AstroStat Talks 2014-2015

# Topics in Astrostatistics

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

### Archive

 Instructor Prof. Meng Xiao Li (HU) Prof. David van Dyk (ICL) Prof. Yu Yaming (UCI) Schedule Tuesdays 1PM - 3PM ET Location SciCen 706

Presentations
Brian Vegetabile (UC Irvine) & Tom Aldcroft (CfA/CXC) / Hong Jae Sub (Harvard)
12 Aug 2014
Noon EDT
SciCen 706
[Brian and Tom] Chandra X-Ray Observatory Aspect Camera Assembly Star Acquisition Analysis
Abstract: The Chandra X-Ray Observatory celebrated its 15th year of service this past July, showcasing a feat of engineering success that can be attributed to a team of talented engineers. Chandra was initially designed as a 5 year mission and as such robust models of performance are necessary to ensure the future success of the spacecraft. This talk gives an overview of the Chandra Aspect Camera Assembly (ACA) and the current challenges it faces with regards to star acquisition. We then present the current methodology that attempts to capture the probability that the ACA will acquire an individual star. Finally we present two methods that are planned for the assessing this probability as well as preliminary results. The methods discussed will be Logistic Regression based on Maximum Likelihood Estimates and then compared against a Bayesian Probit Regression model from the 2006 paper by Holmes and Held.

[Jaesub] Source Detection in Mosaicked NuSTAR Images Using 'Trial Number' Map
Abstract: Nuclear Spectroscopic Telescope ARray (NuSTAR), launched in June, 2012, is the first focusing hard X-ray telescope, covering 3 to 80 keV X-rays. As a part of the Galactic Plane Survey, we have observed the Galactic Center region with a series of overlapping pointings. It is desirable to perform source detection algorithms on the Mosaicked images in order to take full advantage of photon statistics. However, relatively large point spread function and dramatic changes of the exposure across the field make wavdetect and other conventional methods unreliable. We introduce a new approach for source detection using a simple Poisson statistics. This approach has been used to validate selected faint sources detected by wavdetect and other methods in the past. Here we apply the technique on every pixel in the entire image in search for new sources by generating a map of trial numbers needed to produce observed counts by random fluctuations.

Aneta Siemiginowska (CfA) / Vinay Kashyap (CfA)
2 Sep 2014
Noon EDT
SciCen 706
AstroStatistics: What is it good for?
Abstract: We introduce astrostatistics concepts to Statistics students. We describe the nature of high-energy X-ray and gamma-ray data and walk through some examples of high-energy astronomical sources. We then review the work done by CHASC graduate students.
Presentation slides: [.pdf] ; [.pptx] ; [.key '09]

Victor Pankratius (MIT/Haystack)
16 Sep 2014
12:30pm EDT
SciCen 706
Big Computing and Computer-Aided Discovery in Astronomy
Abstract: Next-generation astronomy needs to handle rapidly growing data volumes from ground-based and space-based telescope networks. In radio astronomy for instance, the current generation of antenna arrays produces data at Tbits per second, and forthcoming instruments will expand these rates much further. Human scientists are thus becoming increasingly overwhelmed when attempting to opportunistically explore Big Data.
As real-world phenomena at various wavelengths are digitized and mapped to data, the scientific discovery process essentially becomes a search process across multidimensional data sets. The extraction of meaningful discoveries from this sea of data therefore requires highly efficient and scalable machine assistance to enhance human contextual understanding.
Computer-Aided Discovery uses automation in a new way to match models and observations and support scientists in their search. The NSF-supported computational infrastructure currently being developed at MIT Haystack opens up new possibilities to answer questions such as: What inferences can be drawn from an identified feature? What does a finding mean and how does it fit into the big theoretical picture? Does it contradict or confirm previously established models and findings? How to test hypotheses and ideas effectively? To achieve this, scientists can programmatically express hypothesized scenarios, constraints, and model variations. Using programmable crawlers in a cloud computing environment, this approach helps delegate the automatic exploration of the combinatorial search space of possible explanations in parallel on a variety of data sets.
Bio: Victor Pankratius is a computer scientist at MIT Haystack Observatory. He is the principal investigator of the NSF-supported computer-aided discovery project and currently leading efforts to advance astroinformatics at Haystack. He is also involved in projects such as ALMA Phasing to enhance the ALMA Observatory with Very-Long Baseline Interferometry capabilities, the Event Horizon Telescope, as well as in the Radio Array of Portable Interferometric Detectors (RAPID). His track record includes research on parallel multicore systems and software engineering as well as industry collaborations with partners such as Intel, Sun Labs, and Oracle. Contact him at pankrat [at] mit.edu, victorpankratius.com, or Twitter @vpankratius.
Slides are in Dropbox/astrostat folder

