Presentations 

Cecilia Garraffo (CfA) Sep 06 Noon EDT SC706 
 AstroAI: Integrating Artificial Intelligence into Astrophysics
 Abstract: AstroAI, launched at the Center for Astrophysics  Harvard & Smithsonian (CfA) in November 2022, is a novel initiative focused on developing machine learning (ML) and artificial intelligence (AI) algorithms to further astrophysical research. Its inception was driven by the recognized need, both within the CfA and the broader scientific community, for dependable and interpretable models in astrophysics research. At its core, AstroAI aims to create AI and ML models designed for astrophysical discovery, emphasizing a multidisciplinary approach and collaboration among a diverse group of researchers. This talk will outline the progress and growth of AstroAI since its beginning and highlight some of the key projects undertaken by our team, and showcase a few of our projects and their transformative potential in astrophysical research.
 Presentation Video [!yt]


Mengyang Gu (UC Santa Barbara) Sep 13 Noon EDT SC706 
 Calibration of imperfect geophysical models by multiple satellite interferograms with measurement bias
 Abstract:
Model calibration consists of using experimental or field data to estimate the unknown parameters of a mathematical model. The presence of model discrepancy and measurement bias in the data complicates this task. Satellite interferograms, for instance, are widely used for calibrating geophysical models in geological hazard quantification. In this work, we used satellite interferograms to relate ground deformation observations to the properties of the magma chamber at Kilauea Volcano in Hawai`i. We derived closedform marginal likelihoods and implemented posterior sampling procedures that simultaneously estimate the model discrepancy of physical models, and the measurement bias from the atmospheric error in satellite interferograms. We found that model calibration by aggregating multiple interferograms and downsampling the pixels in the interferograms can reduce the computation complexity compared to calibration approaches based on multiple data sets. The conditions that lead to no loss of information from data aggregation and downsampling are studied. Simulation illustrates that both discrepancy and measurement bias can be estimated, and real applications demonstrate that modeling both effects helps obtain a reliable estimation of a physical model's unobserved parameters and enhance its predictive accuracy. We implement the computational tools in the RobustCalibration package available on CRAN.
 References:
Gu, M., & Wang, L. (2018). Scaled Gaussian stochastic process for computer model calibration and prediction. SIAM/ASA Journal on Uncertainty Quantification, 6(4), 15551583
Gu, M., Xie, F., & Wang, L. (2022). A Theoretical Framework of the Scaled Gaussian Stochastic Process in Prediction and Calibration. SIAM/ASA Journal on Uncertainty Quantification, 10(4), 14351460.
Gu, M., Anderson, K., & McPhillips, E. (2023). Calibration of imperfect geophysical models by multiple satellite interferograms with measurement bias. Technometrics, in press, arxiv:1810.11664 [!arXiv]
Gu, M., He, Y., Liu, X., & Luo Y. (2023). Ab initio uncertainty quantification in scattering analysis of microscopy arXiv:2309.02468 [!arXiv]
 Presentation slides [.pdf]
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Ashley Villar & Rafael MartinezGalarza (CfA) Oct 04, 2023 Noon EDT SC706 
 Project: A Variational Autoencoderinspired Mixture of Poissons to classify Xray photon lists
 In the lowcount limit, astrophysical phenomena follow Poisson distributions across a distribution of energies and time. Learning meaningful representations of these events remains a challenging endeavor; however, such representations can aid in a number of downstream scientific tasks: classification, anomaly detection and potentially inference. Here, we present a project pitch to build a probabilistic (Poissonbased) neural network (inspired by a variational autoencoder) to find meaningful representations of astronomical light curves.


Aneta Siemiginowska (CfA) Oct 11, 2023 Noon EDT SC706 
 Why timedelays?
 Timedelays are often encountered in astronomical measurements. They provide otherwise unresolved intrinsic scales of a variable source or, in the case of gravitational lensing, constraints on the cosmological parameters. I will present an astronomer's view on the timedelay applications, discuss our recent model for timedelays due to gravitational lensing, future directions, and open projects.
 Presentation slides [.pdf]
 Presentation video [!yt]
 See also: Tak et al. 2015, AoAS 11, 1309; Meyer et al. 2023, ApJ 950, 37


