AstroStat Talks 2018-2019
Last Updated: 2019jan15


Topics in Astrostatistics

Statistics 310, Harvard University

AY 2018-2019


Schedule Tuesdays 12:07PM - 1:30PM ET
Location SciCen 706

David Stenning (Imperial)
19 Jul 2018
2pm-3pm EDT
SSXG Operations Center at CfA
Classification and Modeling of Evolving Solar Features
Abstract: Advances in space-based observatories are increasing both the quality and quantity of solar data, primarily in the form of high-resolution images. The goal of these observatories is to better understand and predict space weather. To analyze massive streams of solar image data, we have developed a science-driven dimension reduction methodology to extract scientifically meaningful features from images. Adopting a science-driven approach, as opposed to a solely black-box algorithmic approach, enables interpretable secondary data-driven analyses of complex phenomena, such as the evolution of magnetic active regions. The methodology utilizes mathematical morphology to produce a concise numerical summary of the magnetic flux distribution in active regions that (i) is far easier to work with than the source images, (ii) encapsulates scientifically relevant information in a much more informative manner than existing schemes (i.e. manual classification schemes), and (iii) is amenable to sophisticated statistical analyses.
Presentation slides [.pdf]
4 Sep 2018
Noon EDT
SciCen 706
Organizational & EBASCS
Cora Dvorkin (HU)
11 Sep 2018
Noon EDT
SciCen 706
Inverse Problems in Early Universe Cosmology
Abstract: Cosmological observations have provided us with answers to age-old questions, involving the age, geometry, and composition of the universe. However, there are profound questions that still remain unanswered. I will describe ongoing efforts to shed light on some of these questions. In this talk, I will explain how we can use measurements of the Cosmic Microwave Background and the large-scale structure of the universe to reconstruct the detailed physics of much earlier epochs, when the universe was only a tiny fraction of a second old. I will address this inverse-problem reconstruction from a Bayesian perspective.
Andrea Sottosanti (Imperial)
2 Oct 2018
Astronomical source detection and background separation via hierarchical Bayesian nonparametric mixtures
Abstract: We propose an innovative approach based on Bayesian nonparametric methods to the signal extraction of astronomical sources in gamma-ray count maps under the presence of a strong background contamination. Our model simultaneously induces clustering on the photons using their spatial information and gives an estimate of the number of sources, while separating them from the irregular signal of the background component that extends over the entire map. From a statistical perspective, the signal of the sources is modeled using a Dirichlet Process mixture, that allows to discover and locate a possible infinite number of clusters, while the background component is completely reconstructed using a new flexible Bayesian nonparametric model based on b-spline basis functions. The resultant can be then thought of as a hierarchical mixture of nonparametric mixtures for flexible clustering of highly contaminated signals. We provide also a Markov chain Monte Carlo algorithm to infer on the posterior distribution of the model parameters which does not require any tuning parameter, and a suitable post-processing algorithm to quantify the information coming from the detected clusters. Results on different datasets confirm the capacity of the model to discover and locate the sources in the analysed map, to quantify their intensities and to estimate and account for the presence of the background contamination.
Presentation slides [.pdf]
Xixi Yu (Imperial)
23 Oct 2018
Multistage Anslysis on Solar Spectral Analyses with Uncertainties in Atomic Physical Models
Abstract: Information about the physical properties of astrophysical objects cannot be measured directly but is inferred by interpreting spectroscopic observations in the context of atomic physics calculations. A critical component of this analysis is understanding how uncertainties in the underlying atomic physics propagates to the uncertainties in the inferred plasma parameters.
Instead of using the standard approach, a common strategy deployed by the astrophysicists, that treats the uncertainty as fixed and known and obtains the best-fit values of the parameters, we propose a multistage analysis to prevent underestimation of the error bars on the model parameters and increase the accuracy of the analysis results. Four methods for a two-stage analysis are outlined, the standard method, multiple imputation, the pragmatic and the fully Bayesian methods. A case study on Fe XIII is discussed where two different priors, discrete uniform and Gaussian approximation via principal component analysis prior, are deployed.
Presentation slides [.pdf]
Yang Chen (UMich)
30 Oct 2018
A second look at cstat
Abstract: After decades of least chi-squares fitting and goodness-of-fit, the C-stat has been gaining popularity in the astrophysics community for model fitting and assessment of goodness-of-fit. In this work, we study the statistical properties of the C-stat and explore lower-resolution C-stat fitting and testing, which potentially improves statistical and computational efficiency. This is ongoing joint work with CHASC team.
David Jones (TAMU)
13 Nov 2018
Exoplanet detection: some statistical challenges
Abstract: The radial velocity (RV) technique is one of the two main approaches for detecting planets outside our solar system. The method works by detecting the Doppler shift resulting from the motion of a host star caused by an orbiting planet. Unfortunately, this Doppler signal is typically contaminated by various "stellar activity" phenomena, such as dark spots on the star surface. This makes it difficult to determine if a planet is really present or not.
Last time I presented a Gaussian process framework for separating planet RV signals from stellar activity. In this talk, I will review the key points of the method and discuss current statistical challenges and opportunities for generalizing and improving the approach. I will also discuss related computational challenges in exoplanet detection.
Presentation slides [.pdf]
Thomas Lee (UC Davis)
27 Nov 2018
Change Point Detection for Poisson Time Series Images with Applications to Astronomy and Astrophysics
Presentation slides [.pdf]
Hyungsook Tak (Notre Dame)
11 Dec 2018
Time Delay Lens Modeling Challenge for the Hubble Constant Estimation
Abstract: The Hubble constant is a core cosmological parameter that represents the current expansion rate of the Universe. One way to infer this quantity is to use strong gravitational lensing, i.e., an effect that multiple images of an astronomical object (e.g., a quasar) appear in the sky. This effect occurs when the trajectories of the light (from the object to the Earth) are bent by a strong gravitational field of an intervening galaxy. Strong gravitational lensing produces two types of the data; (i) multiple brightness time series data of the gravitationally-lensed images and (ii) pixel-wise image data of the lens and lensed object. The former is used to infer time delays between the arrival times of the multiply-lensed images (arXiv 1602.01462 ) and the latter is used to estimate gravitational potential that the lensed images pass through (arXiv 1801.01506 ). These two components are used to infer the Hubble constant via physical equations. In this talk, I explain how we infer the Hubble constant using the relationship among these three components, i.e., time delays, gravitational potential, and the Hubble constant. I will also describe the performance of this approach during the first stage of a blind competition, called the Time Delay Lens Modeling Challenge.
Presentation slides [.pdf]
Vinay Kashyap, Aneta Siemiginowska, & Andreas Zezas (CfA)
29 Jan 2019
SciCen 706
Intro to Astro Data for Statisticians and what you can do with them
Arturo Avelino (CfA)
5 Feb 2019
SciCen 706
Gabriel Collin (MIT)
12 Feb 2019
SciCen 706
Using path integrals for the propagation of light in a scattering dominated medium
Sara Algeri (UMinnesota)
19 Feb 2019
Di Zhang (UCIrvine)
5 Mar 2019
New population-based MCMC method
David Stenning (Imperial)
19 Mar 2019
Vinay Kashyap, Mark Weber, & Aneta Siemiginowska (CfA)
SciCen 706
The Feigelson List

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., Siemiginowska, A., & Zezas, A. / Avelino, A. / Collin, G. / Algeri, S. / Zhang, D. / Stenning, D. / Kashyap, V., Weber, M., & Siemiginowska, A. /