AstroStat Talks 2019-2020
Last Updated: 2019oct16

International CHASC AstroStatistics Centre

Topics in Astrostatistics

Statistics 310, Harvard University

AY 2019-2020


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

Katy McKeough (HU)
2019 Sep 03
(contd.) 2019 Sep 10
SciCen 706
Defining Regions that Contain Complex Astronomical Structures
Abstract: Astronomers are interested in delineating boundaries of extended sources in noisy images. An example is finding outlines of a jet in a distant quasar. This is particularly difficult for jets in high redshift, X-ray images where there are a limited number of pixel counts. Using Low-counts Image Reconstruction and Analysis (LIRA), McKeough 2016 and Stein 2015 propose and apply a method where jets are detected using previously defined regions of interest (ROI). LIRA, a Bayesian multi-scale image reconstruction, has been tremendously successful in analyzing low count images and extracting noisy structure. However, we do not always have supplementary information to predetermine ROI and the size and shape can greatly affect flux/luminosity. LIRA is also unaware of correlations that may exist between adjacent pixels in the real image. In order to group similar pixels, we impose a successor or post-model on the output of LIRA. We adopt the Ising model as a prior on assigning the pixels to either the background or the ROI. The final boundary and uncertainty are informed by the posterior draws of these assignments. This method has been applied to the jet data as well as simulations and appears to be capable of picking out meaningful ROIs. [webcast url] (connect with desktop browser [Chrome works best] or dedicated mobile app)
Presentation slides [.pdf]
[animated gif]
Javiera Astudillo (IACS) and Pavlos Protopapas (SEAS/HU)
2019 Oct 01
SciCen 706
An Information Theory Approach on Deciding Spectroscopic Follow Ups
Abstract: Classification and characterization of variable phenomena and transient phenomena are critical for astrophysics and cosmology. These objects are commonly studied using photometric time series or spectroscopic data. Given that many ongoing and future surveys are in time-domain and given that adding spectra provide further insights but requires more observational resources, it would be valuable to know which objects should we prioritize to have spectrum in addition to time series. We propose a methodology in a probabilistic setting that determines a-priory which objects are worth taking spectrum so that classification prediction is improved. Objects for which we query spectrum are reclassified using their full spectrum information. We first train two classifiers, one that uses photometric data and another that uses photometric and spectroscopic data together. Then for each photometric object we estimate the probability of each possible spectrum outcome. We combine these models in various probabilistic frameworks (strategies) which are used to guide the selection of follow up observations. The best strategy depends on the intended use. For a given number of objects (127, equal to $5\%$ of the dataset) to be observed, we improve 37\% (47) class prediction accuracy as opposed to 20% (25) of a non-naive (non-random) best base-line strategy. Further, we improve the ground truth probability 1.18 times as much as the best base-line strategy. Our approach provides a general framework for follow-up strategies and can be extended beyond classification and to include other forms of follow-ups beyond spectroscopy. [webcast url] (connect with desktop browser [Chrome works best] or dedicated mobile app)
Presentation slides:
gdrive [ppt]
download [pdf]
Andreas Zezas (CfA, Crete)
2019 Oct 08
SciCen 706
Projects of RISE-AstroStat II
Abstract: Current challenges in the analysis of astronomical data include the development of efficient source detection algorithms. This includes images, as well as, multi dimensional data with spectral and/or timing information. Although major progress has been made in these directions over the past years, significant work is needed in order to apply these method to the next generation of X-ray, and multi-wavelength data. I will present some of these challenges and how they are linked to the ASTROSTAT-II project, a network of European, US, and Canadian Astronomy and/or Statistics institutes.
Presentation slides [.pdf]
Josh Speagle (HU)
2019 Oct 22
SciCen 706
The Devil's in the Details: Photometric Biases in Modern Surveys
Abstract: Many modern surveys use maximum-likelihood estimates (MLEs) for positions, fluxes, and other parameters for stars, galaxies, and other astrophysical phenomena from 2-D images. These MLEs are then used to make catalogs used in the vast majority of astronomical analyses. I will provide an overview of the basic ingredients present when modeling these images, and illustrate how the MLE behaves in various cases. I will then present results from recent work showing that the MLE systematically overestimates the flux as a function of the signal-to-noise ratio (SNR) and the number of parameters involved in the fit. I will then examine how this bias behaves when fitting multiple images at once, which are necessary to estimate the "colors" of astronomical objects. We find that common "forced" photometry approaches (where the position is sometimes fixed) actually compound the above bias in derived colors, while more rigorous "joint" photometry approaches (where all images are modeled simultaneously) actually distribute the bias between all the images. We find our bias is present when examining data from idealized simulations, fake object pipeline tests, and real astronomical datasets, implying it is widespread among most datasets in use today. I will also discuss second-order effects relating to error estimation.
See: arXiv:1902.02374 [url]
Xiao-Li Meng (HU), Aneta Siemiginowska (CfA), Vinay Kashyap (CfA)
2019 Oct 29
12:30pm-1:30pm EDT
The Next Decade of Astroinformatics and Astrostatistics
Chun Liu (IIT)
2019 Nov 12
SciCen 706
Clustering via energy minimization
Hans Moritz Guenther (MIT)
2019 Nov 19
SciCen 706
Inferring the ACIS grade distribution

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. / Liu, C. / Guenther, H.