Pavlos Protopapas (SEAS) Oct 18, 2023 Noon EDT SC706 
 ResidualBased Error Bound for PhysicsInformed Neural Networks
 Abstract: Neural networks are universal approximators and are studied for their use in solving differential equations. However, a major criticism is the lack of error bounds for obtained solutions. In this talk I will describe a technique to rigorously evaluate the error bound of PhysicsInformed Neural Networks (PINNs) on most linear ordinary differential equations (ODEs), certain nonlinear ODEs, and firstorder linear partial differential equations (PDEs).
The error bound is based purely on equation structure and residual information and does not depend on assumptions of how well the networks are trained. We propose algorithms that bound the error efficiently.
 Reference:
Liu et al. 2023, arXiv:2306.03786 [!arXiv]
 Presentation video [!yt]


Herman Marshall (MIT), Subramania Athray (UAlabama), & Vinay Kashyap (CfA) Nov 8 Noon EST SciCen 706 
 Deconvolving dispersed gratings spectra from extended sources
 Abstract: We will present the mostly unsolved problem of deconvolving highresolution grating dispersed spectra of extended sources. We will show examples of the data from Chandra, and some examples of how solar physicists are modeling data from the dispersed Sun in the high counts regime when there are strong line features in the spectrum. Can this be extended to smoother spectra in the Poisson regime?
 See also: Winebarger et al. 2019, ApJ 882, 12, Unfolding Overlapped Slitless Imaging Spectrometer Data for Extended Sources [!ads]
 Slides:
Herman Marshall [.key]
Vinay Kashyap [.key]
Subramania Athiray [.pptx]


Adel Daoud (Linkoping/Chalmers) 24 Jan 2024 Noon EST SC706 
 Are You Devising an Observatory of Extraterrestrial Life? Lessons learned from Observatory of PovertyMeasuring Living Conditions on Planet Earth with AI and Earth Observations
 Abstract:
The question, "Is there other especially intelligent life in the Universe," is one of the most intriguing questions in the sciences and beyond. If there is indeed life on other planets and the only means of observing it is through highresolution satellite images, a followup question would be, "How may we use those images to measure extraterrestrial activities on the surface of their planets?" This talk gives some pointers to addressing that followup question by showing how we, at the AI and Global Development Lab, are measuring health and living conditions on Earth by using satellite images and deep learning. The Lab is currently measuring the historical and geographical development trajectories from satellite images from the 1990s to the present, focusing on the African continent. These measurements are our data product, capturing living conditions at unprecedented temporal and spatial granularity. This talk will discuss key scientific challenges and research prospects.
 Presentation video [!yt]


AnaSofia Uzsoy (Harvard) 7 Feb 2024 Noon EST SC706 
 Variational Inference for Acceleration of SN Ia Photometric Distance Estimation with BayeSN
 Abstract:
We use variational inference (VI) to fit the light curves of Type Ia supernovae (SN Ia) using the BayeSN hierarchical Bayesian model for SN Ia spectral energy distributions. We fit both simulated light curves and data from the Foundation Supernova Survey with two different forms of surrogate posterior  a multivariate normal and a custom multivariate zerolowertruncated normal distribution  and compare them with baseline MCMC fits and the Laplace Approximation. To evaluate the accuracy of our variational approximation, we calculate the paretosmoothed importance sampling (PSIS) diagnostic, and perform variational simulationbased calibration (VSBC). The VI approximation achieves similar results to MCMC but with significantly reduced runtime. Overall, we show that VI is a promising method for scalable parameter inference as we enter the era of "big data".
 Presentation slides [.pptx]
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Axel Donath (CfA) 14 Feb 2024 Noon EST SC706 
 Joint Likelihood Deconvolution of Astronomical Images in the Presence of Poisson Noise
 Abstract:
I will present a new method for Joint Likelihood Deconvolution (Jolideco) of astronomical images in the presence of Poisson noise. The method reconstructs a single flux image from a set of observations of the same sky region by optimizing the a posteriori joint Poisson likelihood of all observations under a patch based image prior. Simulations demonstrate that both the combination of multiple observations as well as the patch based prior lead to a much improved reconstruction quality, compared to alternative methods like the RichardsonLucy method. I will showcase some results using example data from the Chandra observatory and conclude with an overview of open questions, most importantly the question of uncertainties on reconstructed flux images.
 Presentation slides [.pdf]
 Presentation video [!yt]


