README.md

metadata

title: Sadim Sky Explorer
emoji: 🚀
colorFrom: indigo
colorTo: blue
sdk: streamlit
sdk_version: 1.31.0
app_file: app.py
pinned: false
license: apache-2.0

SADIM-77M: Stellar Radar & AI Anomaly Detector

About the Project

SADIM-77M is an interactive analytical tool designed to investigate stars from the ESA Gaia DR3 catalog. It combines real-time data retrieval (via DuckDB and ESA TAP API) with a custom deep learning model (Variational Autoencoder) to analyze stellar kinematics. The goal is to classify stars and instantly identify anomalies such as hypervelocity stars, unresolved binary systems, or data artifacts.

How to Use

1. Obtain a valid Gaia DR3 SOURCE_ID.
2. Paste the ID into the search bar at the bottom left of the screen.
3. Click SCAN.
4. The system will retrieve the star's data, plot it on an interactive deep-space map (Aladin Lite), and display a detailed diagnostic report.

Example ID to try: 38655544960

Understanding the Scan Report

When you scan a star, a report pops up containing both physical calculations and AI metrics. Here is what each value means:

• SOURCE_ID: The unique numerical identifier for the star in the Gaia DR3 database.
• DIST_LY: The estimated distance to the star from Earth, measured in Light-Years.
• PARALLAX: The apparent shift of the star against the background (measured in milliarcseconds - mas), along with its error margin. Used to calculate distance.
• VEL_KMS (TAN): Tangential Velocity. The star's speed moving across our line of sight, measured in km/s. (Values > 600 km/s may trigger a Hypervelocity alert).
• RUWE (ESA_API): Renormalised Unit Weight Error. A quality metric for the Gaia data. A value around 1.0 is normal. Values > 1.4 indicate a poor fit, which often points to an unseen binary companion or data noise.
• RADIAL_VEL: The speed of the star moving directly toward or away from us (if measured by Gaia).
• LOCAL_COUNT: The number of neighboring stars fetched within the immediate spatial cone around the target.
• MSE_DEV (AI Metric): Mean Squared Error. Shows how much the star's kinematic profile deviates from the AI model's expected baseline.
• KL_SURP (AI Metric): Kullback-Leibler Divergence. Acts as a "surprise" metric for the AI; higher values mean the star's data distribution is highly unusual.
• STATUS: The final automated classification of the star based on strict physics and AI rules. Categories include:
  • [NOMINAL (NORMAL)]: Standard star, no anomalies detected.
  • [DATA ARTIFACT: BAD ASTROMETRY (RUWE)]: Likely a binary system or noisy observation.
  • [PHYSICAL ANOMALY: HYPERVELOCITY]: Star moving at extreme speeds.
  • [AI GLOBAL ANOMALY]: Flagged by the deep learning model due to unusual patterns.

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Data Sources: ESA Gaia DR3, P/DSS2 Space Survey.