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]
 Presentation video [!yt]


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]


Yang Chen (Michigan) & Max Bonamente (UAH) Date TBD Noon EST/11am CST Zoom 
 cstatapalooza


Xiangyu Zhang (Minnesota) Feb 21, 2024 11am CST Zoom 
 Smooth tests for line emission detection under high background in highresolution Xray spectra


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.





