The End Of Knowledge - Vault 5/63 - CVPR - 2021 - Beyond the First Portrait of a Black Hole

graph LR classDef bauman fill:#f9d4d4, font-weight:bold, font-size:14px classDef blackholes fill:#d4f9d4, font-weight:bold, font-size:14px classDef imaging fill:#d4d4f9, font-weight:bold, font-size:14px classDef methods fill:#f9f9d4, font-weight:bold, font-size:14px classDef future fill:#f9d4f9, font-weight:bold, font-size:14px 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
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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