AstroStat Talks 2023-2024
Last Updated: 20231108

International CHASC AstroStatistics Centre

Topics in Astrostatistics

AY 2023-2024


Schedule Wednesdays Noon - 1:30pm Eastern Time
Location SC-706 + Zoom

Cecilia Garraffo (CfA)
Sep 06
Noon EDT
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
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 closed-form 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.
Gu, M., & Wang, L. (2018). Scaled Gaussian stochastic process for computer model calibration and prediction. SIAM/ASA Journal on Uncertainty Quantification, 6(4), 1555-1583
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), 1435-1460.
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 Martinez-Galarza (CfA)
Oct 04, 2023
Noon EDT
Project: A Variational Autoencoder-inspired Mixture of Poissons to classify X-ray photon lists
In the low-count 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 (Poisson-based) neural network (inspired by a variational autoencoder) to find meaningful representations of astronomical light curves.
Aneta Siemiginowska (CfA)
Oct 11, 2023
Noon EDT
Why time-delays?
Time-delays 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 time-delay applications, discuss our recent model for time-delays 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
Residual-Based Error Bound for Physics-Informed 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 Physics-Informed Neural Networks (PINNs) on most linear ordinary differential equations (ODEs), certain nonlinear ODEs, and first-order 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.
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 high-resolution 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]
Herman Marshall [.key]
Vinay Kashyap [.key]
Subramania Athiray [.pptx]
Yang Chen (Michigan) & Max Bonamente (UAH)
Date TBD
Noon EST/11am CST
Xiangyu Zhang (Minnesota)
Feb 21, 2024
11am CST
Smooth tests for line emission detection under high background in high-resolution X-ray spectra
Jason Siyang Li (Imperial)
Apr 24, 2024
Noon EDT
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 well-known 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 163-185
Maturana-Russel, P., Meyer, R., Veitch, J., and Christensen, N. (2019). Stepping-stone 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):1313-1321.
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):1-18
Boileau, G., Christensen, N., Gowling, C., Hindmarsh, M., and Meyer, R. (2023). Prospects for lisa to detect a gravitational-wave background from first order phase Transitions.

