|
| | | | |
| | | |
| Stat |
Members: 3645
Articles: 2'500'096
Articles rated: 2609
19 April 2024 |
|
| | | | |
|
Article overview
| |
|
|
Machine Learning Based Real Bogus System for HSC-SSP Moving Object Detecting Pipeline | | Hsing-Wen Lin
; Ying-Tung Chen
; Jen-Hung Wang
; Shiang-Yu Wang
; Fumi Yoshida
; Wing-Huen Ip
; Satoshi Miyazaki
; Tsuyoshi Terai
; | | Date: |
21 Apr 2017 | | Abstract: | The machine learning techniques are widely applied in many modern optical sky
surveys, i.e. Pan-STARRS1, PTF/iPTF and Subaru/Hyper Suprime-Cam survey, to
reduce the human intervention for data verification. In this study, we have
established a machine learning based real-bogus system to reject the false
detections in the HSC-SSP source catalog. Therefore the HSC-SSP moving object
detection pipeline can operate more effectively due to the much less false
positives inputs. To train the real-bogus system, we use the stationary sources
as the real training set and the ’flagged’ data as the bogus set. The training
set contains 49 features, which, in majority, are the photometry measurements
and shape moments generating from the HSC image reduction pipeline (hscPipe).
Our system can reach a true positive rate (tpr) ~ 96% with a false positive
rate (fpr) ~ 1% or tpr ~ 99% at fpr ~ 5%. Therefore we conclude that the
stationary sources are decent real training samples, and using photometry
measurements and shape moments can reject the false positives effectively. | | Source: | arXiv, 1704.6413 | | Services: | Forum | Review | PDF | Favorites | |
|
|
No review found.
Did you like this article?
Note: answers to reviews or questions about the article must be posted in the forum section.
Authors are not allowed to review their own article. They can use the forum section.
browser Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; ClaudeBot/1.0; +claudebot@anthropic.com)
|
| |
|
|
|
|
News, job offers and information for researchers and scientists:
| |
REGISTER