AstroStat Talks 2017-2018
Last Updated: 2018jan16


Topics in Astrostatistics

AY 2017-2018


20 Sep 2017
10am-4pm EDT
Phillips Auditorium at CfA
AstroStat Day
10:00am - 12:10pm : Siemiginowska, Vikhlinin, Finkbeiner, Portillo, Daylan, Speagle, B. Johnson
12:30pm - 1:30pm : Reeves, Winter
(SolarStat, in conjunction with HEAD Lunch Talks)
2:00pm - 4:00pm : Grindlay, M. Johnson, Blackburn, Bouman, Avelino, Zucker
4:00pm - 5:00pm : Discussion
Josh Speagle (CfA)
26 Sep 2017
1:07pm EDT
Pratt (Perkin-G, CfA)
Dynamic Nested Sampling
Nested Sampling is a relatively new method for estimating the Bayesian evidence (with the posterior estimated as a byproduct) that integrates over the posterior by sampling in nested "shells" of constant likelihood. Its ability to sample from complex, multi-modal distributions in a flexible yet efficient way combined with several available sampling packages has contributed to its growing popularity in (astro)physics. In this talk I will outline the basic motivation and theory behind Nested Sampling, derive various statistical properties associated with the method, and discuss how it is applied in practice. I will then talk about how the overall framework can be extended in Dynamic Nested Sampling to accommodate adding samples "dynamically" during the course of a run. These samples can be allocated to maximize arbitrary objective functions, allowing Dynamic Nested Sampling to function as a posterior-oriented sampling method such as MCMC but with the added benefit of well-defined stopping criteria. I will end by applying Dynamic Nested Sampling to a variety of synthetic and real-world problems using an open-source Python package I've been developing (dynesty).
Presentation slides [.pdf]
See also: MultiNest ; PolyChord [url
Gabriel Collin (MIT)
21 Nov 2017
1:07pm EST
Pratt (Perkin-G, CfA)
Searching for the origin of astrophysical neutrinos using a non-Poissonian statistical method
Abstract: The IceCube neutrino observatory was designed to detect astrophysical neutrinos, which originate from outside of our solar system. IceCube has detected candidate astrophysical events, and measured a diffuse flux, but the source of these neutrinos so far remains unknown. Current approaches look for "hot spots" of neutrino events in the sky. It is also possible to describe a population of sources in terms of the number of observed events, forming a non-Poissonian statistical distribution. This distribution was used to show that the excess of gamma rays measured by Fermi-LAT around the galactic center was likely due to point sources rather than decaying dark matter. In this talk, I will present the application of this statistical method to the search for point sources in IceCube.
Evidence for Unresolved Gamma-Ray Point Sources in the Inner Galaxy, Lee et al. arxiv:1506.05124 [.url]
NPTfit [.url]
Katy McKeough & Shihao Yang (Harvard)
28 Nov 2017
1:07pm EST
SciCen 706
Defining regions that contain X-ray jets in high-redshift quasars
Abstract: Using only the X-ray observation of a quasar and a jet, we are interested in creating an outline around an extended source (jet). Astronomers are interested in delineating jets from their quasar source and background radiation. This is particularly difficult in images of high redshift jets taken in X-ray where there are a limited number of pixel counts. McKeough et al. 2016 and Stein et al. 2015 proposes a method where jets are detected using previously defined regions of interest (ROI). However, we do not always have supplementary information to predetermine these ROI and the size and shape can greatly affect flux/luminosity measurements and power of detection. Low Count Image Reconstruction and Analysis (LIRA) has been tremendously successful in analyzing low counts images and extracting structure smeared out by the PSF. However, the intensities derived using it are pixellated. That is, LIRA is unaware of correlations that may exist between adjacent pixels in the real image. In order to group pixels of a similar nature, we impose a successor or post-model on the output of LIRA. We adopt the Ising model, which has been used extensively in Condensed Matter Physics to model electron spin states, as a prior on assigning the pixels to either the background or the ROI.
Presentation slides [.pdf]
Katy McKeough & Luis Campos (Harvard)
12 Dec 2017
12:37pm EST
CfA Library
Ask A Statistician: An oppportunity for astronomers at the CfA to ask statistics questions of statisticians; from the mundane to the philosophical, bring your statistics problems to be discussed by the panel
We will be going through several applications of statistics in astronomy. Each application will serve as the backdrop for discussing a different statistical technique. We will suggest partial solutions or new directions for each of these proposed issues that we hope will stimulate further questions and discussion.
The following examples are:
- Propagating asymmetrical error bars via parametric bootstrap.
- Correlation between two time series observations.
- Using external information as a prior in Bayesian inference.
- Explanation of shrinkage.
- Detection significance with multiple hypothesis testing.
Presentation Slides [url]
James-Stein Estimator R model [.rmd]
Michelle Ntampaka (CfA)
23 Jan 2018
1:07pm EDT
SciCen 706
Constraining Sigma-8 and Omega-Matter with the Velocity Distribution Function
Abstract: I will present the Velocity Distribution Function (VDF), a new approach for quantifying the abundance of galaxy clusters and constraining cosmological parameters using dynamical measurements. In this new method, the probability distribution of velocities for each cluster in the sample are summed to create a new test statistic, which can be measured more directly and precisely than the more standard halo mass function, and can be robustly predicted with cosmological simulations which capture the dynamics of subhalos or galaxies. I will present preliminary constraints on sigma-8 and omega-matter from spectroscopic observations of the HeCS-SZ clusters.
Herman Marshall (MIT)
06 Feb 2018
1:07pm EDT
SciCen 706
X-ray Polarimetry

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