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.


Xixi Yu (Imperial) 9 Oct 2018 Imperial 
 TBD


Yang Chen (UMich) 23 Oct 2018 UMich 
 cstat


Vinay Kashyap, Mark Weber, & Aneta Siemiginowska (CfA) 30 Oct 2018 SciCen 706 
 Statistics: Trick or Treat?
 Abstract: The Feigelson List.


David Jones (TAMU) 13 Nov 2018 TAMU 
 Exoplanets


Thomas Lee (UC Davis) 27 Nov 2018 UCD 
 TBD


Hyungsook Tak (Notre Dame) 11 Dec 2018 ND 
 TBD


David Stenning (Imperial) 22 Jan 2019 Imperial 
 TBD


Arturo Avelino (CfA) 5 Feb 2019 SciCen 706 
 TBD


Sara Algeri (UMinnesota) 19 Feb 2019 UMinn 
 TBD


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





