Digging Deeper: Algorithms for Computationally-Limited Searches in Astronomy

June 7-10, 2011
California Institute of Technology - Pasadena, CA 91125

Final Report

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.

Workshop Participants:

  • 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

Short Course Presentations

Badri Krishnan
Albert Einstein Institute

Gravitational Wave Data Analysis
(2 MB .pdf)


Jeff Scargle

New Developments in Time Series Analysis
(7 MB .pdf)


Ashish Mahabal

Automated Classification of Transients
(9 MB .pdf)


Pavlos Protopapas

Machine Learning and Statistics Applications
(18 MB .pdf)


Workshop Presentations

George Djorgovski

The Broader Context: Exploration of the time domain of the observable parameter space, using synoptic sky surveys
Or: Real-time mining of Petascale data streams
(3.3 MB .pdf)

Curt J. Cutler

Bruce Elmegreen

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

Machine Learning Methods for Astronomy
(425 KB .pdf)

John A. Rice
UC Berkeley

Heirarchical Resolution
(292 KB .pdf)

Baback Moghaddam

What can Biostatistics do for Time-Domain Astronomy
(1.6 MB .pdf)

Rosanne Di Stefano

Extracting Periods from Binary-Lens Events A Slightly Modified Lomb‐Scargle Approach
(278 KB .pdf)

Ciro Donalek

Objects Classification in Synoptic Sky Surveys: Contextual and External Information
(5.6 MB .pdf)

David Thompson

Hidden Markov Models in 10 Seconds
(198 KB .pdf)

Raffaele D'Abrusco

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

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

A Few Comments on Time Series Representations
(1.2 MB .pdf)