Fall/Winter 2004-2005
Siemiginowska, A. / Connors, A. / Kashyap, V. / Zezas, A. / Devor, J. / Drake, J. / Kolaczyk, E. / Izem, R. / Kang, H. / Yu, Y. / van Dyk, D.
Fall/Winter 2005-2006
van Dyk, D. / Ratner, M. / Jin, J. / Park, T. / CCW / Zezas, A. / Hong, J. / Siemiginowska, A. & Kashyap, V. / Meng, X.-L.
Fall/Winter 2006-2007
Lee, H. / Connors, A. / Protopapas, P. / McDowell, J., / Izem, R. / Blondin, S. / Lee, H. / Zezas, A., & Lee, H. / Liu, J.C. / van Dyk, D. / Rice, J.
Fall/Winter 2007-2008
Connors, A., & Protopapas, P. / Steiner, J. / Baines, P. / Zezas, A. / Aldcroft, T.
Fall/Winter 2008-2009
H. Lee / A. Connors, B. Kelly, & P. Protopapas / P. Baines / A. Blocker / J. Hong / H. Chernoff / Z. Li / L. Zhu (Feb) / A. Connors (Pt.1) / A. Connors (Pt.2) / L. Zhu (Mar) / E. Kolaczyk / V. Liublinska / N. Stein
Fall/Winter 2009-2010
A.Connors / B.Kelly / N.Stein, P.Baines / D.Stenning / J. Xu / A.Blocker / P.Baines, Y.Yu / V.Liublinska, J.Xu, J.Liu / Meng X.L., et al. / A. Blocker, et al. / A. Siemiginowska / D. Richard / A. Blocker / Xie X. / Xu J. / V. Liublinska / L. Jing
AcadYr 2010-2011
Astrostat Haiku / P. Protopapas / A. Zezas & V. Kashyap / A. Siemiginowska / K. Mandel / N. Stein / A. Mahabal / Hong J.S. / D. Stenning / A. Diaferio / Xu J. / B. Kelly / P. Baines & I. Udaltsova / M. Weber
AcadYr 2011-2012
A. Blocker / Astro for Stat / B. Kelly / R. D'Abrusco / E. Turner / Xu J. / T. Loredo / A. Blocker / P. Baines / A. Zezas et al. / Min S. & Xu J. / O. Papaspiliopoulos / Wang L. / T. Laskar
AcadYr 2012-2013
N. Stein / A. Siemiginowska / D. Cervone / R. Dawson / P. Protopapas / K. Reeves / Xu J. / J. Scargle / Min S. / Wang L. & D. Jones / J. Steiner / B. Kelly / K. McKeough
AcadYr 2013-2014
Meng X.-L. / Meng X.-L., K. Mandel / A. Siemiginowska / S. Vrtilek & L. Bornn / Lazhi W. / D. Jones / R. Wong / Xu J. / van Dyk D. / Feigelson E. / Gopalan G. / Min S. / Smith R. / Zezas A. / van Dyk D. / Hyungsuk T. / Czerny, B. / Jones D. / Liu K. / Zezas A.
AcadYr 2014-2015
Vegetabile, B. & Aldcroft, T., / H. Jae Sub / Siemiginowska, A. & Kashyap, V. / Pankratius, V. / Tak, H. / Brenneman, L. / Johnson, J. / Lynch, R.C. / Fan, M.J. / Meng, X.-L. / Gopalan, G. / Jiao, X. / Si, S. / Udaltsova, I. & Zezas, A. / Wang, L. / Tak, H. / Eadie, G. / Czekala, I. / Stenning, D. / Stampoulis, V. / Aitkin, M. / Algeri, S. / Barnacka, A.
AcadYr 2015-2016
DePasquale, J. / Tak, H. / Meng, X.-L. / Jones, D. / Huang, J. / Blanchard, P. / Chen, Y. & Wang, X. / Tak, H. / Mandel, K. / Jiao, X. / Wang, X. & Chen, Y. / IACHEC WG / Si, S. / Drake, J. / Stampoulis, V. / Algeri, S. / Stein, N. / Chunzhe, Z. / Andrews, J. / Vrtilek, S. / Udaltsova, I. & Stampoulis, V.
AcadYr 2016-2017
Wang, X. & Chen, Y. / Kashyap, V., Siemiginowska, A., & Zezas, A. / Stampoulis, V. / Portillo, S. / Zhang, K. / Mandel, K. / DiStefano, R. / Finkbeiner, D. & Meade, B. / Gong, R. / Shihao Y. / Zhirui, H. / Xufei, W. / Campos, L. / Tak, H. / Xufei, W. / Jones, D. / Algeri, S. / Speagle, J. / Czekala, I.
AcadYr 2017-2018
AstroStat Day / Speagle, J. / Collin, G. / McKeough, K. & Yang, S. / McKeough, K. & Campos, L. / M. Ntampaka / H. Marshall / D. Huppenkothen / X. Yu / R. DiStefano / J. Yee / H. Tak / A. Avelino
AcadYr 2018-2019
Stenning, D. / Dvorkin, C. / Sottosanti, A. / Yu, X. / Chen, Y. / Jones, D. / Lee, T.C.-M. / Tak, H. / Kashyap, V., McKeough, K., Campos, L., et al. / Baines, P. / Collin, G. / Muthukrishna, D. / Zhang, D. / Algeri, S. / Janson, L. / Ward, S. / de Beurs, Z.
AcadYr 2019-2020
McKeough, K. / Astudillo, J. & Protopapas, P. / Zezas, A. / Speagle, J. / Meng, X.-L., Siemiginowska, A., & Kashyap, V. / Bonfini, P. / Liu, C. / Guenther, H. / Castrillon, J. / McKeough, K. / Broekgaarden, F. / Autenrieth, M. / Motta, G. / Zucker, C. / Tak, H. / Kashyap, V. & Wang, X. / Wang, J. / Wang, X. & Ingram, J.
AcadYr 2020-2021
Diaz Rivero, A. / Marshall, H. & Chen, Y. / McKeough, K. / Chen, Y. / Patil, A. / Jerius, D. / Wang, X. / Siemiginowska, A. / Xu, C. / Picquenot, A. / Jacovich, T. / Geringer-Sameth, A. / Toulis, P. / Donath, A. / Ergin, T. / Phillipson, R. / Sun, H. / Autenrieth, M.
AcadYr 2021-2022
Makinen, T.L. / Siemiginowska, A. / Fox-Fortino, W. / Reddy, K. / Primini, F. / Mishra-Sharma, S. / Meyer, A. / Janson, L. / Group
AcadYr 2022-2023
Saydjari, A. / Rau, M.M. / McKimm, H. / Sairam, L. / Meyer, A. / SCMA8 / Kochanski, N. & Chen, Y. / Jones, G. / ISI WSC / Li, D.D.
AcadYr 2023-2024
Garraffo, C. / Gu, M. / Villar, A. & Martinez-Galarza, J.R. / Siemiginowska, A. / Protopapas, P. / Marshall, H., Athiray, S., & Kashyap, V.L. / Chen, Y. & Bonamente, M. / Zhang, X. / Li, J.S.