TODHESSONG

Greetings. I am Dr. Tod Hessong, an astrophysical computational scientist and AI architect specializing in attention-mechanism-enhanced gravitational lensing simulations. As the Lead AI Researcher at NASA’s Cosmic Vision Lab (2023–present) and a former Einstein Fellow at Princeton’s Institute for Advanced Study (2021–2023), my work pioneers physics-informed neural architectures to decode spacetime distortions caused by dark matter and galaxy clusters. By integrating transformer-based attention layers with general relativistic frameworks, I achieve sub-arcsecond accuracy in lensing mass reconstruction while reducing computational costs by 90% (Nature Astronomy, 2025). My mission: revolutionize precision cosmology by enabling AI to "focus" on critical lensing features that elude traditional methods.

Methodological Innovations

1. Multiscale Spacetime Attention

• Challenge: Lensing signals span 12 orders of magnitude in spatial scale (from galaxy clusters to microlensing pixels).

• Breakthrough: Designed LensFocus-Transformer (LFT):

  • Deploys hierarchical attention heads to weight mass density peaks (e.g., dark matter halos) and ignore noise-dominated regions.

  • Combines convolutional inductive bias for local lensing shear with global self-attention for Einstein ring topology.

  • Achieved 94% F1-score in reconstructing the bullet cluster’s dark matter distribution from mock LSST data.

2. Uncertainty-Aware Attention Masking

• Framework: UncertLens quantifies Bayesian uncertainties in lensing inversions:

  • Uses stochastic attention dropout to flag unreliable mass map regions (e.g., low signal-to-noise arcs).

  • Calibrates predictions against 10,000+ simulated HST/James Webb lensing fields.

  • Adopted by DESI Collaboration to prioritize spectroscopic follow-ups.

3. Real-Time Attention Adaptation

• Tool: AdaptLens dynamically adjusts attention weights during observations:

  • Processes time-domain lensing events (e.g., quasar variability) via recurrent cross-attention.

  • Detected 12 new caustic-crossing events in ZTF data, accelerating microlensing planet searches.

Landmark Projects

1. Dark Matter Map

• ESA Euclid Mission Partnership:

  • Trained Euclid-Net to recover subhalo masses down to 10⁶ M☉ using attention-guided substructure zoom-ins.

  • Identified 23 candidate dark matter filaments in early data.

2. AI Lens Zoo

• Citizen Science Integration:

  • Built LensAttn, a web platform where volunteers label attention heatmaps on lensing candidates.

  • Achieved 4× faster classification than traditional crowdsourcing (1.2M classifications in 2024).

3. Strong Lens Cosmography

• DOE Cosmic Frontier Initiative:

  • Derived Hubble constant (H₀) with 0.7% uncertainty from 400 simulated lensed quasars.

  • Resolved H₀ tension discrepancies via attention-based selection of time-delay systematics.

Technical and Societal Impact

1. Open-Source Ecosystem

• Released LensFlow, a PyTorch library for attention-based lensing:

  • Pre-trained models for JWST/NIRCam and Rubin Observatory data.

  • Tutorials connecting attention weights to physical parameters (σ₈, Ωₘ).

2. Ethical AI Guidelines

• Authored AstroAI Ethics Charter (AAS 2025):

  • Bans attention model use for military space surveillance.

  • Mandates uncertainty disclosure in AI-derived cosmological parameters.

3. Education

• Launched CosmoX MOOC series:

  • Teaches gravitational lensing through interactive attention visualization.

  • Enrolled 40,000+ students from 152 countries.

Future Directions

1. Exascale Attention
   Scale LFT to petapixel surveys via hybrid quantum-classical attention optimization (CERN/NASA partnership).

2. Multimodal Cosmic Vision
   Fuse lensing data with 21cm cosmology and neutrino maps through cross-modal attention.

3. Interferometric Attention
   Develop radio lensing models for SKA/VLBI, resolving nanoarcsecond jet distortions.