How to Find More Supernovae with Less Work: Object ClassificationTechniques for Difference Imaging

PDF Version Also Available for Download.

Description

We present the results of applying new object classificationtechniques to difference images in the context of the Nearby SupernovaFactory supernova search. Most current supernova searches subtractreference images from new images, identify objects in these differenceimages, and apply simple threshold cuts on parameters such as statisticalsignificance, shape, and motionto reject objects such as cosmic rays,asteroids, and subtraction artifacts. Although most static objectssubtract cleanly, even a very low false positive detection rate can leadto hundreds of non-supernova candidates which must be vetted by humaninspection before triggering additional followup. In comparison to simplethreshold cuts, more sophisticated methods such as Boosted DecisionTrees, Random Forests, ... continued below

Creation Information

Bailey, Stephen; Aragon, Cecilia; Romano, Raquel; Thomas, RollinC.; Weaver, Benjamin A. & Wong, Daniel May 2, 2007.

Context

This article is part of the collection entitled: Office of Scientific & Technical Information Technical Reports and was provided by UNT Libraries Government Documents Department to Digital Library, a digital repository hosted by the UNT Libraries. More information about this article can be viewed below.

Who

People and organizations associated with either the creation of this article or its content.

Publisher

Provided By

UNT Libraries Government Documents Department

Serving as both a federal and a state depository library, the UNT Libraries Government Documents Department maintains millions of items in a variety of formats. The department is a member of the FDLP Content Partnerships Program and an Affiliated Archive of the National Archives.

Contact Us

What

Descriptive information to help identify this article. Follow the links below to find similar items on the Digital Library.

Description

We present the results of applying new object classificationtechniques to difference images in the context of the Nearby SupernovaFactory supernova search. Most current supernova searches subtractreference images from new images, identify objects in these differenceimages, and apply simple threshold cuts on parameters such as statisticalsignificance, shape, and motionto reject objects such as cosmic rays,asteroids, and subtraction artifacts. Although most static objectssubtract cleanly, even a very low false positive detection rate can leadto hundreds of non-supernova candidates which must be vetted by humaninspection before triggering additional followup. In comparison to simplethreshold cuts, more sophisticated methods such as Boosted DecisionTrees, Random Forests, and Support Vector Machines provide dramaticallybetter object discrimination. At the Nearby Supernova Factory, we reducedthe number of non-supernova candidates by a factor of 10 while increasingour supernova identification efficiency. Methods such as these will becrucial for maintaining a reasonable false positive rate in the automatedtransient alert pipelines of upcoming projects such as PanSTARRS andLSST.

Source

  • Journal Name: Astrophysical Journal; Journal Volume: 665; Journal Issue: 2; Related Information: Journal Publication Date: 08/2007

Language

Item Type

Identifier

Unique identifying numbers for this article in the Digital Library or other systems.

  • Report No.: LBNL--62659
  • Grant Number: DE-AC02-05CH11231
  • Grant Number: NSF:AST-0407297, 0087344, AND0426879
  • DOI: 10.1086/519832 | External Link
  • Office of Scientific & Technical Information Report Number: 923360
  • Archival Resource Key: ark:/67531/metadc896676

Collections

This article is part of the following collection of related materials.

Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

Office of Scientific and Technical Information (OSTI) is the Department of Energy (DOE) office that collects, preserves, and disseminates DOE-sponsored research and development (R&D) results that are the outcomes of R&D projects or other funded activities at DOE labs and facilities nationwide and grantees at universities and other institutions.

What responsibilities do I have when using this article?

When

Dates and time periods associated with this article.

Creation Date

  • May 2, 2007

Added to The UNT Digital Library

  • Sept. 27, 2016, 1:39 a.m.

Description Last Updated

  • Oct. 3, 2017, 2 p.m.

Usage Statistics

When was this article last used?

Congratulations! It looks like you are the first person to view this item online.

Interact With This Article

Here are some suggestions for what to do next.

Start Reading

PDF Version Also Available for Download.

Citations, Rights, Re-Use

Bailey, Stephen; Aragon, Cecilia; Romano, Raquel; Thomas, RollinC.; Weaver, Benjamin A. & Wong, Daniel. How to Find More Supernovae with Less Work: Object ClassificationTechniques for Difference Imaging, article, May 2, 2007; Berkeley, California. (digital.library.unt.edu/ark:/67531/metadc896676/: accessed October 23, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.