Multi-Channel Auto-Calibration for the Atmospheric Imaging Assembly using Machine Learning
Abstract: Solar activity plays a quintessential role in influencing the interplanetary medium and space-weather around the Earth. Remote sensing instruments onboard heliophysics space missions provide a pool of information about the Sun's activity via the measurement of its magnetic field and the emission of light from the multi-layered, multi-thermal, and dynamic solar atmosphere. Extreme UV (EUV) wavelength observations from space help in understanding the subtleties of the outer layers of the Sun, namely the chromosphere and the corona. Unfortunately, such instruments, like the Atmospheric Imaging Assembly (AIA) onboard NASA's Solar Dynamics Observatory (SDO), suffer from time-dependent degradation, reducing their sensitivity. Current state-of-the-art calibration techniques rely on periodic sounding rockets, which can be infrequent and rather unfeasible for deep-space missions. We present an alternative calibration approach based on convolutional neural networks (CNNs). We use SDO-AIA data for our analysis. Our results show that CNN-based models could comprehensively reproduce the sounding rocket experiments' outcomes within a reasonable degree of accuracy, indicating that it performs equally well compared with the current techniques. Furthermore, a comparison with a standard "astronomer's technique" baseline model reveals that the CNN approach significantly outperforms this baseline. Our approach establishes the framework for a novel technique to calibrate EUV instruments and advance our understanding of the cross-channel relation between different EUV channels.
Summary (4 min read)
- Solar activity plays a significant role in affecting the interplanetary medium and space weather around Earth and all the other planets of the Solar System (Schwenn 2006).
- One of the flagship missions of HSO is the Solar Dynamics Observatory (SDO, Pesnell et al. 2012).
- The top row shows the images observed during the early days of the mission, from 13 May 2010, and the bottom row shows the corresponding images observed more recently on 31 August 2019, scaled within the same intensity range.
- The dimming effect observed in the channels is due to the temporal degradation of EUV instruments in space that is also known to diminish the overall instrument sensitivity with time (e.g., BenMoussa et al. 2013).
- A comparison of the sounding-rocket observation with the satellite instrument observation provides an updated calibration, revealing long-term trends in the sensitivities of EVE and thus of AIA.
2. Data description and preprocessing
- The authors used the preprocessed SDO-AIA dataset from Galvez et al. (2019, hereafter referred to as SDOML).
- The data from the two SDO instruments are temporally aligned, with cadences of 6 min for AIA (instead of the original 12 s) and EVE and 12 min for HMI.
- The authors established that 256 × 256 is a sufficient resolution for the predictive task of interest (inference of a single coefficient), and the reduced size enabled quicker processing and more efficient use of the computational resources.
- These factors represent the approximate average AIA data counts in each channel and in the period from 2011 to 2018 (derived from Galvez et al. 2019).
3.1. Formulation of the problem
- It is known that some bright structures in the Sun are observed at different wavelengths.
- Based on this cross-channel structure, the authors established a hypothesis divided into two parts.
- Second, that this relation of the morphological features and the brightness of solar structures can be found in multiple AIA channels.
- The authors hypothesize that these two relations can be used to estimate the dimming factors, and that a deep-learning model can automatically learn these inter- and cross-channel patterns and exploit them to accurately predict the dimming factor of each channel.
- The spatial dependence of the degradation is assumed to be accounted for by regularly updated flat-field corrections applied to AIA images.
3.2. Convolutional neural network model
- Deep learning is a highly active subfield of ML that focuses on specific models called deep neural networks (DNNs).
- DNNs have produced the state-of-art results in many complex tasks, including object detection in images (He et al. 2016), speech recognition (Amodei et al. 2016) and synthesis (Oord et al. 2016), and translation between languages (Wu et al. 2016).
- This architecture is a test of the first hypothesis in Sect. 3.1. The second architecture is instead designed to exploit possible cross-channel relations while training, and it tests their second hypothesis: solar surface features that appear in the different channels will make a multichannel CNN architecture more effective than a single-channel CNN that only exploits interchannel structure correlations.
- The first convolution block has 64 filters, and the second convolution block has 128 filters.
