alpha-Deep Probabilistic Inference (alpha-DPI): efficient uncertainty quantification from exoplanet astrometry to black hole feature extraction

Sun, He; Bouman, Katherine L.; Tiede, Paul; Wang, Jason J.; Blunt, Sarah; Mawet, Dimitri

doi:10.48550/arxiv.2201.08506

Cited by 4 publications

(4 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The third software we use for snapshot geometric modeling is the Python code Deep Probabilistic Imaging/Inference (DPI/α-DPI;Sun & Bouman 2021;Sun et al 2022). DPI approximates the posterior over all model parameters by fitting a normalizing flow neural network(Rezende & Mohamed 2015) to the data using a Rényi α-divergence variational inference technique(Li & Turner 2016).…”

mentioning

confidence: 99%

First Sagittarius A* Event Horizon Telescope Results. IV. Variability, Morphology, and Black Hole Mass

Collaboration¹,

Akiyama

Alberdi

et al. 2022

ApJL

Self Cite

187

View full text Add to dashboard Cite

In this paper we quantify the temporal variability and image morphology of the horizon-scale emission from Sgr A*, as observed by the EHT in 2017 April at a wavelength of 1.3 mm. We find that the Sgr A* data exhibit variability that exceeds what can be explained by the uncertainties in the data or by the effects of interstellar scattering. The magnitude of this variability can be a substantial fraction of the correlated flux density, reaching ∼100% on some baselines. Through an exploration of simple geometric source models, we demonstrate that ring-like morphologies provide better fits to the Sgr A* data than do other morphologies with comparable complexity. We develop two strategies for fitting static geometric ring models to the time-variable Sgr A* data; one strategy fits models to short segments of data over which the source is static and averages these independent fits, while the other fits models to the full data set using a parametric model for the structural variability power spectrum around the average source structure. Both geometric modeling and image-domain feature extraction techniques determine the ring diameter to be 51.8 ± 2.3 μas (68% credible intervals), with the ring thickness constrained to have an FWHM between ∼30% and 50% of the ring diameter. To bring the diameter measurements to a common physical scale, we calibrate them using synthetic data generated from GRMHD simulations. This calibration constrains the angular size of the gravitational radius to be 4.8 − 0.7 + 1.4 μas, which we combine with an independent distance measurement from maser parallaxes to determine the mass of Sgr A* to be 4.0 − 0.6 + 1.1 × 10 6 M ⊙.

show abstract

mentioning

confidence: 99%

First Sagittarius A* Event Horizon Telescope Results. IV. Variability, Morphology, and Black Hole Mass

Collaboration¹,

Akiyama

Alberdi

et al. 2022

ApJL

Self Cite

187

View full text Add to dashboard Cite

show abstract

“…This formulation is the same as the likelihood formulation currently employed by many retrieval frameworks. Instead of optimising the ELBO objective in its actual formulation, we adopted the weight annealing approach from Sun et al (2022) to optimise a modified ELBO objective, i.e.…”

Section: Elbo Formulationmentioning

confidence: 99%

To Sample or Not To Sample: Retrieving Exoplanetary Spectra with Variational Inference and Normalising Flows

Yip¹,

Changeat²,

Al-Refaie³

et al. 2022

Preprint

View full text Add to dashboard Cite

Current endeavours in exoplanet characterisation rely on atmospheric retrieval to quantify crucial physical properties of remote exoplanets from observations. However, the scalability and efficiency of the technique are under strain with increasing spectroscopic resolution and forward model complexity. The situation becomes more acute with the recent launch of the James Webb Space Telescope and other upcoming missions. Recent advances in Machine Learning provide optimisation-based Variational Inference as an alternative approach to perform approximate Bayesian Posterior Inference. In this investigation we combined Normalising Flow-based neural network with our newly developed differentiable forward model, Diff-τ , to perform Bayesian Inference in the context of atmospheric retrieval. Using examples from real and simulated spectroscopic data, we demonstrated the superiority of our proposed framework: 1) Training Our neural network only requires a single observation; 2) It produces high-fidelity posterior distributions similar to sampling-based retrieval and; 3) It requires 75% less forward model computation to converge. 4.) We performed, for the first time, Bayesian model selection on our trained neural network. Our proposed framework contribute towards the latest development of a neural-powered atmospheric retrieval. Its flexibility and speed hold the potential to complement sampling-based approaches in large and complex data sets in the future.

show abstract

“…The Julia-based [149] Comrade [150] and Python-based Deep Probabilistic Imaging/Inference (DPI/α-DPI, [151,152]) frameworks are versatile geometric model fitting tools, which are used by the EHT [153].…”

Section: Comrade and Dpimentioning

confidence: 99%

Software and Techniques for VLBI Data Processing and Analysis

2022

View full text Add to dashboard Cite

Very-long-baseline interferometry (VLBI) is a challenging observational technique, which requires in-depth knowledge about radio telescope instrumentation, interferometry, and the handling of noisy data. The reduction in raw data is mostly left to the scientists and demands the use of complex algorithms implemented in comprehensive software packages. The correct application of these algorithms necessitates a good understanding of the underlying techniques and physics that are at play. The verification of the processed data produced by the algorithms demands a thorough understanding of the underlying interferometric VLBI measurements. This review describes the latest techniques and algorithms that scientists should know about when analyzing VLBI data.

show abstract

alpha-Deep Probabilistic Inference (alpha-DPI): efficient uncertainty quantification from exoplanet astrometry to black hole feature extraction

Cited by 4 publications

References 21 publications

First Sagittarius A* Event Horizon Telescope Results. IV. Variability, Morphology, and Black Hole Mass

First Sagittarius A* Event Horizon Telescope Results. IV. Variability, Morphology, and Black Hole Mass

To Sample or Not To Sample: Retrieving Exoplanetary Spectra with Variational Inference and Normalising Flows

Software and Techniques for VLBI Data Processing and Analysis

Contact Info

Product

Resources

About