RareAlert: Real-Time Rare Event Detection

• RareAlert is a framework that identifies rare, high-value signals in vast data streams, crucial for domains like astronomy and medicine.
• It employs multi-stage filtering, contextual enrichment, and calibrated anomaly scoring to isolate events of scientific or clinical significance.
• The system integrates advanced ML techniques, including deep learning and LLM ensemble reasoning, to enhance real-time decision making.

RareAlert encompasses a class of real-time computational systems designed for high-throughput, automated identification of rare, unusual, or otherwise high-value signals among vast streams of routine data. The term is used in domains including time-domain astronomy—most notably broker architectures developed for optical transient surveys and multi-messenger astrophysics—and in biomedical informatics for large-scale early-stage rare disease risk screening. The core architectural motif involves stream ingestion, multi-stage filtering and enrichment, rare-event scoring, and integration with human or automated downstream workflows. Across domains, RareAlert systems operationalize uncertainty resolution, anomaly detection, and calibrated prioritization at scale, responding to the growing need for rapid, reliable sifting of voluminous, heterogeneous event streams.

1. Historical Context and Motivating Problems

The genesis of RareAlert frameworks is tightly coupled to the data deluge encountered in next-generation sensor platforms and digital health ecosystems. In time-domain optical astronomy, surveys such as the Zwicky Transient Facility (ZTF) and the Large Synoptic Survey Telescope (LSST) produce on the order of 10⁶–10⁷ alerts per night, of which only a tiny fraction are scientifically novel or require immediate follow-up (Saha et al., 2014, Duev et al., 2021). The challenge of sifting rare transients—supernovae, unusual flaring sources, or events with multi-messenger significance—prompted the development of broker-in-the-middle architectures and dedicated anomaly pipelines.

In medicine, rare diseases collectively affect millions globally, but individual conditions are sparsely represented and prone to delayed, missed, or incorrect diagnosis at the primary care interface. Structural limitations in triage and the non-specificity of early presentations necessitate universal, scalable, and privacy-preserving early risk-screening (Chen et al., 26 Jan 2026). This motivates RareAlert designs that combine machine learning, LLMs, and calibrated ensemble reasoning on routinely captured clinical data.

2. Core Architectural Principles

RareAlert systems adhere to a standardized pipeline:

• Stream ingestion: High-volume event data is received in low-latency, machine-readable form (e.g., VOEvent packets in astronomy, free-text and structured clinical records in medicine).
• Multi-source annotation: Contextual enrichment is performed by cross-matching with archival or catalog data (e.g., Aggregated AstroObject Catalog, Orphanet mappings).
• Feature extraction and derivation: Primary features and secondary statistics are computed, spanning positional, photometric, time-domain, or semantic dimensions.
• Hierarchical filtering cascade: A sequence of increasingly sophisticated filters is applied, ordered by computational complexity and rejection efficiency (from rules-based cuts, probabilistic models, to ML-based outlier detectors).
• Rarity/Anomaly scoring: Scalar scores are computed for each event, enabling ranking or binary triage; scores may reflect statistical rarity, ensemble-calibrated probabilities, or embedding-space distances from known classes.
• User and external module integration: APIs or plug-in mechanisms allow custom filters, real-time adjustment of thresholds, and escalation to domain experts or autonomous robotic agents (Saha et al., 2014, Duev et al., 2021, Chen et al., 26 Jan 2026).

3. Statistical and Machine Learning Methodologies

RareAlert implementations utilize a range of statistical and ML strategies for rare-event isolation and prioritization.

Astronomy

• Probabilistic filtering: Use of sky-position-dependent PDFs for transient appearance rates, enabling quantification of expected versus observed behavior (Saha et al., 2014).
• Unsupervised/outlier detection: Clustering and feature-space distance from labeled classes (e.g., $$d_{\min}(x) = \min_{c\in clusters} \| F(x) - \mu_c \|$$) for identification of rare alerts (Saha et al., 2014).
• Deep learning classifiers: Multi-branch CNNs and tabular networks (e.g., ACAI's five binary classifiers for ZTF) produce class probabilities, with anomaly scores assigned to low-confidence or multi-modal outputs (Duev et al., 2021).
• Active and semi-supervised learning: Iterative inclusion of high-loss or ambiguous cases for human vetting and model retraining (Duev et al., 2021).