- The final configurations of the model architectures were obtained through a grid search of different hyperparameters and layer configurations.
3.3. Training process
- During training, the authors intentionally excluded this time-dependence from the model.
- This was done (1) using the SDOML dataset, which has already been corrected for degradation effects, (2) without assuming any relation between t and α and avoiding to use t as an input feature, and (3) temporally shuffling the data used for training.
- The authors did not use the full dataset to calculate the gradient descent and propagated back to update the network parameters or weights in the minibatch concept.
- This procedure allowed decreasing the computation cost while still obtaining a lower variance.
- The test dataset, that is, the sample of data the authors used to provide an unbiased evaluation of a model fit on the training dataset, holds images obtained during August to October between 2010 and 2013, again every six hours per day, totaling 9422 images over 1346 time stamps.
3.4. Toy model formulation to probe the multichannel relation
- Using the described CNN model, the authors tested the hypothesis using a toy dataset, which is simpler than the SDOML dataset.
- The authors tested whether the physical relation between the morphology and brightness of solar structures (e.g., ARs, coronal holes) across multiple AIA channels would help the model prediction.
- This toy dataset was designed so that the authors were able to independently test the effect of the presence of (a) a relation between brightness Ai and size σ, and (b) a relation between Ai for various channels; and the presence of both (a) and (b) on the performance.
- The bottom left cell shows the loss when there is no cross-channel correlation, but it has a relation between brightness and size.
- Ai and size σ were removed, the performance was poorer, which increased the MSE loss.
3.5. Reconstruction of the degradation curve using the CNN models
- In order to evaluate the model on a different dataset from the dataset the authors used in the training process, they used both singleand multichannel CNN architectures to recover the instrumental degradation in the entire period of SDO (from 2010 to 2020).
- To produce the degradation curve for the two CNN models, the authors used a dataset that was equivalent to the SDOML dataset, but they did not correct the images for degradation4 (Dos Santos et al. 2021).
- All other preprocessing steps, including masking the solar limb, rescaling the intensity, and so on, remained unchanged.
- A53, page 5 of 12 While the EVE-to-AIA cross-calibration introduced errors of only a few percent (in addition to the calibration uncertainty intrinsic to EVE itself), the FISM-to-AIA cross-calibration errors are considerably larger.
- The feature maps expand their understanding of the model operation.
4. Baseline model
- The authors compared their DNN approach to a baseline motivated by the assumption that the EUV intensity outside magnetically ARs, that is, the quiet Sun, is invariant in time (a similar approach was also considered for the in-flight calibration of some UV instruments, e.g., Schühle et al. 1998).
- It is important to remark that the authors used exactly the same data preprocessing and splitting approach as they used for the neural network model described in Sect. 3.3. From the processed dataset, a set of reference images per channel, Cref , were selected at time t = tref .
- The authors then applied an absolute global threshold value of 5 Mx cm−2 on the coaligned HMI LOS magnetic field maps corresponding to t = tref , such that only those pixels in which BLOS was lower than the threshold were extracted.
- Based on the assumption that the intensity of the quiet-Sun area does not change significantly over time (as discussed in the preceding section), the authors chose to artificially dim these regions by multiplying them with a constant random factor between 0 and 1.
- Subsequently, the dimming factor was obtained by computing the ratio of the two most probable intensity values according to the following equation: αi := Impi Impi,ref .
5.1. Comparing the performances of the baseline model with different CNN architectures
- The results of the learning algorithm were binarized using five different thresholds: the absolute value of 0.05, and relative values of 5%, 10%, 15%, and 20%.
- The authors chose different success rate thresholds to gauge the model, all of which are lower than the uncertainty of the AIA calibration (estimated as ∼10% earlier than May 2014 and ∼28% later).
- When the relative tolerance levels are increased, the mean success rate increases from 27% (for 5% relative tolerance) to 66% (with 20% relative tolerance) and with a 39% success rate in the worst-performing channel (211 Å).
- The most significant improvement was shown in the 94 Å channel with an increase from 32% in the baseline model to about 70% in the single input CNN model, with an absolute tolerance of 0.05.
