AstroStat Talks 2022-2023
Last Updated: 20230725

International CHASC AstroStatistics Centre

Topics in Astrostatistics

AY 2022-2023

Archive


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



Presentations
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
Zoom
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]
 
Antoine Meyer (Imperial)
Wed Jun 7
12:30pm EDT
Phillips Auditorium, CfA
Special High Energy Seminar
TD-CARMA: Painless, accurate, and scalable estimates of gravitational-lens time delays with flexible CARMA processes
Abstract: Cosmological parameters encoding our understanding of the expansion history of the Universe can be constrained by the accurate estimation of time delays arising in gravitationally lensed systems. We propose TD-CARMA, a Bayesian method to estimate cosmological time delays by modelling the observed and irregularly sampled light curves as realizations of a Continuous Auto-Regressive Moving Average (CARMA) process. Our model accounts for heteroskedastic measurement errors and microlensing, an additional source of independent extrinsic long-term variability in the source brightness. The semi-separable structure of the CARMA covariance matrix allows for fast and scalable likelihood computation using Gaussian Process modeling. We obtain a sample from the joint posterior distribution of the model parameters using a nested sampling approach. This allows for ``painless'' Bayesian Computation, dealing with the expected multi-modality of the posterior distribution in a straightforward manner and not requiring the specification of starting values or an initial guess for the time delay, unlike existing methods. In addition, the proposed sampling procedure automatically evaluates the Bayesian evidence, allowing us to perform principled Bayesian model selection. TD-CARMA is parsimonious, and typically includes no more than a dozen unknown parameters. We apply TD-CARMA to six doubly lensed quasars HS 2209+1914, SDSS J1001+5027, SDSS J1206+4332, SDSS J1515+1511, SDSS J1455+1447, SDSS J1349+1227, estimating their time delays as -21.96 +- 1.448, 120.93 +- 1.015, 111.51 +- 1.452, 210.80 +- 2.18, 45.36 +- 1.93 and 432.05 +- 1.950 respectively. These estimates are consistent with those derived in the relevant literature but are typically two to four times more precise.
Presentation video [!yt]
 
SCMA VIII
Jun 12-16
State College, PA
Talks and posters by CHASC collaborators at SCMA VIII
Hector McKimm on Bayesian Modelling of Photon Pile-up
Sara Algeri on On computationally efficient methods for testing multivariate distributions with unknown parameters
Max Autenrieth on Estimation of Galaxy Luminosity Distributions from Incomplete X-ray and Optical Survey Data
Xiao-Li Meng on Conducting Highly Principled Data Science: A Statistician's Job and Joy (Working with Astrophysicists)
Antoine Meyer on Bayesian analysis of dispersed line complexes
Yang Chen on Statistical Methods for Solar Flare Prediction/Classification
Vinay Kashyap on Naive Bayes Classification of X-ray Sources in Cygnus OB2
 
Noah Kochanski & Yang Chen (Michigan)
Fri Jun 9
11am EDT
Zoom
[1] Statistical Properties of Solar Flare Dependency
[2] Goodness-of-fit in Astrophysics: Properties of C statistics
Abstract: In the first half of the talk, we will discuss the solar flare dependency tests. As machine learning methods become more prevalent within the solar flare prediction community, a complete understanding of the distributions which govern the flare process is needed for appropriate statistical modeling. In order to analyze the dependency structure that subsequent flares exhibit, we adopt the use of hypothesis testing to identify time intervals which flaring events are highly dependent as well as time intervals where they appear to be independent events. Information from this analysis could be implemented to improve operational solar flare prediction systems where forecasts are constantly updated with the most up to date information.
In the second half of the talk, we will discuss properties of goodness-of-fit assessment using C-statistics.
Presentation slides: Solar flare dependency ; Cstat [.pdf]
Presentations video [!yt]
 
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).
Presentation slides [.pdf]
Presentation video [!yt]
 
ISI WSC 2023
Jul 19
Ottawa, ON
Statistics for Astronomy
Invited paper session at International Statistical Institute's World Statistics Congress.
 
David Dayi Li (Toronto)
Fri Jul 21
Noon EDT
Principled Bayesian Inference for estimating Globular Cluster Counts in Ultra-Diffuse Galaxies using Mark-Dependently Thinned Point Process
Abstract: Ultra-Diffuse Galaxies (UDGs) are a class of extremely faint galaxies that have attracted recent attention in astronomy due to their potential to understand Dark Matter. One important aspect of studying UDGs is their globular clusters (GCs) as many UDGs have been found to possess quite a significant number of GCs despite their low-surface brightness. However, existing GC counting methods in astronomy present various issues. Moreover, there seems to be massive disagreements on the GC counts in the same UDGs. In this paper, we introduce a novel Bayesian model to infer the GC counts in UDGs based on the Mark-Dependently Thinned Point Process (MTPP). Our work is the first substantive application of the MTPP model framework to a complex real-world problem where full Bayesian inference is conducted. We also elucidate some novel insights on the MTPP model framework that provide a holistic view on the nature of the MTPP. Based on MTPP, we treat the point pattern of observed GCs as a realization of a thinned inhomogeneous Poisson process where the thinning depends on both the location and the apparent magnitude of the GC (mark). We construct an efficient adaptive MCMC algorithm to conduct inference for our model. We demonstrate the use of our model on simulated data as well as a set of real GC data obtained from the PIPER survey targeting GCs in the Perseus galaxy cluster.
Presentation slides [.pdf]
Presentation video [!yt]
 
 
 
 
















Archive
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.

CHASC