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AstroStat Talks 2015-2016

Last Updated: 2016feb29

ICHASC

Topics in Astrostatistics

Statistics 310, Harvard University
Statistics 281, University of California, Irvine

AY 2015-2016

Archive

Instructor	Prof. Meng Xiao Li (HU)
	Prof. David van Dyk (ICL)
	Prof. Yu Yaming (UCI)
Schedule	Tuesdays 1:07PM - 2:30PM ET
Location	SciCen 706

Presentations
Joe DePasquale (CfA) 8 Sep 2015 1pm EDT SciCen 706	The Art & Science of Image Processing Abstract: The Chandra Communications & Public Engagement group at the Center for Astrophysics plays a central role in representing NASA's Chandra X-ray Observatory to the public. Bi-weekly Chandra press releases provide high-quality peer-reviewed science content for non-experts, and the astronomical imagery produced to support such releases can be considered the public face of Chandra. Each public image is unique and a considerable effort is put into creating imagery that is not only aesthetically pleasing, but also accurately and effectively communicates the science. After a brief overview of the Chandra program and the unique challenges in processing X-ray data, I will walk through the creation of an image for public release, providing key insights into the processing involved. With an eye towards the future, I'll briefly describe an ongoing project to merge the end result "pretty pictures" with science analysis. Presentation slides: [.key] (v6+) [.pdf]
Hyungsuk Tak (Harvard) 15 Sep 2015 12:37pm EDT SciCen 706	Microlensing corrections in TDC Abstract: I briefly overview the time delay estimation problem and introduce a microlensing effect that arises when stars in the lensing galaxy introduce independent flickering noises into the quasar images. If the timescale of this effect is much larger than that of the quasar variability, the light curves can have different long-term trends. Because our curve-shifting assumption does not hold with the different long-term trends, our model produces (approximate) marginal posterior distribution of the time delay that has several modes near margins of the entire range of the time delay. These modes near margins tend to overwhelm the density near the true time delay, making it hard to estimate the true time delay. I suggest a way to estimate the time delay, reducing the effect of microlensing. Slides [.pdf]
Xiao-Li Meng (Harvard) 06 Oct 2015 12:15pm EDT SciCen 706	Seeking Effective Adjustments for Effective Areas Discussion of the cross-calibration shrinkage method. Given I instruments and J sources, measurements in a variety of combinations thereof, to determine a regression correction to the underlying effective areas to bring them all in sync. Slides [.pdf]
David Jones (Harvard) 13 Oct 2015 1:15pm EDT SciCen 706	Designing Test Information and Test Information in Design Abstract: Since telescope time is limited, astronomers wishing to classify lightcurves must carefully select future time points at which to observe lightcurves of interest in order to maximize the information that will be gained for classification. This work proposes a framework for constructing measures of test / classification / model selection information and explores how to use them in experimental design problems such as lightcurve classification. Degroot (1962) developed a general framework for constructing Bayesian measures of the expected information that an experiment will provide for estimation, and our framework analogously constructs frequentist and Bayesian measures of information for hypothesis testing. In contrast to estimation information measures that are typically used in experimental design for surface estimation, test information measures are most useful in experimental design for model selection and classification problems. Indeed, our framework suggests a probability based measure of test information, which in hypothesis test applications has more appealing properties than variance based measures. We also extend a result of Nicolae et al. (2008) linking test and Fisher information, and propose a fundamental coherence requirement for test information measures. Slides [.pdf]
Jane Huang (HU) Peter Blanchard (HU) 27 Oct 2015 1:07pm EDT SciCen 706	Searching for vibronic progressions in the diffuse interstellar bands [JH] Abstract: The diffuse interstellar bands, observed at optical, infrared, and UV wavelengths, are a series of hundreds of absorption bands observed in stellar spectra due to molecules in intervening clouds. Though the first DIBs were discovered about a century ago, so far only one of the molecular carriers has been confirmed. Identifying more of these carriers would add to our understanding of how complex molecules form in the interstellar medium.To constrain the characteristics of candidate carriers, Duley and Kuzmin (2010) suggested searching for low-energy harmonic progressions among DIBs in order to identify bands that may arise from