Hyungsuk Tak (Harvard)
23 Sep 2014
12:30pm EDT
SciCen 705
Bayesian approach to time delay estimation
Abstract: Light rays from a quasar take dierent routes towards the earth when deflected due to a strong gravitational field that obstructs their ways. The arrival times of these rays vary depending on the lengths of paths and gravitational potentials that they go through, which leads to several images of the same source with lagged times. These differences in arrival time are called time delays used to calculate cosmological parameters, e.g. Hubble constant, H0. Though various grid-optimization methods to find time delay estimates have been dominating this field, a fully Bayesian model is promising because it turns computationally expensive grid optimization problem into a simple posterior sampling scheme. The model is based on a state- space representation for irregularly-observed time series data with an Ornstein-Uhlenbeck process for the unobserved underlying process. I present simulated data of doubly-lensed quasar with known delays and one real quasar data set to show its effectiveness and accuracy.
Slides [.pdf]

Laura Brenneman (CfA)
30 Sep 2014
12:30pm EDT
SciCen 706
Measurement of a Black Hole Spin: X-ray spectroscopy of the active galaxy NGC 4151: an example of spectral model fitting and caveats
Abstract: I will present our X-ray spectral analysis of a 150,000-s X-ray observation of the Seyfert 1.5 galaxy NGC 4151 taken with the NuSTAR and Suzaku observatories. We show that our broadband observations can disentangle the coronal emission, reflection, and absorption properties of the active galactic nucleus (AGN). Despite spectral complexity added by absorption variability during the observations, we find strong evidence for relativistic reflection from an ionized inner accretion disk. We compare our results with an alternative model composed only of absorption and reprocessing of continuum emission in several zones of material relatively far from the SMBH. We measure an increase in the total line-of-sight column density during a four-hour time interval, which is the shortest timescale for X-ray absorption variability yet observed in this source. These results demonstrate the power of employing NuSTAR in conjunction with lower-energy X-ray observatories such as Suzaku to measure the fundamental physical properties of AGNs with the greatest accuracy and precision ever achieved.

John Johnson (CfA)
07 Oct 2014
1pm EDT
SciCen 706
A way to teach Bayesian statistical methods to novices
Abstract: When a student first hears "Bayesian analysis", the words immediately sound foreign and the concept can be daunting. I will present my method of teaching Bayesian statistics to novices that leaves them with a set of fundamentals that can serve as a starting point to solving any any analysis problem (at least in principle). To the extent that applying these basic fundamentals becomes computationally intractable will lead the student to more advanced concepts.

Ryan Christopher Lynch (MIT)
21 Oct 2014
1pm EDT
SciCen 706
Bayesian approaches to the detection and analysis of unmodeled gravitational wave signals
Abstract: With the Advanced LIGO detectors planning to begin their first science runs in 2015, efforts are being made by data analysts to prepare for the first gravitational wave detections. While the gravitational waveforms emitted by compact binary coalescence events are well-modeled theoretically, efforts are also being made to prepare for unmodeled, burst-like signals emitted from events such as supernovae. In this talk, I will discuss two Bayesian approaches to the unmodeled burst problem. The first is an MCMC-based approach to parameter estimation, known as LALInference Burst (LIB), which I will show can also be used in the context of signal detection. The second is a semi-analytic sky-localization pipeline that is currently under development, which ideally could be used as a low-latency precursor to the full parameter estimation produced by LIB.
Slides [.pdf]

Fan Min Jie
04 Nov 2014
1pm EST/10am PST
SciCen 706
remotely from UCD
Separating image structures via graph-based seeded region growing
Detecting source structure in 2-D images is of great importance in astronomy. This talk reports our on-going work on using a graph-based seeded region growing (G-SRG) method for clustering and detecting source structures. The Delaunay triangulation and the Voronoi estimator are utilized in the process. The computation is fast and easy to implement. Our numerical experiments on a typical Chandra X-ray Observatory image show that this method achieves visually reasonable results.
slides [.pdf]

Meng Xiao-Li (Harvard)
25 Nov 2014
1pm-2:30pm EST
Sanders Theater
The Magic of MCMC and Statistics: A Live Performance
Markov chain Monte Carlo (MCMC) methods, originated in computational physics more than half a century ago, have had magical impact in quantitative scientific investigations. This is mainly due to their ability to simulate from very complex distributions needed by all kinds of statistical models, from bioinformatics to financial engineering to astronomy. This talk provides an introductory tutorial of the two most frequently used MCMC algorithms: the Gibbs sampler and the Metropolis-Hastings algorithm. Using simple yet non-trivial examples, we demonstrate, via live performance, the good, bad, and ugly implementations. Along the way, we reveal the statistical thinking underlying their designs, including the secret behind the greatest statistical magic ...
(Audience participations are required, though no prior experiences are needed.)

