Presentations 

David Stenning (Imperial) 19 Jul 2018 2pm3pm EDT SSXG Operations Center at CfA 
 Classification and Modeling of Evolving Solar Features
 Abstract:
Advances in spacebased observatories are increasing both the quality and quantity of solar data, primarily in the form of highresolution images. The goal of these observatories is to better understand and predict space weather. To analyze massive streams of solar image data, we have developed a sciencedriven dimension reduction methodology to extract scientifically meaningful features from images. Adopting a sciencedriven approach, as opposed to a solely blackbox algorithmic approach, enables interpretable secondary datadriven analyses of complex phenomena, such as the evolution of magnetic active regions. The methodology utilizes mathematical morphology to produce a concise numerical summary of the magnetic flux distribution in active regions that (i) is far easier to work with than the source images, (ii) encapsulates scientifically relevant information in a much more informative manner than existing schemes (i.e. manual classification schemes), and (iii) is amenable to sophisticated statistical analyses.
 Presentation slides [.pdf]


Group 4 Sep 2018 Noon EDT SciCen 706 
 Organizational & EBASCS


Cora Dvorkin (HU) 11 Sep 2018 Noon EDT SciCen 706 
 Inverse Problems in Early Universe Cosmology
 Abstract: Cosmological observations have provided us with answers to ageold questions, involving the age, geometry, and composition of the universe. However, there are profound questions that still remain unanswered. I will describe ongoing efforts to shed light on some of these questions. In this talk, I will explain how we can use measurements of the Cosmic Microwave Background and the largescale structure of the universe to reconstruct the detailed physics of much earlier epochs, when the universe was only a tiny fraction of a second old. I will address this inverseproblem reconstruction from a Bayesian perspective.


Andrea Sottosanti (Imperial) 2 Oct 2018 Imperial 
 Astronomical source detection and background separation via hierarchical Bayesian nonparametric mixtures
 Abstract:
We propose an innovative approach based on Bayesian nonparametric methods to the signal extraction of astronomical sources in gammaray count maps under the presence of a strong background contamination. Our model simultaneously induces clustering on the photons using their spatial information and gives an estimate of the number of sources, while separating them from the irregular signal of the background component that extends over the entire map. From a statistical perspective, the signal of the sources is modeled using a Dirichlet Process mixture, that allows to discover and locate a possible infinite number of clusters, while the background component is completely reconstructed using a new flexible Bayesian nonparametric model based on bspline basis functions. The resultant can be then thought of as a hierarchical mixture of nonparametric mixtures for flexible clustering of highly contaminated signals. We provide also a Markov chain Monte Carlo algorithm to infer on the posterior distribution of the model parameters which does not require any tuning parameter, and a suitable postprocessing algorithm to quantify the information coming from the detected clusters. Results on different datasets confirm the capacity of the model to discover and locate the sources in the analysed map, to quantify their intensities and to estimate and account for the presence of the background contamination.
 Presentation slides [.pdf]


Xixi Yu (Imperial) 23 Oct 2018 Imperial 
 Multistage Anslysis on Solar Spectral Analyses with Uncertainties in Atomic Physical Models
 Abstract: Information about the physical properties of astrophysical objects cannot be measured directly but is inferred by interpreting spectroscopic observations in the context of atomic physics calculations. A critical component of this analysis is understanding how uncertainties in the underlying atomic physics propagates to the uncertainties in the inferred plasma parameters.
Instead of using the standard approach, a common strategy deployed by the astrophysicists, that treats the uncertainty as fixed and known and obtains the bestfit values of the parameters, we propose a multistage analysis to prevent underestimation of the error bars on the model parameters and increase the accuracy of the analysis results. Four methods for a twostage analysis are outlined, the standard method, multiple imputation, the pragmatic and the fully Bayesian methods. A case study on Fe XIII is discussed where two different priors, discrete uniform and Gaussian approximation via principal component analysis prior, are deployed.
 Presentation slides [.pdf]


Yang Chen (UMich) 30 Oct 2018 UMich 
 A second look at cstat
 Abstract: After decades of least chisquares fitting and goodnessoffit, the Cstat has been gaining popularity in the astrophysics community for model fitting and assessment of goodnessoffit. In this work, we study the statistical properties of the Cstat and explore lowerresolution Cstat fitting and testing, which potentially improves statistical and computational efficiency. This is ongoing joint work with CHASC team.


David Jones (TAMU) 13 Nov 2018 TAMU 
 Exoplanet detection: some statistical challenges
 Abstract: The radial velocity (RV) technique is one of the
two main approaches for detecting planets outside our solar system. The
method works by detecting the Doppler shift resulting from the motion of
a host star caused by an orbiting planet. Unfortunately, this Doppler
signal is typically contaminated by various "stellar activity"
phenomena, such as dark spots on the star surface. This makes it
difficult to determine if a planet is really present or not.
Last time I presented a Gaussian process framework for separating planet
RV signals from stellar activity. In this talk, I will review the key
points of the method and discuss current statistical challenges and
opportunities for generalizing and improving the approach. I will also
discuss related computational challenges in exoplanet detection.
 Presentation slides [.pdf]


Thomas Lee (UC Davis) 27 Nov 2018 UCD 
 Change Point Detection for Poisson Time Series Images with Applications to Astronomy and Astrophysics
 Presentation slides [.pdf]


