RAPID, MACHINE-LEARNED RESOURCE ALLOCATION: APPLICATION TO HIGH-REDSHIFT GAMMA-RAY BURST FOLLOW-UP-Reference-Cited by-同舟云学术

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RAPID, MACHINE-LEARNED RESOURCE ALLOCATION: APPLICATION TO HIGH-REDSHIFT GAMMA-RAY BURST FOLLOW-UP

Published:2012-02-03 Issue:2 Volume:746 Page:170
ISSN:0004-637X
Container-title:The Astrophysical Journal
language:
Short-container-title:ApJ

Author:

Morgan A. N.,Long James,Richards Joseph W.,Broderick Tamara,Butler Nathaniel R.,Bloom Joshua S.

Publisher

American Astronomical Society

Subject

Space and Planetary Science,Astronomy and Astrophysics

Link

http://stacks.iop.org/0004-637X/746/i=2/a=170/pdf

Reference222 articles.

1. Intrinsic spectra and energetics of BeppoSAX Gamma–Ray Bursts with known redshifts

2. How to Find More Supernovae with Less Work: Object Classification Techniques for Difference Imaging

3. Gamma‐Ray Burst Intensity Distributions

4. Gamma‐Ray Bursts versus Quasars: Lyα Signatures of Reionization versus Cosmological Infall

Cited by 12 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Gamma-Ray Bursts as Distance Indicators by a Statistical Learning Approach;The Astrophysical Journal Letters;2024-05-24

2. A Comprehensive Statistical Study of Gamma-Ray Bursts;The Astrophysical Journal;2020-04-01

3. Memberships, distance and proper-motion of the open cluster NGC 188 based on a machine learning method;Astrophysics and Space Science;2018-10-11

4. Memberships of the Open Cluster NGC 6405 Based on a Combined Method: Gaussian Mixture Model and Random Forest;The Astronomical Journal;2018-08-23

5. Machine-z: rapid machine-learned redshift indicator forSwiftgamma-ray bursts;Monthly Notices of the Royal Astronomical Society;2016-03-08

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