Giri Gopalan (Harvard)
02 Dec 2014
1pm EST
SciCen 706
A Bayesian Model for the Detection of X-ray Binary Black Holes
(joint work with Saku V. and Luke B.)
In X-ray binary systems consisting of a compact object that accretes material from an orbiting secondary star, there is no simple means to determine if the compact object is a black hole or a neutron star. To assist this process we develop a Bayesian statistical model, which makes use of the fact that X-ray binary systems appear to cluster based on their compact object type when viewed from a particular 3- dimensional coordinate system derived from spectral data. In particular we utilize a latent variable model in which the latent variables follow a Gaussian process prior, and hence we are able to induce the spatial correlation we believe exists between systems of the same type. The key parameters of this model are the probabilities that an observation comes from a black hole, a pulsar, or non-pulsing neutron star. A benefit of this approach is of a computational nature - the assumption of a prior which follows a multivariate normal distribution allows for the implementation of elliptical slice sampling for performing inference, a fast and stable alternative to standard Metropolis-Hastings or Gibbs sampling (Murray 2010). Our model is fit from 13 years worth of spectral data from 30 X-ray binary systems. Its predictive power is evidenced by the accurate prediction of system types using inferred probabilities from the aforementioned model.

A version of this talk was presented at the 225th meeting of the AAS at Seattle. A transcript, and associated animations are on github. [github]

Jiao Xiyun (Imperial)
27 Jan 2015
1pm EST / 6pm GMT
remotely from Imperial
Embedding Supernova Cosmology into a Bayesian Hierarchical Model
Abstract: The Physics Nobel Prize (2011) was awarded for the discovery that the expansion of the universe is accelerating. We embed a big bang cosmological model into a Bayesian hierarchical Gaussian model to quantify the acceleration. This problem has motivated our work and the complexity of the model makes it an ideal testbed to develop new strategies in algorithm. The Data Augmentation (DA) algorithm and Gibbs sampler are widely used for sampling from highly structured models. To improve their convergence, numerous algorithms have been developed. We pay special attention to the Marginal Data Augmentation (MDA), Partially Collapsed Gibbs (PCG) and Ancillarity-Sufficiency Interweaving Strategy (ASIS). We propose combining the usage of MDA, PCG, ASIS along with Metropolis-Hastings algorithm to simplify the implementation and further improve the convergence of Gibbs-type samplers. We use both the cosmological hierarchical model and a factor analysis model to illustrate our combining strategy. Moreover, we introduce the idea of surrogate distribution, which shares the same marginals with the target distribution but has different correlation structure. MDA, PCG and ASIS are unified by this idea. If we are only interested in a subset of parameters, it is promising to produce further efficiency with surrogate distribution samplers. In the end, we describe some extensions on the cosmological model.
Slides [.pdf]

Si Shijing (Imperial)
10 Feb 2015
1pm EST / 6pm GMT
remotely from Imperial
Empirical and Fully Bayesian Hierarchical Models: Two Applications in the Study of Stellar Evolution
Abstract: Fitting complex statistical models often requires sophisticated computing that for practitioners takes the form of black-box codes. While introducing hierarchical structures can have important statistical advantages (e.g., borrowing strength to reduce mean square error), it poses significant computational challenges, especially if one does not want to "open the black box". In this project we develop a wrapper for such black-box computer code that iteratively calls the black-box code in order to fit a hierarchical model via Empirical Bayes. Specifically, an EM-type algorithm is employed to obtain the MLEs of the hyper-parameters in a way that takes advantage of the available black-box code. We compare the results with a fully-Bayesian approach and deploy our methods on two problems in stellar evolution: estimating the population distribution of the age of Halo white dwarf stars and exploring the variability of the Initial Final Mass Relationship among stellar clusters.
Presentation Slides [.pdf]

