Upload README.md

README.md

@@ -2,16 +2,64 @@
 license: cc0-1.0
 ---
 
-# Experienced
-
-ML training data for the Roman Microlensing Data CHallenge 2025 - `Experienced` tier.
-
+# Experienced Tier
+
+The experienced tier data can be found in **RMDC26_Experienced_Data.parquet**. If read in properly, it should have a shape of 103,281,456 rows × 18 columns and contain lightcurves for 2,087 unique events (event names range from `RMDC26_000189` to `RMDC26_002288`, with 13 events excluded). The columns are as follows.
+
+## Columns
+
+| Column Name | Data Type | Description |
+| :--- | :--- | :--- |
+| `name` | string | Unique event identifier (e.g., `RMDC26_000189`). Format: `RMDC26_XXXXXX`. Range: `RMDC26_000189` to `RMDC26_002288` (2,087 events total). |
+| `l_deg` | float64 | Galactic longitude of the event in degrees. Range: [0, 360]. |
+| `b_deg` | float64 | Galactic latitude of the event in degrees. Range: [-90, 90]. |
+| `ra_deg` | float64 | Catalog right ascension of the event in degrees. Range: [0, 360]. |
+| `dec_deg` | float64 | Catalog declination of the event in degrees. Range: [-90, 90]. |
+| `bjd` | float64 | Barycentric Julian Date of the observation. Approximate range: 2,461,447–2,463,164. |
+| `filt` | string | Filter used for the observation. Values: `F087`, `F146`, `F213`. |
+| `flux_uJy` | float64 | Measured flux in microjanskys (μJy). |
+| `flux_err_uJy` | float64 | Uncertainty in the measured flux in μJy. Always > 0. |
+| `extinction` | float64 | Extinction in the specific filter's band. Range: [0, ~16.4]. |
+| `saturation_flag` | int64 | Saturation flag: 0 = unsaturated, 1 = saturated. ~0.04% of observations are saturated. |
+| `measured_ra_deg` | float64 | Measured right ascension in degrees. Range: [0, 360]. |
+| `measured_dec_deg` | float64 | Measured declination in degrees. Range: [-90, 90]. |
+| `measured_ra_err_deg` | float64 | Uncertainty in measured RA in degrees. Always > 0. |
+| `measured_dec_err_deg` | float64 | Uncertainty in measured Dec in degrees. Always > 0. |
+| `obs_x` | float64 | Observatory X-coordinate in AU (Cartesian-ecliptic reference frame). Range: [-1.01, 1.02]. |
+| `obs_y` | float64 | Observatory Y-coordinate in AU (Cartesian-ecliptic reference frame). Range: [-0.64, 0.62]. |
+| `obs_z` | float64 | Observatory Z-coordinate in AU (Cartesian-ecliptic reference frame). Range: [-0.005, 0.005]. |
+
+**Astrometry Note:** Astrometric positions are calculated as the flux-weighted average position of the source images and lens flux. Any additional ambient flux is assumed to be isotropic.
+
+## Dataset Statistics
+
+- **Total rows**: 103,281,456
+- **Unique events**: 2,087
+- **Observations per event**: 49,488 (all events have the same number)
+- **Filters per event**: All events have observations in 3 filters (F087, F146, F213)
+- **Filter distribution**: F146 (~93.9%), F087 (~3.0%), F213 (~3.0%)
+- **Saturated observations**: ~0.04% (42,831 out of 103,281,456)
+
+## Simulation Details & Disclaimer
+
+**Simulation Engine:** All events have been generated using **GULLS**. More information is available at [gulls-microlensing.github.io](https://gulls-microlensing.github.io/).
+
+**Event Complexity:**
+- Up to **two source stars** per event.
+- Up to **three lens bodies** per event, which may include up to two planets and one moon.
+
+**Sample Bias & Selection:**
+- **Anomaly Filtering:** This dataset has been filtered to include only events that likely contain an anomaly (e.g., planetary or lunar perturbations). Note that some false-positive anomaly detections may be present.
+- **Non-Representativeness:** Due to this filtering, the sample is **not representative** of the full population of events expected from the Roman Space Telescope, nor is it representative of the full range of anomaly types expected in the actual mission.
+- **Stellar Variability:** To increase realism, stellar variability due to starspots has been added to a subset of the sources using [butterpy](https://github.com/zclaytor/butterpy).
+
+**Disclaimer:** This dataset is intended for testing and development purposes and should be used with the understanding of the biases described above.
 
 ## Uploading
 
-CLI:
+### CLI
 
-```
+```bash
 # Install the Hugging Face CLI
 brew install huggingface-cli
 
@@ -22,9 +70,9 @@ hf auth login
 hf upload RGES-PIT/Experienced . --repo-type=dataset
 ```
 
-Python:
+### Python
 
-```
+```python
 from huggingface_hub import HfApi
 
 api = HfApi(token=os.getenv("HF_TOKEN"))
@@ -35,9 +83,9 @@ api.upload_folder(
 )
 ```
 
-HTTPS:
+### HTTPS
 
-```
+```bash
 # Make sure git-lfs is installed (https://git-lfs.com)
 git lfs install
 
@@ -47,9 +95,9 @@ git remote add origin https://huggingface.co/datasets/RGES-PIT/Experienced
 git push -u origin main
 ```
 
-SSH:
+### SSH
 
-```
+```bash
 # Make sure git-lfs is installed (https://git-lfs.com)
 git lfs install