Speaker
Description
Solar Low-Energy X-ray Spectrometer (SoLEXS) is a Sun-as-a-star payload onboard the Aditya-L1 mission designed to monitor solar coronal emissions and flare energetics. It has been in continuous operation at the L1 Lagrangian point for almost two years, capturing solar soft X-ray (SXR) spectra at a 1-second cadence. The stability of the instrument and its observing conditions has produced a large, homogenous, and high-cadence dataset ideal for machine learning analysis. However, analyzing this 1-second data presents a significant computational challenge. The study of rapid plasma heating during solar flares, a key science goal, is fundamentally limited by the trade-off between temporal resolution and signal-to-noise ratio (SNR). While key dynamic processes occur on shorter timescales, the low SNR of these short-integration spectra makes traditional forward-modeling techniques infeasible, as they require long integration times (e.g., 30-180s) for reliable fitting. This temporal averaging obscures the fast dynamics targeted for investigation.
A novel analysis pipeline is presented, designed to overcome this limitation and extract plasma parameters (e.g., temperature, elemental abundances) at the full 1-second cadence from SoLEXS spectra. The method employs a hybrid ‘Teacher-Student’ deep learning approach. First, a ‘Teacher’ model, consisting of a fully connected neural network, is trained on reliable, high-SNR time-integrated spectra, learning to rapidly emulate the results of traditional fitting. A second, recurrent model (LSTM) is then trained to learn the temporal dynamics, using sequences of noisy 1-second spectra to predict the parameters of the integrated interval. This allows the pipeline to leverage both the reliability of high-SNR data and the temporal information in the 1-second-cadence sequences, effectively ‘de-noising’ the data by learning how plasma parameters evolve smoothly in time.
This pipeline produces a 1-second-cadence time series of plasma parameters, capturing rapid variations previously lost in time-averaged analysis. This high-cadence output reveals new phenomena. Notably, less-intense flare events are observed that are typically missed by traditional, long-integration analysis. Furthermore, the 1-second temperature model output reveals evidence of quasi-periodic pulsations (QPPs) in the thermal emission. This technique opens a new observational window, enabling the quantitative study of fast heating and cooling processes in the flare plasma at the instrument's native cadence.