Irina Udaltsova (UCDavis) and Andreas Zezas (Crete)
17 Feb 2015
10am PST / 1pm EST / 6pm GMT / 7pm EET
remotely from UC Davis and Crete
Colorful logN-logS
Abstract: The logN-logS is the relation between the number of sources (e.g. galaxies, stars, etc) as a function of their intensity. It is one of the key tools we have for characterizing and studying the populations of Astrophysical objects.
Unfortunately, its derivation is subject to several biases arising, for example, from the source detection process, and the Poissonian nature of the measured source intensities and the observed number of sources.
Over the past few years we have made several advances in the direction of accounting for these biases in a principled way.
We developed a hierarchical Bayesian model for inferring the logN-logS distribution for source population, corrected for the non-ignorable missing data mechanism. The method also allows the joint estimation of breakpoint(s) in the logN-logS and the unknown number of observed sources.
However, an additional complication that is related to the fact that not all sources have the same spectral parameters and influences the inferred source intensities, is rarely included in the formulation of the logN-logS analysis.
The goal of this talk is to present the problem, and discuss different possibilities for addressing it, which may ultimately lead to the combination of two major CHASC endeavors: the logN-logS analysis with BLoCKS or BEHR.
AZ slides [.ppt]
IS slides [.pdf]

Lazhi Wang (Harvard)
03 Mar 2015
1pm EST
Bayesian Model for Detection of X-ray Dark" Sources
Abstract: The goal of source detection is to obtain the luminosity function, which specifies the relative number of sources at each luminosity for a population of sources. Of particular interest is the existence of dark" sources with zero luminosity. In this talk, we first introduce the hierarchical Bayesian model we build for the source intensities. To capture the possible existence of X-ray dark" sources, we assume the intensities are zero with probability $\pi_d$, and follow a gamma distribution with probability $1-\pi_d$. We then discuss a hypothesis testing procedure to examine the existence of X-ray dark" sources. Results of simulation studies are provided to show the performance of the model, and the level and the power of the testing procedure under a variety of simulation settings. Finally, we apply our method to the real data.
Presentation slides [.pdf]

Hyungsook Tak (Harvard)
10 Mar 2015
1pm EST
Time Delay Challenge

24 Mar 2015
1pm EDT
remotely from McMaster
Bayesian Mass Estimates of the Galaxy: incorporating incomplete data
Abstract: The total mass and cumulative mass profile M(r) of the Milky Way Galaxy are two of the most fundamental properties of the Galaxy. To estimate these properties, we rely on the kinematic information of satellites which orbit the Galaxy, such as globular clusters and dwarf galaxies. However, transforming this data accurately into a mass profile is not a trivial problem, because the complete 3D velocity and position vectors of objects are sometimes unavailable. We have developed a Bayesian method to deal with incomplete data effectively. Our method uses a hybrid-Gibbs sampler that treats the unknown velocity components of satellites as parameters in the model. We explore the effectiveness of this method using simulated data, and then apply our method to the Milky Way using velocity and position data of globular clusters and dwarf galaxies. We find that in general, missing velocity components have little effect on the total mass estimate for each of four different models.
Presentation slides [.pdf]

Ian Czekala (CfA)
14 Apr 2015
1pm EDT
Robust Spectroscopic Inference with Imperfect Models
Abstract: We present a modular, extensible framework for the spectroscopic inference of physical parameters based on synthetic model spectra. The subtraction of an imperfect model from a continuously sampled spectrum introduces covariance between adjacent datapoints (pixels) into the residual spectrum. In the limit of high signal-to-noise data with large spectral range that is common for stellar parameter estimation, that covariant structure can bias the parameter determinations. We have designed a likelihood function formalism to account for the structure of the covariance matrix, utilizing the machinery of Gaussian process kernels. We specifically address the common problem of mismatches in model spectral line strengths (with respect to data) due to intrinsic model imperfections (e.g., in the atomic or molecular data, or radiative transfer treatment) by developing a novel local covariance kernel framework that identifies and self-consistently downweights pathological spectral line "outliers." By fitting multiple spectra in a hierarchical manner, these local kernels provide a mechanism to learn about and build data-driven corrections to synthetic model spectral libraries. The application of this method, implemented as a freely available open source code, is demonstrated by fitting the high resolution optical (V-band) spectrum of WASP-14, an F5 dwarf with a transiting exoplanet, and the moderate resolution near-infrared (K-band) spectrum of Gliese 51, an M5 dwarf.
arXiv:1412.5177
Starfish
Presentation Slides [url]

