MARTIKA CAMPBELL

Greetings. I am Martika Campbell, an astrophysicist and machine learning researcher dedicated to decoding the universe’s hidden narratives through anomaly detection in multi-scale astrophysical data. With a Ph.D. in Computational Astrophysics (Caltech, 2024) and a joint appointment at NASA’s Jet Propulsion Laboratory and the European Southern Observatory (ESO), my work bridges high-dimensional data mining and theoretical cosmology to uncover rare cosmic phenomena—from hypervelocity stars to dark matter substructures—with sub-millijansky sensitivity and real-time anomaly localization.

Methodological Innovations

1. Self-Supervised Anomaly Detection Frameworks

• Problem: Traditional supervised methods fail for rare or unknown astrophysical events (e.g., fast radio bursts, orphan gamma-ray bursts).

• Breakthrough: Developed Cosmic-AE (Anomaly Embedding), a self-supervised architecture that:

  • Learns latent representations of multi-wavelength data (X-ray, radio, optical) via contrastive divergence.

  • Detects outliers at 5σ significance with <1% false positives in petascale surveys (e.g., LSST, SKA).

  • Published in Nature Astronomy (2024) with open-source code (GitHub Stars: 3.5k+).

2. Temporal-Spatial Fusion Networks

• Framework: STARFIRE (Spatio-Temporal Attention for Irregular Events) integrates:

  • Time-series decomposition of pulsar timing arrays (PTAs) to isolate glitches from gravitational wave noise.

  • Graph-based attention for correlating anomalies across sky regions (e.g., correlating neutrino flares with AGN jets).

• Impact: Achieved 30× faster anomaly localization than Markov Chain Monte Carlo (MCMC) methods.

3. Explainable AI for Anomaly Attribution

• Toolkit: AstroXAI quantifies anomaly origins via:

  • Saliency maps for instrumental artifacts vs. astrophysical signals.

  • Counterfactual reasoning to simulate "what-if" scenarios (e.g., "What if this X-ray transient were a tidal disruption event?").

• Adoption: Integrated into Zwicky Transient Facility (ZTF) and FINK alert pipelines.

Landmark Discoveries

1. Galactic Halo "Ghost Streams"

• Data: Gaia DR4 + DESI spectroscopy (10^9 stars).

• Approach: Clustered phase-space overdensities using topological data analysis (TDA).

• Outcome: Discovered 12 stellar streams linked to dwarf galaxy mergers, revising Milky Way mass estimates by +15% (Science, 2025).

2. Anomalous Quasar Variability

• Challenge: Distinguishing supermassive black hole mergers from microlensing in 10^6 quasar light curves.

• Solution: Trained Siamese Neural Networks on simulated LSST data.

• Discovery: Identified 47 candidate merger events, enabling targeted LISA follow-ups.

3. Dark Matter Subhalo Signatures

• Technique: Applied weak lensing anomaly scores to HSC-SSP data.

• Result: Detected low-mass subhalos (10^6 M☉) violating ΛCDM predictions, sparking debates on warm dark matter models.

Technical and Collaborative Contributions

1. Open Science Ecosystem

• Launched AstroAnomaly Hub: A FAIR (Findable, Accessible, Interoperable, Reusable) repository hosting:

  • 500+ pre-trained anomaly detection models.

  • Benchmarks for multi-messenger alerts (neutrino + gravitational wave + EM counterparts).

2. Hardware-Software Co-Design

• Edge Computing: Deployed FPGA-accelerated inference for real-time anomaly ranking at ESO’s Cherenkov telescopes.

• Quantum Readiness: Prototyped quantum kernel methods for high-dimensional feature spaces with IBM Quantum.

3. Ethics of Cosmic Exploration

• Authored IAU White Paper on Anomaly Ethics (2024), addressing:

  • Bias mitigation in anomaly prioritization (e.g., avoiding Northern Sky bias).

  • Protocols for reporting potential technosignatures to the UN Office for Outer Space Affairs.

Future Frontiers

1. Multi-Messenger Anomaly Fusion:
   Correlating LIGO-Virgo gravitational waves with CTA gamma-ray outliers to probe quantum gravity.

2. Autonomous Observatory Networks:
   Developing self-adaptive telescopes that reorient based on live anomaly risk maps.

3. Anomaly-Driven Cosmology:
   Harnessing anomalies to test cyclic universe models and Hubble tension resolutions.