Alapfogalmak
A deep neural network can obtain unbiased photometry for saturated stars with a median dispersion of only 0.037 mag, significantly better than the standard ASAS-SN pipeline.
Kivonat
The authors developed a deep neural network (DNN) to obtain photometry of saturated stars in the All-Sky Automated Survey for Supernovae (ASAS-SN). The DNN can obtain unbiased photometry for stars from g ≃ 4 to 14 mag with a dispersion (15%-85% 1σ range around median) of 0.12 mag for saturated (g < 11.5 mag) stars. More importantly, the light curve of a non-variable saturated star has a median dispersion of only 0.037 mag, which is significantly better than the standard ASAS-SN pipelines.
The authors trained the DNN using a dataset of approximately 332,000 postage stamp images of stars ranging from g ≃ 3 to 15 mag. The network was able to learn the sensor-specific behavior for stars of different levels of saturation and predict their true brightness.
The DNN light curves of many bright variable stars, such as Miras, Cepheids, and eclipsing binaries, are dramatically better than the results from the standard ASAS-SN pipeline. While the network was trained on data from only one ASAS-SN camera, initial experiments suggest that it can be used for any camera and the older ASAS-SN V band data as well.
The dominant problems seem to be associated with correctable issues in the ASAS-SN data reduction pipeline for saturated stars, rather than limitations of the DNN itself. The method is now publicly available as a light curve option on ASAS-SN Sky Patrol v1.0.
Statisztikák
The dispersion (15%-85% 1σ range around median) of the DNN photometry is 0.12 mag for saturated (g < 11.5 mag) stars.
The median dispersion of the DNN light curves for non-variable saturated stars is 0.037 mag.
Idézetek
"The DNN light curves are, in many cases, spectacularly better than provided by the standard ASAS-SN pipelines."
"The dominant problems seem to be associated with correctable issues in the ASAS-SN data reduction pipeline for saturated stars more than the DNN itself."