- The authors stopped the training before epoch 1000; the improvements achieved in the test set over many epochs were only marginal.
5.2. Modeling the channel degradation over time
- In this section the authors discuss the results obtained when the AIA degradation curves V8 and V9 were compared with the singleand multichannel CNN models.
- It is important to note that MEGS-A was used earlier for sounding-rocket calibration purposes, the loss of which caused the V8 and V9 degradation curves to become noisier.
- The different panels of Fig. 6 show that even though the authors trained the single and multichannel models with the SDOML dataset, which was produced and corrected using the V8 degradation curve, the two CNN models predict the degradation curves for each channel quite accurately over time, except for the 94 Å and 211 Å channel.
- The deviations of the predicted values for these two channels fall well within the shaded area associated with the V9 calibration curve.
- The first is the two-sample Kolmogorov-Smirnov test (KS), which determines whether two samples come from the same distribution (Massey 1951), and the null hypothesis assumes that the two distributions are identical.
5.3. Feature maps
- As mentioned in Sect. 3.5, the feature maps are the result of applying the filters to an input image.
- That is, at each layer, the feature map is the output of that layer.
- In Fig. 7 the authors present such maps obtained from the output of the last convolutional layer of their CNN.
- The top row shows the reference input image observed at 193 Å the authors used in this analysis, with its intensity scaled between 0 − 1 pixel units, and the bottom row shows 4 representative feature maps (out of a total of 128) with their corresponding weights.
- The predicted α dimming factors from the model are given by the sigmoid activation function applied to a linear combination of these features.
6. Concluding remarks
- This paper reports a novel ML-based approach to autocalibration and advances their understanding of the cross-channel relation of different EUV channels by introducing a robust novel method to correct for the EUV instrument time degradation.
- The authors also found that the CNN models reproduce the most recent sounding-rocket-based degradation curves (V8 and V9) very closely and within their uncertainty levels.
- This paper finally presents a unique possibility of autocalibrating deep-space instruments such as those onboard the STEREO spacecraft and the recently launched remote-sensing instrument called Extreme Ultraviolet Imager (Rochus et al. 2020) on board the Solar Orbiter satellite (Müller et al. 2020), which are too far away from Earth to be calibrated using a traditional method such as sounding-rockets.
- This project was partially conducted during the 2019 Frontier Development Lab (FDL) program, a co-operative agreement between NASA and the SETI Institute.
- L.F.G.S was supported by the National Science Foundation under Grant No. AGS-1433086.
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Frequently Asked Questions (2)
Q1. What are the contributions mentioned in the paper "Multichannel autocalibration for the atmospheric imaging assembly using machine learning" ?
The authors aim to develop a novel method based on machine learning ( ML ) that exploits spatial patterns on the solar surface across multiwavelength observations to autocalibrate the instrument degradation. Their approach establishes the framework for a novel technique based on CNNs to calibrate EUV instruments. The dataset was further augmented by randomly degrading images at each epoch, with the training dataset spanning nonoverlapping months with the test dataset. Moreover, multichannel CNN outperforms the single-channel CNN, which suggests that cross-channel relations between different EUV channels are important to recover the degradation profiles. The authors envision that this technique can be adapted to other imaging or spectral instruments operating at other wavelengths.
Q2. What are the future works mentioned in the paper "Multichannel autocalibration for the atmospheric imaging assembly using machine learning" ?
The authors showed that the CNNs learned representations that make use of the different features within solar images, but further work needs to be done on this aspect to establish a more detailed interpretation. This paper finally presents a unique possibility of autocalibrating deep-space instruments such as those onboard the STEREO spacecraft and the recently launched remote-sensing instrument called Extreme Ultraviolet Imager ( Rochus et al. 2020 ) on board the Solar Orbiter satellite ( Müller et al. 2020 ), which are too far away from Earth to be calibrated using a traditional method such as sounding-rockets. The authors wish to thank IBM for providing computing power through access to the Accelerated Computing Cloud, as well as NASA, Google Cloud and Lockheed Martin for supporting this project. This is particularly promising, given that no time information has been used in training the models.