torsional motion of large molecules. I will discuss the use of agglomerative clustering methods to search for harmonic progressions and examine the likelihood of such progressions arising due to chance alignments. Slides [.pptx] The Impact of Positional Uncertainty on Gamma-Ray Burst Environment Studies [PB] Abstract: While it is now established that long-duration gamma-ray bursts (LGRBs) are a rare outcome of the death of some massive stars, it remains unclear what special conditions are required for the production of an LGRB. Studies of the preferred locations of LGRBs within their host galaxies can shed light on this open question. I use ground-based detections of LGRB afterglows to locate the bursts within high-resolution images of their faint host galaxies obtained using the Hubble Space Telescope. I measure the distribution of LGRB offsets from the centers of their host galaxies, and compare the brightness of the burst position relative to the total host light distribution. The dominant limiting factor in these studies comes from the uncertainty on the position of the LGRB relative to its host galaxy. In this talk, I will discuss the impact of positional uncertainty on host galaxy identification, and offset and light distribution measurement. It is important to address this issue to avoid biases in the full sample distributions so that LGRB distributions can be understood and compared to those for other types of stellar explosions. After a careful consideration of uncertainties, I will present the results I obtain and their implications for the progenitors of LGRBs. Slides [.pdf]
Yang Chen & Xufei Wang 10 Nov 2015 1:07pm EST SciCen 706	Calibration Concordance Follow-up to Xiao-Li Meng's talk on Oct 6 Outline: Part 1: Explanation of Multiplicative Model Part 2: log-Normal Model (shrinkage estimator, variance estimator, additive model) Part 3: Poisson Model (simplified version) Part 4: Question for discussion Presentation Slides [.pdf]
Hyungsuk Tak 24 Nov 2015 1:07pm EST SciCen 706	Down-Up Metropolis-Hastings Algorithm for Multimodality Abstract: I suggest a down-up Metropolis-Hastings (DUMH) algorithm that expedites Markov chain's jumps between modes of a multi-modal distribution in a simple and fast manner. This algorithm is essentially a Metropolis-Hastings (MH) algorithm that generates a proposal via two steps, a downhill step and an uphill step. Given the current state, an intermediate proposal is generated by a Metropolis algorithm with a reciprocal ratio of the target densities in the acceptance probability so that this Metropolis algorithm prefers a downward movement in density. For example, if the density of the intermediate proposal is smaller than that of the current state, then the intermediate proposal is accepted with a probability one. Given the intermediate proposal, a final proposal is generated by another Metropolis algorithm with a typical acceptance probability that prefers an upward movement in density. This down-up movement in density increases the chance that the final proposal is at a different mode. The DUMH algorithm accepts the final proposal with an MH acceptance probability. Because this MH acceptance probability involves a ratio of intractable integrations, the DUMH algorithm uses an auxiliary variable to cancel out the intractable ratio. Simulation results show that the DUMH algorithm explores some high-dimensional and multi-modal distributions more effectively than a random-walk Metropolis algorithm. Slides [.pdf]
Kaisey Mandel (CfA) 26 Jan 2016 1:07pm EST SciCen 706	Supernova Cosmology in the Near-Infrared with Hierarchical Bayesian Light Curve Models Abstract: The Nobel Prize-winning discovery of the accelerating expansion of the Universe was made by astronomers using optical observations of the brightness time series of faraway exploding stars (Type Ia supernova light curves) to determine cosmological distances. Current and future optical supernova surveys aim to determine the physical nature of the mysterious dark energy driving the acceleration. However, these efforts are now limited by systematic, rather than statistical errors. The fortuitous properties of supernova light curves in the near-infrared (NIR) offer a powerful strategy for improving distance estimates and cosmological constraints. I have constructed a hierarchical Bayesian model for optical and NIR supernova light curves incorporating multiple random effects and uncertainties, including host galaxy dust, measurement error, and intrinsic supernova variations across time and wavelength, to determine precise and accurate supernova distances. I will describe ongoing efforts to trace the history of cosmic expansion by applying this statistical model to analyze new, large datasets of ground-based NIR observations of nearby supernovae, as well as recent Hubble Space Telescope NIR observations of cosmologically distant supernovae discovered by the Pan-STARRS and Dark Energy Survey supernova searches. Presentation slides [.pdf]
