Beyond the First Portrait of a Black Hole

Katie Bouman

**Concept Graph & Resume using Claude 3 Opus | Chat GPT4o | Llama 3:**

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A[Beyond the First

Portrait of a

Black Hole] --> B[Bauman: Caltech professor,

images the invisible. 1] A --> C[Black holes: Einsteins mysteries

from general relativity. 2] C --> D[Earth-sized telescope captured

black hole data. 3] D --> E[Image shows black holes

event horizon ring. 4] D --> F[Scientists, instruments, algorithms

enabled the picture. 5] F --> G[Bauman reconstructed, validated

black hole images. 6] A --> H[Imaging solves ill-posed

problem, sparse noisy data. 7] H --> I[Maximum likelihood finds

image fitting data. 8] H --> J[Multiple pipelines tackled

telescope data challenges. 9] J --> K[Synthetic data guided

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image uncertainty. 11] L --> M[DPI: neural net samples

posterior efficiently. 12] M --> N[DPI extends estimation,

quantifies uncertainty mathematically. 13] C --> O[M87 black hole: massive,

static, week-long imaging. 14] C --> P[Sagittarius A: smaller, closer,

faster orbiting gas. 15] P --> Q[Sagittarius A imaging needs

time variability methods. 16] Q --> R[Naive evolution imaging:

uninformative or oversmoothed. 17] Q --> S[Advection-diffusion balances flexibility,

interpretability, efficiency. 18] S --> T[Dimensionality reduction estimates

dynamics without forcing. 19] S --> U[Projection loss estimates

parameters from measurements. 20] A --> V[Better algorithms and instruments

crucial for science. 21] V --> W[Telescope network expanding

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around Sagittarius A*. 23] A --> Y[Astrophysics, AI, machine learning

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Portrait of a

Black Hole] --> B[Bauman: Caltech professor,

images the invisible. 1] A --> C[Black holes: Einsteins mysteries

from general relativity. 2] C --> D[Earth-sized telescope captured

black hole data. 3] D --> E[Image shows black holes

event horizon ring. 4] D --> F[Scientists, instruments, algorithms

enabled the picture. 5] F --> G[Bauman reconstructed, validated

black hole images. 6] A --> H[Imaging solves ill-posed

problem, sparse noisy data. 7] H --> I[Maximum likelihood finds

image fitting data. 8] H --> J[Multiple pipelines tackled

telescope data challenges. 9] J --> K[Synthetic data guided

hyperparameters, assessed methods. 10] H --> L[Posterior sampling explored

image uncertainty. 11] L --> M[DPI: neural net samples

posterior efficiently. 12] M --> N[DPI extends estimation,

quantifies uncertainty mathematically. 13] C --> O[M87 black hole: massive,

static, week-long imaging. 14] C --> P[Sagittarius A: smaller, closer,

faster orbiting gas. 15] P --> Q[Sagittarius A imaging needs

time variability methods. 16] Q --> R[Naive evolution imaging:

uninformative or oversmoothed. 17] Q --> S[Advection-diffusion balances flexibility,

interpretability, efficiency. 18] S --> T[Dimensionality reduction estimates

dynamics without forcing. 19] S --> U[Projection loss estimates

parameters from measurements. 20] A --> V[Better algorithms and instruments

crucial for science. 21] V --> W[Telescope network expanding

worldwide for quality. 22] W --> X[Future: gas properties

around Sagittarius A*. 23] A --> Y[Astrophysics, AI, machine learning

collaboration advances imaging. 24] class A,B bauman class C,D,E,O,P blackholes class F,G,H,I,J,K,L,M,N imaging class Q,R,S,T,U methods class V,W,X,Y future

**Resume: **

**1.-** Katie Bauman is an assistant professor at Caltech working on imaging the invisible, including black holes.

**2.-** Black holes are mysterious phenomena predicted by Einstein's theory of general relativity over 100 years ago.

**3.-** In April 2017, an Earth-sized telescope captured data to create the first picture of a black hole.

**4.-** The ring of light in the image is a signature of the black hole's event horizon.

**5.-** Taking the picture required an international collaboration of scientists building new instruments and algorithms.

**6.-** Bauman's group focused on reconstructing and validating the black hole images.

**7.-** Imaging black holes involves solving an ill-posed inverse problem with sparse and noisy data.

**8.-** Regularized maximum likelihood estimation was used to find a likely image that fits the data well.

**9.-** Multiple imaging pipelines were developed to handle the challenges in the Event Horizon Telescope data.

**10.-** Synthetic data was used to select hyperparameter modeling choices and assess the methods' performance.

**11.-** Uncertainty in the reconstructed images was explored by sampling from the posterior distribution.

**12.-** Deep Probabilistic Imaging (DPI) uses a neural network to efficiently sample from the posterior distribution.

**13.-** DPI extends regularized maximum likelihood estimation and provides a solid mathematical foundation for uncertainty quantification.

**14.-** The black hole in galaxy M87 is massive and static, allowing for imaging over the course of a week.

**15.-** Sagittarius A*, the black hole at the center of the Milky Way, is much smaller but closer to Earth.

**16.-** Gas orbits Sagittarius A* every 4-30 minutes, requiring methods to capture its time variability.

**17.-** Naive approaches to imaging Sagittarius A*'s evolution result in uninformative or overly smooth reconstructions.

**18.-** Modeling the persistent evolution of black hole sources as a stochastic advection-diffusion process provides a middle ground.

**19.-** The model balances flexibility and interpretability while being computationally efficient.

**20.-** Dimensionality reduction allows the estimation of the model's dynamics parameters without knowing the input forcing term.

**21.-** The projection residual loss function is used to estimate the underlying true parameters from sparse measurements.

**22.-** Improving both the algorithms and the instrument collecting the data is essential for extracting more science.

**23.-** The Event Horizon Telescope network is being expanded with more telescopes worldwide to improve data quality.

**24.-** Future arrays and improved algorithms may enable recovering the properties of gas circling Sagittarius A* over a night.

**25.-** Collaboration between astrophysics and computer vision, AI, and machine learning is crucial for advancing black hole imaging.

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