Speaker
Description
The detection of high-redshift (high-z) Gamma-ray Bursts (GRBs) can enhance our understanding of early universe phenomena, although detecting them in gamma-rays is challenging due to the sensitivity of current telescopes in this energy range.
We present a data-driven methodology based on an Artificial intelligence (AI) approach to identify faint high-z GRB signals in Fermi Large Area Telescope (LAT) data, exploiting the expected power-law extension of the afterglow emission into the LAT energy range. From an AI perspective, the identification of high-z GRB afterglows represents a challenging low-signal-to-noise classification problem in a high-dimensional space.
In this work, we simulate high-z ($z>2$) GRB afterglow emission and its detection by Fermi-LAT by modeling the spectral and temporal evolution of the afterglow and convolving the resulting events with the Instrument Response Functions. We train a Convolutional Neural Network (CNN) encoder, which excels at pattern recognition for feature extraction, coupled with a multilayer perceptron for binary classification. We encode observational data as four-dimensional tensors – 4D binned counts-maps – combining spatial, spectral, and temporal information to capture the afterglow evolution. This representation allows the CNN to jointly learn spatial morphology, spectral characteristics and temporal patterns.
This architecture learns correlated patterns across domains without relying on hand-crafted features, making it transferable across instruments with different resolutions and energy ranges. After training on simulated data, model performance is comprehensively evaluated using multiple classification and calibration metrics and further assessed by applying the model to real Fermi-LAT observations of GRB afterglows spanning the redshift range $z\in [0.05-5.26]$. The classification results are interpreted using SHAP (Shapley Additive exPlanations), a game theoretic approach that quantifies feature contributions to the model predictions.
This framework is scalable and instrument-independent, providing a general strategy for faint transient detection that can be extended to other classes of astrophysical events in current and next-generation observatories.