David Stenning (UCIrvine)
21 Apr 2015
1pm EDT / 6pm GMT
remotely from UCI
Astrostatistical Analysis in Solar and Stellar Physics
Presentation slides
Abstract: This talk focuses on developing statistical models and methods to address data-analytic challenges in astrostatistics---a growing interdisciplinary field fostering collaborations between statisticians and astrophysicists. The astrostatistics projects we tackle can be divided into two main categories: modeling solar activity and Bayesian analysis of stellar evolution. These categories from Part I and Part II of this talk, respectively.
The first line of research we pursue involves classification and modeling of evolving solar features. Advances in space-based observatories are increasing both the quality and quantity of solar data, primarily in the form of high-resolution images. To analyze massive streams of solar image data, we develop a science-driven dimension reduction methodology to extract scientifically meaningful features from images. This methodology utilizes mathematical morphology to produce a concise numerical summary of the magnetic flux distribution in solar "active regions" that (i) is far easier to work with than the source images, (ii) encapsulates scientifically relevant information in a more informative manner than existing schemes (i.e., manual classification schemes), and (iii) is amenable to sophisticated statistical analyses.
In a related line of research, we perform a Bayesian analysis of the solar cycle using multiple proxy variables, such as sunspot numbers. We take advantage of patterns and correlations among the proxy variables to model solar activity using data from proxies that have become available more recently, while also taking advantage of the long history of observations of sunspot numbers. This model is an extension of the Yu et al. (2012) Bayesian hierarchical model for the solar cycle that used the sunspot numbers alone. Since proxies have different temporal coverage, we devise a multiple imputation scheme to account for missing data. We find that incorporating multiple proxies reveals important features of the solar cycle that are missed when the model is fit using only the sunspot numbers.
In Part II of this talk we focus on two related lines of research involving Bayesian analysis of stellar evolution. We first focus on modeling multiple stellar populations in star clusters. It has long been assumed that all star clusters are comprised of single stellar populations---stars that formed at roughly the same time from a common molecular cloud. However, recent studies have produced evidence that some clusters host multiple populations, which has far-reaching scientific implications. We develop a Bayesian hierarchical model for multiple-population star clusters, extending earlier statistical models of stellar evolution (e.g., van Dyk et al., 2009; Stein et al., 2013). We also devise an adaptive Markov chain Monte Carlo algorithm to explore the complex posterior distribution. We use numerical studies to demonstrate that our method can recover parameters of multiple-population clusters, and also show how model misspecification can be diagnosed. Our model and computational tools are incorporated into an open-source software suite known as BASE-9. We also explore statistical properties of the estimators and determine that the influence of the prior distribution does not diminish with larger sample sizes, leading to non-standard asymptotics.
In a final line of research, we present the first-ever attempt to estimate the carbon fraction of white dwarfs. This quantity has important implications for both astrophysics and fundamental nuclear physics, but is currently unknown. We use a numerical study to demonstrate that assuming an incorrect value for the carbon fraction leads to incorrect white-dwarf ages of star clusters. Finally, we present our attempt to estimate the carbon fraction of the white dwarfs in the well-studied star cluster 47 Tucanae.

