Accurate Photometry of Saturated Stars Using Deep Neural Networks

Core Concepts

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.

Abstract

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.

Stats

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.

Quotes

"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."

Key Insights Distilled From

Photometry of Saturated Stars with Machine Learning

by Dominek Wine... at arxiv.org 04-26-2024

https://arxiv.org/pdf/2404.15405.pdf

Deeper Inquiries

How can the DNN be further improved to handle the remaining outliers and issues with the brightest Cepheid variables

To address the remaining outliers and issues with the brightest Cepheid variables, several improvements can be implemented in the DNN approach:

Data Augmentation: Increasing the diversity of the training data by introducing more variations in the input images, such as different noise levels, backgrounds, or saturation levels, can help the model learn to handle outliers better.
Ensemble Learning: Training multiple DNN models and combining their predictions can help reduce the impact of outliers and improve overall performance.
Outlier Detection: Implementing outlier detection mechanisms within the DNN can help identify and mitigate the impact of outliers on the final results.
Fine-tuning Hyperparameters: Adjusting the hyperparameters of the DNN, such as learning rate, batch size, or optimizer, can help improve the model's ability to handle outliers and challenging cases.
Feature Engineering: Introducing additional features or transformations of the existing features can provide the model with more information to better distinguish outliers from regular data points.

What other types of metadata or information could be incorporated into the DNN to improve its performance on unsaturated stars

Incorporating additional metadata or information into the DNN can enhance its performance on unsaturated stars. Some potential metadata that could be beneficial include:

Observational Conditions: Information about the observing conditions, such as atmospheric conditions, telescope pointing, or image quality metrics, can help the model account for variations in data quality.
Instrumental Parameters: Including details about the instrument used for observation, like exposure time, filter used, or detector characteristics, can aid in calibrating the data and improving accuracy.
Temporal Information: Incorporating time-related data, such as observation timestamps or cadence, can help the model capture temporal trends and variations in the light curves.
Crowding Metrics: Metrics indicating the level of crowding around the target star can assist in better handling blended sources and crowded fields, especially for fainter stars.
Photometric Errors: Providing estimates of photometric uncertainties or errors for each data point can help the model weigh the importance of different observations during training and prediction.

Could the DNN approach be applied to other astronomical surveys and instruments facing similar challenges with saturated sources

The DNN approach developed for handling saturated stars in the ASAS-SN survey can be applied to other astronomical surveys and instruments facing similar challenges with saturated sources. Some considerations for its application include:

Data Compatibility: Ensuring that the input data format and characteristics from the new survey or instrument align with the requirements of the DNN model trained on ASAS-SN data.
Transfer Learning: Utilizing transfer learning techniques to adapt the pre-trained DNN model to the new dataset, fine-tuning specific layers or parameters to account for differences in data distribution.
Customization for Instrumentation: Tailoring the DNN architecture and training process to accommodate the unique characteristics and challenges posed by the new survey or instrument, such as different PSF shapes, saturation levels, or observational cadence.
Validation and Testing: Thoroughly validating the performance of the DNN on the new data through rigorous testing, comparison with existing pipelines, and analysis of outliers to ensure its reliability and accuracy.
Collaboration and Knowledge Sharing: Collaborating with experts from the new survey or instrument to leverage domain knowledge and insights, facilitating the adaptation and optimization of the DNN approach for specific observational challenges.