Hyungsook Tak (Notre Dame) 11 Dec 2018 ND 
 Time Delay Lens Modeling Challenge for the Hubble Constant Estimation
 Abstract: The Hubble constant is a core cosmological parameter that represents the current expansion rate of the Universe. One way to infer this quantity is to use strong gravitational lensing, i.e., an effect that multiple images of an astronomical object (e.g., a quasar) appear in the sky. This effect occurs when the trajectories of the light (from the object to the Earth) are bent by a strong gravitational field of an intervening galaxy. Strong gravitational lensing produces two types of the data; (i) multiple brightness time series data of the gravitationallylensed images and (ii) pixelwise image data of the lens and lensed object. The former is used to infer time delays between the arrival times of the multiplylensed images (arXiv 1602.01462 ) and the latter is used to estimate gravitational potential that the lensed images pass through (arXiv 1801.01506 ). These two components are used to infer the Hubble constant via physical equations. In this talk, I explain how we infer the Hubble constant using the relationship among these three components, i.e., time delays, gravitational potential, and the Hubble constant. I will also describe the performance of this approach during the first stage of a blind competition, called the Time Delay Lens Modeling Challenge.
 Presentation slides [.pdf]

Vinay Kashyap (CfA), Katy McKeough (HU), Luis Campos (HU), et al. 29 Jan 2019 SciCen 706 
 Introduction to Highenergy Astronomy Data for Statisticians
 We will describe what highenergy datasets look like using the example of the Chandra Xray Observatory. We will then highlight some of the problems our group has tackled in the past, and focus in detail on two current projects: (i) to isolate and locate extended sources in posterior draw images, and (ii) to probabilistically disentangle photons from overlapping sources using spatial, spectral, and temporal variability information.
 Chandra archive: cda.harvard.edu/chaser [url]
 Presentation slides: Kashyap, McKeough, Campos [.pdf]


Paul Baines (Wise.io) 5 Feb 2019 Berkeley 
 The Colorful Stars and the Black Box: Bayesian Analysis of Stellar Populations
 Abstract:
Many modern statistical applications involve noisy observations of an underlying process that can best be described by a complex deterministic system. In astrophysics these systems often involve the solution of partial differential equations representing the best available understanding of the underlying physical processes. Statistical computation in such settings is hampered by the use of lookup tables or expensive `blackbox' function evaluations.
The estimation of properties of stellar populations provides an example of statistical modeling with such a `lookup table' likelihood. The mapping between the physical parameters and the dataspace cannot be solved analytically and is represented as a series of lookup tables. In this context, we present a flexible hierarchical model for analyzing stellar populations. By utilizing the structure of the posterior distribution we construct efficient data augmentation schemes which create a robust sampling procedure. The performance of various sampling schemes are presented, together with the results of applying our model to a wellstudied dataset.


Gabriel Collin (MIT) 12 Feb 2019 SciCen 706 
 Simulating light in large volume detectors using Metropolis Light Transport
 Abstract: In gigaton scale neutrino detectors, such as the IceCube experiment, interaction products are detected by the Cherenkov radiation emitted by their passage through the detector medium. Simulating this propagation of light is traditionally approached through ray tracing. This is complicated by the sparsity of the detector: the vast majority of light rays are scattered and absorbed by the detector medium, with only a tiny fraction finding their way to a light sensitive element. In this presentation, I develop an alternative method, based on the Metropolis light transport algorithm used in the CGI industry. This method poses the problem as a classical path integral, and samples only the paths of light rays that end on a light sensitive element using a Markov chain MonteCarlo. This yields a significant performance increase compared to ray tracing when simulating the timing distribution of light detected by a photosensitive element. The general concept behind this method can be widely applied, and I discuss some potential applications to other problem areas in physics and astronomy.
 Presentation slides [.pdf]


Daniel Muthukrishna (Cambridge) 19 Feb 2019 Cambridge 
 Realtime classification of explosive transients using deep recurrent neural networks
 Abstract:
Astronomical transients are stellar objects that become temporarily brighter on various timescales and have led to some of the most significant discoveries in cosmology and astronomy. New and upcoming widefield surveys such as the Zwicky Transient Facility (ZTF) and the Large Synoptic Survey Telescope (LSST) will record millions of multiwavelength transient alerts each night. To meet this demand, we have developed a novel machine learning approach, RAPID (Realtime Automated Photometric Identification using Deep learning), that automatically classifies transients as a function of time. Using a deep recurrent neural network (RNN) with Gated Recurrent Units (GRUs), we are able to quickly classify multichannel, sparse, time series datasets into 12 different astrophysical types. The classification accuracy improves over the lifetime of the transient as more photometric data becomes available. In this talk, I will explain the main parts of our deep neural network architecture and describe our approach's classification performance on simulated and real data streams.
 Presentation slides [.pdf]


Di Zhang (UCIrvine) 5 Mar 2019 UCI 
 New populationbased MCMC method


Sara Algeri (UMinnesota) 19 Mar 2019 UMinn 
 TBD


Arturo Avelino (CfA) postponed/TBR 
 TBD


David Stenning (Imperial) postponed/TBR Imperial 
 TBD


Vinay Kashyap, Mark Weber, & Aneta Siemiginowska (CfA) postponed/TBR SciCen 706 
 The Feigelson List