Vasileios Stampoulis (Imperial)
28 Apr 2015
1pm EDT / 6pm GMT
remotely from Imperial
Classifying Galaxies using a Data-driven approach
Abstract: [.pdf]
Spectroscopy has been utilised in identifying the main power source in active galaxies. Based on the different mechanisms that excite the gas that exists inside the galaxies (and which, as a result of those mechanisms, glows in different wavelengths), the Galaxies may be separated into 4 categories: the AGN (Active Galactic Nuclei), which are divided into LINERs and Seyferts, the HII region-like galaxies(star forming galaxies) and the Composite galaxies (their spectra contain significant contributions from both AGN and star-forming).
Four emissions intensities ratios are being used as means to classify different galaxies; log(NII/Halpha), log(SII/Halpha), log(OI/Halpha) and log(OIII/Halpha). Both physical and empirical models have been developed in order to propose a classifcation scheme based on those ratios. However, the exact demarcation between star-forming galaxies and AGN is subject to considerable uncertainty and the increasing flow of data from massive new surveys shows the inadequacy of the existing scheme.
In this project we utilise a data-driven approach in order to build a density estimation model that will describe accurately the distributions of the 4 different classes of galaxies. Identifying and parametrizing the distributions of the pure star-forming Galaxies and the pure AGN would provide a solid quantitative tool in order to explore further scientific problems.
Presentation slides [.pdf]
Murray Aitkin (Melbourne)
Monday
04 May 2015
4pm EDT
SciCen 705
Superclusters and voids in the galaxies (revisited)
Presentation slides [.pdf]
Abstract: The 1990 JASA paper by Kathryn Roeder on the analysis of the recession velocities of 82 galaxies led to a major sequence of papers on Bayesian methods for determining the number of components in a finite mixture of normal densities. Most methods computed the integrated likelihoods for each number of components and converted the integrated likelihoods to posterior model probabilities. Different analyses of the galaxy velocity data by major groups gave mystifyingly different conclusions.
This talk revisits the data, and gives a grapical and a new (and controversial) Bayesian analysis which concludes that there is strong evidence for three components, weak evidence for four, and no evidence for more than four. The new analysis replaces the integrated likelihood for each model by the posterior distribution of the model likelihood.
References
Postman, M.J., Huchra, J.P. and Geller, M.J. (1986) Probes of large-scale structures in the Corona Borealis region. The Astronomical journal 92, 1238-1247. [The astronomical data]
Roeder, K. (1990) Density estimation with confidence sets exemplified by superclusters and voids in the galaxies. JASA 85, 617-624. [The first statistical analysis]
Aitkin, M. (2001) Likelihood and Bayesian analysis of mixtures. Statistical Modelling 1, 287-304. [A comparison of the different frequentist and Bayesian conclusions]
Aitkin, M. (2010) Statistical Inference: an Integrated Bayesian/Likelihood Approach. CRC Press, Boca Raton. [The model comparison approach, with the galaxy application pp. 210-221]
Aitkin, M. (2011) How many components in a finite mixture? pp. 277-292 In Mixtures: Estimation and Applications. eds. K.L. Mengersen, C.P. Robert and D.M. Titterington. Wiley, Chichester
Gelman, A., Robert, C.P. and Rousseau, J. (2013) Inherent difficulties of non-Bayesian likelihood inference, as revealed by an examination of a recent book by Aitkin. Statistics and Risk Modeling 30, 105-120. [A confused attack on the approach]
Aitkin, M. (2013) Comments on the review of Statistical Inference. Statistics and Risk Modeling 30, 121-132. [A spirited defense of the approach]

Sara Algeri (Imperial)
19 May 2015
1pm EDT / 6pm BST
remotely from Imperial
Statistical Issues in the Search for Particle Dark Matter
Abstract: Non-standard hypothesis tests commonly arise in the the search for new physics. For example, parameters may lie on the boundary of the parameter space, nuisance parameters may only be defined under the alternative model, or researchers may want to compare non-nested models. Although these issues have been addressed since the early days of modern statistics, they pose significant challenges in practice. Testing separate families of hypotheses, in particular, has not yet found a theoretically satisfactory solution that is easy to implement and does not require any prior assumption.
This talk proposes, validates, and compares a set of new methods that aim to address these issues. In particular, we show that an opportune reformulation of a non-nested models hypothesis test allows us to use well-known results in asymptotic theory and provides a simple and ready-to-use solution. The proposed methods rely on the Likelihood Ratio Test (LRT) and an alternative test introduced in 2005 by Pilla et al. that is based on the Score function. Both approaches reduce the problem of testing non-nested hypotheses to finding an approximation for excursion probabilities of the form P(sup{Yt}>c), with Yt being either a Chi-square or a Gaussian process. The main difference between the two solutions is that the method based on the LRT formalizes the problem in terms of excursion sets, whereas the Score-based method provides an approximation based on tubes formulae. It will be shown that both methodologies exhibit advantages and suffer limitations, both in terms of computation and, more importantly, in terms of the specific conditions associated with the models being tested.
Presentation slides [.pdf]
Anna Barnacka (CfA)
16 Jun 2015
1pm EDT
SciCen 706
Resolving the High Energy Universe with Strong Gravitational Lensing
Gravitational lensing is a potentially powerful tool for elucidating the origin of gamma-ray emission from distant sources. Cosmic lenses magnify the emission and produce time delays between mirage images. Gravitationally-induced time delays depend on the position of the emitting regions in the source plane. Well sampled gamma-ray light curves provide a measure of the time delay and thus a new route to resolving the sources. We have investigated three methods of time-delay estimation from unresolved light curves; the Autocorrelation Function, the Double Power Spectrum, and the Maximum Peak Method. As a prototypical example of the power of lensing combined with long, uniformly sampled light curves provided by the Fermi satellite, we investigated the spatial origin of gamma-ray flares from PKS 1830-211.
Presentation slides: [.key.zip] [.pdf]

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
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
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
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
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.