Xiyun Jiao (Imperial) 2 Feb 2016 6:07pm GMT 10:07am PST/1:07pm EST Imperial	Next-generation Gibbs-type Samplers: Combining Strategies to Boost Efficiency Abstract: Although the Data Augmentation (DA) algorithm and the Gibbs sampler are widely used tools for obtaining a sample from the posterior distribution under complex Bayesian models, they are sometimes notoriously slow to converge. Thus, a number of strategies have been proposed to improve their convergence properties. Among them, we focus on three methods: the Marginal Data Augmentation (MDA), Ancillarity-Sufficiency Interweaving Strategy (ASIS), and Partially Collapsed Gibbs (PCG) sampling. To further improve the convergence of Gibbs-type samplers, we propose strategies for combining MDA, ASIS, PCG sometimes in conjunction with Metropolis-Hastings type updates. We construct a general framework to combine these methods into coherent samplers and guarantee that the combined samplers maintain their target stationary distributions and can only improve the convergence properties of their parent Gibbs-type samplers. We demonstrate the implementation and efficiency of our framework by implementing it to fit a factor analysis model and a cosmological hierarchical model, which is the motivation of our work. Presentation slides [.pdf]
Wang Xufei and Chen Yang (Harvard) 16 Feb 2016 1:07pm EST SciCen 706	Progress report of 'Calibration with Multiplicative Means but Additive Errors: A Log Normal Approach' Abstract: Useful information to calibrate instruments used for astrophysical measurements is usually obtained by observing different sources with well-understood characteristics simultaneously with different detectors. This requires a careful modeling of the mean signals, the intrinsic source variations, and measurement errors. Because our data are typically large (>>30) photon counts, we propose an approximate log-normal model, with the advantage of permitting imperfection in the multiplicative mean model to be captured by the residual variance. The calibration takes an analytically tractable form of power shrinkage, with a half-variance adjustment to ensure an unbiased multiplicative mean model on the original scale. In this presentation, we will talk about the current progress regarding the properties and model fittings (frequentist and Bayesian methods) of the log-normal regression model. We will also discuss the potential issues and pitfalls of fitting a Poisson regression model, which is more realistic but more restrictive. Simulation studies and real data fittings will be shown. Presentation slides [.pdf]
Group 1 Mar 2016 9:00am IST 29 Feb 7:30pm PST / 10:30pm EST IUCAA	IACHEC Calibration Uncertainties Working Group Meeting Preliminary agenda: Vinay Kashyap, Introduction to pyBLoCXS Slides [.pdf] Konrad Dennerl, Modeling RMFs Herman Marshall, Introduction to Calibration Concordance Project Keith Arnaud, Updates to XSPEC Xiao-Li Meng/Yang Chen/Xufei Wang, Calibration Concordance
Shijing Si (Imperial) 22 Mar 2016 6:07pm GMT 10:07am PST/1:07pm EST Imperial	Bayesian Hierarchical Models for Stellar Evolution Abstract: In astrophysics, we often aim to estimate a parameter for each of a number of objects in a population. For example, we may want to estimate the age of each of a sample of halo white dwarf (WD). The standard strategy is to separately study each of the objects using case-by-case analyses, and then in a follow-up analysis, study the distribution of the fitted parameters across the population. In this research, we develop novel methods that allow us to take advantage of existing software designed for such case-by-case analyses to simultaneously fit parameters of both the individual objects and the parameters that quantify their distribution across the population. Our methods are based on Bayesian hierarchical modelling which is known to produce parameter estimators for the individual objects that are on average closer to their true values than estimators based on case-by-case analyses. We verify this in the context of estimating ages of Halo white dwarfs via a series of simulation studies. We apply our techniques to three astrophysical problems. The first one is essentially meta-analysis of the distance modulus to the large magenllanic cloud (MLC) by combining some published results. The second project is to obtain more precise estimates of WD ages by fitting their photometric data in a hierarchical model. The third problem is to study the initial-final mass relationships (IFMR) via combining three stellar clusters. Presentation slides [.pdf]
Vasileios Stampoulis (Imperial) 3 May 2016 6:07pm GMT 10:07am PST/1:07pm EST Imperial	TBD
Sara Algeri (Imperial) 17 May 2016 6:07pm GMT 10:07am PST/1:07pm EST Imperial	TBD

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. / Stampoulis, V. / Algeri, S.

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