Medicine

• LLM ensemble reasoning: Independent quantitative and qualitative risk assessments by multiple LLMs, coupled with chain-of-thought explanations.
• Probability calibration: Model-wise Platt scaling aligns raw LLM risk outputs onto a common, well-calibrated probability scale.
• Ensemble weighting: Supervised ensemble models (CatBoost) integrate calibrated risk predictions, with SHAP-based attribution for local interpretability.
• Distillation: The heterogeneous weighted ensemble is compressed into a single, locally deployable Transformer (Qwen3-4B), utilizing both hard labels and probability-matched soft distillation (Chen et al., 26 Jan 2026).

4. Evaluation and Performance Metrics

Rigorous quantitative metrics are essential to calibrate candidate selection stringency and operational latency.

Astronomy

• Filtering efficiency: Cascaded architectures (e.g., ANTARES) report O(90%) rejection by stage 1 (rules), additional factor-5 reduction via probabilistic filters (stage 2), and final output of O(10–100) rare candidates per night at >90% purity (Saha et al., 2014).
• Latency: End-to-end processing times well within domain constraints (e.g., ∼30 s per alert for ANTARES, <100 ms for ACAI/ZTF) (Saha et al., 2014, Duev et al., 2021).
• Precision/recall/AUC: Classifier test-set AUC ≳0.98; empirical false positive/negative rates ≈1–3%; anomaly stream yields customizable candidate budgets by threshold tuning (Duev et al., 2021).
• Real-time distributed alerting: For IceCube ESTReS, total alert latency ≤5 min, with per-alert astrophysical purity of ≈50% and annual yield ≈10 (Osborn et al., 2023).

Medicine

• Discrimination/calibration: RareAlert achieves AUC 0.917 on large-scale rare disease screening, outperforming ensemble and individual LLM baselines; balanced accuracy = 0.856 at optimal threshold (Chen et al., 26 Jan 2026).
• Calibration effect: Ablations confirm statistically additive benefit of calibration, SHAP weighting, and top-1 reasoning chain inclusion.
• Scalability: Inference throughput supports population-scale screening with <0.2 s per case on modern hardware (Chen et al., 26 Jan 2026).

5. Domain-Specific Implementations

Time-Domain Astronomy

• ANTARES is a prototypical RareAlert engine, acting as a flexible broker-in-the-middle capable of real-time cross-match, contextual annotation, and filtering of LSST-scale alert streams; supports user-defined modular filters and is architected to handle up to a billion alerts annually (Saha et al., 2014).
• ACAI (ZTF) is a production-grade, microservice-deployed deep learning classifier stack that leverages binary classifiers plus anomaly criteria for rare and emergent transient identification, with customizable real-time performance (Duev et al., 2021).
• IceCube ESTReS extends neutrino alerting into under-sampled phase space, providing rapid, southern sky, medium-energy (5–100 TeV) neutrino triggers of direct value for multi-messenger programs (Osborn et al., 2023).

Biomedical Informatics

• RareAlert (Medicine) is an early-screening system that harnesses LLM model diversity, probability calibration, interpretable feature extraction, and distillation into a compact local model. Designed for large-scale deployment in privacy-sensitive health systems, it reconceptualizes rare disease risk as uncertainty resolution over the general population (Chen et al., 26 Jan 2026).

6. Extension, Adaptation, and Limitations

RareAlert engineering emphasizes modularity, extensibility, and domain adaptability.

• Model extension: Direct addition of anomaly/exotic class detectors (e.g., deep autoencoders, one-class SVDD) supports inclusion of previously unseen signal classes and adapts to survey specifics via transfer learning or domain-specific retraining (Duev et al., 2021).
• User customization: Open APIs and plugin hooks (ANTARES) enable users to inject filters, set candidate quotas, or fetch low-level provenance for downstream workflows (Saha et al., 2014).
• Active learning and feedback: Continuous improvement pipelines close the loop by incorporating new human-vetted discoveries into retraining datasets, sustaining sensitivity to emergent or previously unclassified event morphologies (Duev et al., 2021).
• Deployment constraints: RareAlert medical models are optimized for privacy via local deployment and quantized inference; time-domain brokers prioritize sub-minute latencies and robust error handling for high-throughput streams.

Known limitations stem from dependence on retrospective labeled data, restricted feature scope (e.g., English-only clinical narratives), and lack of fine-grained phenotypic localization in initial risk screening. Development trajectories point toward integration with more granular outcome models, dynamic feature attribution, and cross-domain joint anomaly detection.