ISCO2023@OISTEHT Collaboration
3 March 2023, ISCO2023@OISTSgrA*
3 March 2023, ISCO2023@OISTEHT M87 Paper IV

EHT SgrA* Paper III
3 March 2023, ISCO2023@OIST

Credit: Hanae Ikeda
3 March 2023, ISCO2023@OIST

Credit: NASA/JPL-Caltech
Credit: ESO/S. Brunier
3 March 2023, ISCO2023@OISTTime variability

Simulation: EHT Collaboration et al. 2022, Sgr A* Paper V
Credit: Abhishek Joshi, University of Illinois at Urbana-Champaign

Scattering

Simulation: EHT Collaboration et al. 2022, Sgr A* Paper V
Slide by Kazu Akiyama@MIT

3 March 2023, ISCO2023@OISTOn-Sky Image=

Angular-broadened Image+

Refractive Substructure+=

Intrinsic Image

Scattering Kernel*
Refractive substructures appear as Gaussian noises in the Fourier domain
Johnson & Gwinn 2014, Johnson & Narayan 2016, Johnson et al. 2018, Psaltis et al. 2018

Credit: Kazu Akiyama@MIT

3 March 2023, ISCO2023@OIST

Traditional Method
CLEAN
DIFMAP (Shepherd+97, 98, 03)

New Imaging Methods
Regularized Maximum Likelihood (RML)
eht-imaging (Chael+16, 18)

Bayesian Imaging

SMILI (Akiyama+17a, b)

THEMIS (Broderick+20a, b)

ModelModel

Model

More Restrictive Model

General Pixelated Images
Residual Calibration Gains

General Pixelated Images

General Pixelated Images
Residual Calibration Gains

Low-resolution Pixel Images

Hyper Parameters

Hyper Parameters

Hyper Parameters

Image Assumptions

Image Assumptions

Image Assumptions

Variability Parameters
Field of View / GridOrientations

Scattering Parameters

Scattering Parameters

Scattering Parameters

Hyper parameters

Variability Parameters

Variability Parameters

Variability Parameters

Number of Image Pixels

Residual Calibration Gains
Scattering Parameters

Credit: K. Akiyama, MIT
3 March 2023, ISCO2023@OIST

Credit: K.Moriyma, Frankfurt
3 March 2023, ISCO2023@OIST

Synthetic
Observations

Training Images

Synthetic
Data Sets

100 kParametersets

ReconstructedImages

4 Imaging
Pipelines
Fidelity
checkReal SgrA* Data

OptimalParameters

Fiducial SgrA*Images

4 Imaging
Pipelines+ chisq cut off
3 March 2023, ISCO2023@OISTEHT SgrA* Paper III, 2022
3 March 2023, ISCO2023@OIST

Credit: K. Moriyama, and EHT Collaboration

3 March 2023, ISCO2023@OISTEHT Collaboration
3 March 2023, ISCO2023@OIST

Credit: NRAO, AUI, NSF
3 March 2023, ISCO2023@OIST

Credit: Thalia Traianou
3 March 2023, ISCO2023@OIST

3 March 2023, ISCO2023@OIST

3 March 2023, ISCO2023@OIST