In-Orbit GRB Identification Using LLM-based model for the CXPD CubeSat

TL;DR

This paper presents a machine learning-based method using a fine-tuned large language model for real-time gamma-ray burst identification onboard CubeSats, addressing background complexity challenges in wide FOV X-ray observations.

Contribution

It introduces a novel in-orbit GRB identification approach employing a multimodal LLM fine-tuned with LoRA and quantization, optimized for onboard processing constraints.

Findings

Achieved perfect classification accuracy on validation data.

Demonstrated strong spectral index regression with RMSE of 0.118.

Validated feasibility of onboard deployment through simulated pipeline.

Abstract

To validate key technologies for wide field-of-view (FOV) X-ray polarization measurements, the Cosmic X-ray Polarization Detector (CXPD) CubeSat series has been developed as a prototype platform for the Low-Energy X-ray Polarization Detector (LPD) onboard the POLAR-2 mission. The wide-FOV design significantly increases the complexity of the background environment, posing notable challenges for real-time gamma-ray burst (GRB) identification. In this work, we propose an in-orbit GRB identification method based on machine learning, using simulated spectral data as input. A training dataset was constructed using a Geant4-based simulator, incorporating in-orbit background and GRB events modeled within the 2-10 keV energy range. To meet the computational constraints of onboard processing, we employ a multimodal large language model (MLLM), which is fine-tuned using low-rank adaptation (LoRA) based on miniCPM-V2.6 and quantized to 4-bit precision. The model achieves perfect classification accuracy on validation data and demonstrates strong regression performance in estimating GRB spectral indices, with an RMSE of 0.118. Furthermore, we validate the feasibility of onboard deployment through a simulated satellite data processing pipeline, highlighting the potential of our approach to enable future real-time GRB detection and spectral analysis in orbit.