| |
 |
| Paper: |
Transfer Learning in Large Spectroscopic Surveys |
| Volume: |
532, ASTRONOMICAL DATA ANALYSIS SOFTWARE AND SYSTEMS XXX |
| Page: |
235 |
| Authors: |
Podsztavek, O.; Škoda, P.; Tvrdík, P. |
| Abstract: |
Transfer learning is a machine learning method that can reuse knowledge across spectroscopic archives with different distributions of observations.
We applied transfer learning based on a convolutional neural network to spectra from Large Sky Area Multi-Object Fiber Spectroscopic Telescope and Sloan Digital Sky Survey archives.
Taking advantage of known quasars in LAMOST DR5 version 3, we wanted to discover yet unseen quasars in SDSS DR14.
Our transfer learning approach reaches 99.6% precision and 98.9% recall.
We found examples of quasars previously classified as stars. |
|
|
|
|
|
