Digging Deeper: Algorithms for Computationally-Limited Searches in Astronomy

Workshop Overview:

In several areas of astronomy the sensitivity of our searches for some types of signals is computationally limited. That is, either faster computers or better algorithms would lead to more discoveries in the same datasets. This is certainly true for many cases in gravitational-wave data analysis. Improved algorithms are also critical in the rapidly developing field of time domain astronomy, where transient signals from a variety of interesting astrophysical phenomena, ranging from the Solar System to cosmology and extreme relativistic objects, must be discerned in massive data streams.

We plan to limit our investigations to time series, which could be light curves of sources detected in multiple images, or output from a detector like LIGO. The challenge has two related sides:

detection of faint and/or transient signals, and
their classification/characterization, which informs the detection process through a design of optimal detection algorithms,and is essential for the follow-up prioritization of the detected signals and events.

Our technical goal is to develop a few realistic, benchmark problems on which the methods can be compared, keeping in mind computational resources and available architectures. In practice, we plan to define 2 or 3 specific, timely, astrophysically motivated challenges to guide our thinking and serve as methodological testbeds.

Our aim is to compile a practical guide to the best available methods of attacking these problems. Our work should lead to an improved understanding and useful rules of thumb regarding the advantages and scaling properties of different methods, which can be carried over to other data analysis challenges.

Team Leads

George Djorgovski

Caltech
Curt Cutler

Jet Propulsion Laboratory
Bruce Elmegreen

Cornell University

Guillermo Cabrera — University of Chile
Curt J. Cutler — JPL
Raffaele D'Abrusco — Harvard-Smithsonian Center for Astrophysics
Rosanne Di Stefano — Harvard-Smithsonian
George Djorgovski — Caltech
Ciro Donalek — Caltech
Bruce G. Elmegreen — IBM Research Division
Matthew J. Graham — California Institute of Technology
Vinay Kashyap — SAO
Badri Krishnan — Albert Einstein Institute
Joseph Lazio — JPL
Giuseppe Longo — University Federico II
Ashish Mahabal — Caltech
Frank J. Masci — Caltech
Walter Max-Moerbeck — Caltech
Baback Moghaddam — Jet Propulsion Laboratory
Pavlos Protopapas — Harvard
Umaa D. Rebbapragada — JPL
John A. Rice — University of California, Berkeley
Graca Rocha — JPL/Caltech
Jeff D. Scargle — NASA Ames Research Center
David R. Thompson — Jet Propulsion Laboratory
Mike Turmon — JPL/Caltech
Michele Vallisneri — Jet Propulsion Laboratory
Kiri L. Wagstaff — Jet Propulsion Laboratory
Yan Xu — Microsoft

Presentations

Speaker	Affiliation	Presentation
Badri Krishnan	Albert Einstein Institute	Gravitational Wave Data Analysis (2 MB .pdf)
Jeff Scargle	NASA Ames	New Developments in Time Series Analysis (7 MB .pdf)
Ashish Mahabal	Caltech	Automated Classification of Transients (9 MB .pdf)
Pavlos Protopapas	CfA	Machine Learning and Statistics Applications (18 MB .pdf)
George Djorgovski	Caltech	The Broader Context: Exploration of the time domain of the observable parameter space, using synoptic sky surveys (3.3 MB .pdf)
Curt J. Cutler	JPL	Presenting areas to be reviewed and proposed benchmark problems (545 KB .pdf)
Giuseppe Longo	University Federico II	Computationally limited tasks in astronomy? (540 KB .pdf)
Kiri L. Wagstaff	JPL	Machine Learning Methods for Astronomy (425 KB .pdf)
John A. Rice	UC Berkeley	Heirarchical Resolution (292 KB .pdf)
Baback Moghaddam	JPL	What can Biostatistics do for Time-Domain Astronomy (1.6 MB .pdf)
Rosanne Di Stefano	Harvard-Smithsonian	Extracting Periods from Binary-Lens Events A Slightly Modified Lomb‐Scargle Approach (278 KB .pdf)
Ciro Donalek	Caltech	Objects Classification in Synoptic Sky Surveys: Contextual and External Information (5.6 MB .pdf)
David Thompson	JPL	Hidden Markov Models in 10 Seconds (198 KB .pdf)
Raffaele D'Abrusco	CfA	The Exploration of Multi-Wavelength Astronomical Datasets: The Case of AGNS in the Chandra Source Catalog and Unsupervised Clustering (1.5 MB .pdf)
Walter Max-Moerbeck	Caltech	What is the Connection Between Radio and Gamma-Ray Emission in Blazars? (3.89 MB .pdf)
Guillermo Cabrera	Univ. of Chile	Automated Detection of Objects Based on Sérsic Profiles (3.87 MB .pdf)
Jeff Scargle	NASA Ames	A Few Comments on Time Series Representations (1.2 MB .pdf)