AstroStat Talks 2022-2023
Last Updated: 20230417

International CHASC AstroStatistics Centre

Topics in Astrostatistics

AY 2022-2023


Schedule Fridays 11am - 12:30pm Eastern Time
Location Remote

Andrew Saydjari (CfA/Harvard)
Fri Jan 27, 2023
11am-12:30pm EST
Zoom + B-105
Measuring the 8621 ┼ Diffuse Interstellar Band in Gaia DR3 RVS Spectra: Obtaining a Clean Catalog by Marginalizing over Stellar Types
Andrew will talk about a method (in the Gaussian limit) to carefully measureáweak spectral lines and obtain quantitative uncertainties by marginalizing over the contribution of other components to the spectra. He will discuss this method in the context of a recent application to diffuse interstellar bands.
Abstract: Diffuse interstellar bands (DIBs) are broad absorption features associated with interstellar dust and can serve as chemical and kinematic tracers. Conventional measurements of DIBs in stellar spectra are complicated by residuals between observations and best-fit stellar models. To overcome this, we simultaneously model the spectrum as a combination of stellar, dust, and residual components, with full posteriors on the joint distribution of the components. This decomposition is obtained by modeling each component as a draw from a high-dimensional Gaussian distribution in the data-space (the observed spectrum) -- a method we call "Marginalized Analytic Data-space Gaussian Inference for Component Separation" (MADGICS). We use a data-driven prior for the stellar component, which avoids missing stellar features not included in synthetic line lists. This technique provides statistically rigorous uncertainties and detection thresholds, which are required to work in the low signal-to-noise regime that is commonplace for dusty lines of sight. We reprocess all public Gaia DR3 RVS spectra and present an improved 8621 ┼ DIB catalog, free of detectable stellar line contamination. We constrain the rest-frame wavelength to 8623.14▒0.087 ┼ (vacuum), find no significant evidence for DIBs in the Local Bubble from the 1/6th of RVS spectra that are public, and show unprecedented correlation with kinematic substructure in Galactic CO maps. We validate the catalog, its reported uncertainties, and biases using synthetic injection tests. We believe MADGICS provides a viable path forward for large-scale spectral line measurements in the presence of complex spectral contamination.
Presentation slides [.pptx]
Presentation video [!yt]
Markus Michael Rau (CMU)
Fri Feb 10
11am EST
Zoom + Pratt
Cosmological Inference in Photometric Surveys under Redshift Uncertainty
Abstract: Large current collaborative experiments like the Hyper Suprime-Cam Subaru Strategic Program and future programs like the Rubin Observatory Legacy Survey of Space and Time (LSST) lead modern observational Cosmology into an era of unprecedented precision. I discuss current challenges in cosmological inference in the context of photometric surveys and present my work on developing Hierarchical Bayesian models to parametrize photometric redshift uncertainty in the context of Weak Lensing and Large-Scale Structure cosmology.
I also discuss my recent work on sample redshift distribution inference for the Hyper Suprime-Cam Subaru Strategic Program Weak Lensing three-year analysis and discuss plans and implications for future experiments like LSST.
Presentation slides [.pdf]
Presentation video [!yt]
Hector McKimm (Imperial)
Fri Feb 17
4pm GMT
Sampling using Adaptive Regenerative Processes
Abstract: Enriching Brownian Motion with regenerations from a fixed regeneration distribution # at a particular regeneration rate # results in a Markov process that has a target distribution # as its invariant distribution. We introduce a method for adapting the regeneration distribution, by adding point masses to it. This allows the process to be simulated with as few regenerations as possible, which can drastically reduce computational cost. We establish convergence of this self-reinforcing process and explore its effectiveness at sampling from a number of target distributions. The examples show that our adaptive method allows regeneration-enriched Brownian Motion to be used to sample from target distributions for which simulation under a fixed regeneration distribution is computationally intractable.
Presentation slides [.pdf]
Lalitha Sairam (Birmingham)
Tue Apr 11
3pm EDT
Pratt + Zoom
When Stars Misbehave: The Impact of Stellar Activity on Exoplanet Research and the Need for a Public Forecast
Abstract: The study of exoplanets has unveiled a diverse array of worlds beyond our solar system. However, the detection and characterization of exoplanets remain challenging due to the magnetic activity of their host stars. Stellar noise produced by flares, star spots, and plages can mimic the signal of a low-mass exoplanet, leading to spurious detections and reducing the accuracy of atmospheric characterization. Although they are modelled for hindrance, stellar activity continues to affect detections by reducing the signal. In this talk, I will give an overview of the challenges that stellar activity poses for exoplanet detection and atmospheric characterisation. I will present my ongoing project, STellar ACtvity foreCAst for Optimal observations of exoplanets (STACCATO), which provides a forecasting model to predict the optimal time for exoplanet detection and atmospheric characteristics reducing the need for stellar activity mitigation. I will also demonstrate how STACCATO is synergistic with ongoing and upcoming missions such as HARPS3, ARIEL, and PLATO, and how these missions can be used in conjunction with STACCATO to further advance our understanding of exoplanets and their host stars.
See also: Sairam & Triaud 2022 MNRAS 514, 2259 [ADS]
Presentation slides/a> [.pdf]
Presentation video [!yt]
Noah Kochanski & Yang Chen (Michigan)
Fri Jun 9
11am EDT
Solar flare dependency and structures
Galin Jones (Minnesota)
Fri Jul 14
10am CDT
Hierarchical Bayesian method for constraining the neutron star equation of state with an ensemble of binary neutron star postmerger remnants: statistical, computational, and collaborative challenges
Abstract: Multi-Messenger Astrophysics employs multiple messengers to study astrophysical and cosmological events and processes: light, gravitational waves, neutrino particles, cosmic rays, and gamma rays. The field is experiencing a substantial increase in data with more to come driven by new telescopes, gravitational-wave detectors, neutrino detectors, and gamma-ray detectors. This is prompting development of novel tools for data processing and analysis, including tools for machine learning and Bayesian statistical methods.
The University of Minnesota is developing an interdisciplinary approach to addressing these challenges through teams of faculty and students from Statistics, Computer Science, Electrical Engineering, and Physics & Astronomy. I will consider some of the successes and challenges in taking such an approach, but the focus will be on statistical challenges and potential solutions. This will be illustrated with a detailed case study on developing a hierarchical Bayesian model for constraining the neutron star equation of state based on binary neutron star post-merger gravitational wave signals, which resulted in the publication Criswell et al. (2023, PhysRevD 107, 042021).
David Dayi Li (Toronto)
Fri Jul 21
Noon EDT
Point processes to detect ultra-diffuse galaxies

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. / Kochanski, N. & Chen, Y. / Jones, G.