Xiangyu Zhang (Minnesota) Feb 21, 2024 11am CST Zoom 
 On smooth tests of goodnessoffit for astrophysical searches under high background
 Abstract:
Smooth tests were first introduced by Neyman (1937) as a comprehensive approach to the goodnessoffit (GOF). Compared to classical GOF tests, such as KolmogorovSmirnov or Cramer von Mises, smooth tests use an alternative model that incorporates the null through a series of orthonormal basis functions (e.g., Shifted Legendre Polynomial or Cosine bases). As a result, they concentrate their power on a limited number of directions. A particularly appealing feature of smooth tests is that, when the null model is rejected, they naturally provide a correction for it. This aspect will be illustrated in the context of detecting line emissions under a high background. New methodological developments on the construction of distributionfree smooth tests that are unaffected by postselection inference problems will also be discussed.
 Presentation slides [.pdf]
 Presentation Video [!yt]


Yang Chen (Michigan) & Max Bonamente (UAH) Feb 28, 2024 Noon EST/11am CST Zoom 
 Cstatapalooza

 Yang Chen: Comparison of Goodnessoffit Assessment Methods with C statistics in Astronomy
 Abstract:
In astrophysics, the C statistic, which is a likelihood ratio statistic, has been widely adopted for model fitting and goodnessoffit assessments for Poissoncount data with heterogeneous rates. It is well known that when the sample size is very large, the C statistics enjoy convenient theoretical properties, especially in the largemean limit. However, in many astronomy and highenergy physics applications, the observations are very sparse, making the theoretical properties of C statistics questionable. We comprehensively study the properties of C statistics and evaluate various algorithms for goodnessoffit assessment using C statistics, emphasizing lowcount scenarios.
 Presentation slides [.pdf]

 Max Bonamente: Systematic errors and Poisson regression
 Abstract: A new statistical method is proposed that includes systematic errors in the analysis of Poisson data, especially for the purpose of regression analysis and subsequent hypothesis testing. The method is based on the introduction of an intrinsic model variance, which is enforced after the usual maximumlikelihood regression is performed. With this method, the usual goodnessoffit statistic  the Poisson deviance also known as the Cash statistic  becomes distributed like a newlyintroduced overdispersed chisquared distribution under the null hypothesis, at least in the largemean limit. This new distribution defaults to the usual chisquared when systematic errors are negligible, and continues to be normallydistributed for extensive data. The method offers also the opportunity to estimate systematic errors, if they cannot be estimated a priori. It is hoped that this model, which is simple to use for most applications, offers an answer to the quest for a simple and statisticallymotivated means of handling systematic errors in count data.
 Presentation slides [.pdf]

 meeting chat [.txt]
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Ann Lee (CMU) Apr 10, 2024 Noon EDT SciCen 706 



Jason Siyang Li (Imperial) Apr 24, 2024 Noon EDT SC706 
 Evidence computation methods in Gravitational Wave data analysis
 Abstract:
Evidences are crucial in Bayesian model selection. The calculation of evidences are often
analytically intractable. Apart from the wellknown nested sampling, there are several
computation methods of Bayesian model evidence. This presentation focuses on the
evidence computation methods that have gained interests in the field of gravitational wave
data analysis. Namely, thermodynamic integration (TI) and stepping stone (SS) are widely
accepted and applied for a while, which are special cases in path and bridge samplings. A
new method, Fourier integral (FI), is a fast alternative to TI and SS, based on Chib (1995). In
essence, FI estimates the posterior density value at a single point in the parameter space
using a generalization of kernel density estimator.
The last part of the presentation will focus on my recent work in Bayesian model evidences,
including nesting evidence estimation in posterior samplers (to reduce parameter space
dimension) in hierarchical models, and potentially, using machine learning for evidence
computation.
 See also:
Gelman, A. and Meng, X.L. (1998). Simulating normalizing constants: From importance sampling to bridge sampling to path sampling. Statistical science, pages 163185
MaturanaRussel, P., Meyer, R., Veitch, J., and Christensen, N. (2019). Steppingstone sampling algorithm for calculating the evidence of gravitational wave models. Physical Review D, 99(8):084006.
Chib, S. (1995). Marginal likelihood from the gibbs output. Journal of the american statistical association, 90(432):13131321.
Rotiroti, F. and Walker, S. G. (2022). Computing marginal likelihoods via the fourier integral theorem and pointwise estimation of posterior densities. Statistics and Computing, 32(5):118
Boileau, G., Christensen, N., Gowling, C., Hindmarsh, M., and Meyer, R. (2023). Prospects for lisa to detect a gravitationalwave background from first order phase Transitions.





