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Research

Graph neural network methods for spatial reconstruction in ground-based gamma-ray astronomy.

§1 Current Research

MSc Thesis · CIC-IPN · 2024–present

The thesis investigates the reconstruction of extensive air shower (EAS) core positions from sparse hit patterns on dual-layer water Cherenkov detector (WCD) arrays, as proposed for the Southern Wide-field Gamma-ray Observatory (SWGO). The central contribution is BiLayerEdgeConv, a graph neural network architecture that treats the electromagnetic and muonic detector layers as separate graph structures and fuses their learned representations through a bidirectional cross-attention mechanism. The work is supervised by Dr. J.A. Martínez Castro (CIC-IPN) and Dr. I.D. Torres Aguilar (INAOE).

Each detector layer is modelled as a k-nearest-neighbor graph over station coordinates. The upper branch applies EdgeConv operators to electromagnetic signals — integrated charge, arrival timing, and PMT amplitude — while the lower branch processes muonic responses from the shielded detector volume. After independent feature extraction, a bidirectional multi-head cross-attention module allows each layer's node embeddings to attend to the other layer's spatial context. Learned gating coefficients control the information flow, enabling the network to weight inter-layer correlations adaptively. The fused representation is passed to a regression head that predicts the shower core position (xy).

Training and evaluation use CORSIKA-generated showers propagated through a Geant4 detector simulation. The architecture achieves a mean absolute error of 45 m on the core position task — within one station spacing of the 161-tank reference array — and a median error of 29.5 m, representing sub-station-pitch resolution. The full model comprises 119,845 trainable parameters, making it compact enough for deployment in online reconstruction pipelines.

Table 1 — BiLayerEdgeConv Architecture

ComponentDescription
Upper branchEdgeConv on electromagnetic layer (charge, timing, PMT amplitude)
Lower branchEdgeConv on muonic layer (shielded detector response)
FusionBidirectional multi-head cross-attention with learned gating
OutputCore position (xy) regression

Table 2 — Preliminary Results

MetricValueContext
MAE45 mWithin one station spacing
Improvement−20%vs Centre-of-Gravity baseline
Median error29.5 mSub-station-pitch resolution
Parameters119,845Compact architecture

§2 Publications

  1. [1]  Martínez Castro J.A., Tat'y Mwata-Velu, Mpangi Musungu E., Misonia H., et al. “Suppressor Of Cytokine Signaling Members In Lung Adenocarcinoma: Unveiling Expression Patterns, Posttranslational Modifications, And Clinical Significance.”Journal of Population Therapeutics & Clinical Pharmacology, Vol. 30, No. 18, 2023.
    doi:10.53555/jptcp.v30i18.3309
  2. [2]Misonia H., Martínez Castro J.A., Torres Aguilar I.D. “Reconstruction of EAS Core Position for SWGO via Deep Learning with Spatio-Temporal Bilayer Fusion.”MSc thesis, CIC-IPN, 2025 (in preparation).

§3 Collaborations

SWGO— The Southern Wide-field Gamma-ray Observatory is a next-generation ground-based detector under development in South America, targeting the 100 GeV–PeV energy range with a double-layer water Cherenkov detector array. The collaboration spans 80+ institutions across 14 countries. Misonia contributes to the core position reconstruction pipeline as a student member.
swgo.org

HAWC— The High-Altitude Water Cherenkov Observatory operates at 4,100 m elevation on Sierra Negra, Puebla, Mexico, observing TeV gamma-rays and cosmic rays. Simulation frameworks developed for HAWC (HAWCSim, CORSIKA) inform the architectural decisions in the SWGO thesis work. Misonia participates through the CIC-IPN laboratory.
hawc-observatory.org


§4 Tools & Methods

CategoryTools
Deep learningPyTorch, PyTorch Geometric, EdgeConv, GATConv
AttentionMulti-head cross-attention, learned gating, multi-task learning
SimulationCORSIKA, Geant4, HAWCSim
LanguagesPython, C++, Bash
InfrastructureSLURM, Git, Weights & Biases, LaTeX