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