--- license: mit language: - en pretty_name: MMT Rocket Bodies dataset for classification size_categories: - 1K # RoBo6: Standardized MMT Light Curve Dataset For Rocket Body Classification Dataset contains light curves of 6 rocket body types from Mini Mega Tortora database (MMT)[^1]. The dataset was created to be used as a benchmark for rocket body light curve classification. For more informations follow the original paper: _RoBo6: Standardized MMT Light Curve Dataset for Rocket Body Classification_[^2] Class labels: - ARIANE 5 R/B - ATLAS 5 CENTAUR R/B - CZ-3B R/B - DELTA 4 R/B - FALCON 9 R/B - H-2A R/B ## Dataset description ## Usage ```python >>> from datasets import load_dataset >>> dataset = load_dataset("kyselica/RoBo6", data_files={"train": "train.csv", "test": "test.csv"}) >>> dataset DatasetDict({ train: Dataset({ features: ['label', ' id', ' part', ' period', ' mag', ' phase', ' time'], num_rows: 5676 }) test: Dataset({ features: ['label', ' id', ' part', ' period', ' mag', ' phase', ' time'], num_rows: 1404 }) }) ``` - `label` - class name - `id` - unique identifier of the light curve from MMT - `part` - part number of the light curve - `period` - rotational period of the object - `mag` - relative path to the magnitude values file - `phase` - relative path to the phase values file - `time` - relative path to the time values file Mean and standard deviation of magnitudes are stored in `mean_std.csv` file. ### File structure - `data` directory contains 5 subdirectories, one for each class. Light curves are stored in file triplets in the following format: - `_<#part>_mag.csv` - magnitude values - `_<#part>_time.csv` - time values - `_<#part>_phase.csv` - phase angle values where `` is the unique identifier of the light curve from MMT, `<\#part>` is the part number of the light curve (some light curves are split into multiple parts). - `train.csv` and `test.csv` - contains information about the train and test splits (label, id, part, period, mag, phase, time) - `mean_std.csv` - contains mean and standard deviation for magnitudes, computed over the training set. ``` MMT Rocket Bodies ├── README.md ├── train.csv ├── test.csv ├── mean_std.csv ├── data │ ├── ARIANE 5 R_B │ │ ├── _<\#part>_mag.csv │ │ ├── _<\#part>_time.csv │ │ ├── _<\#part>_phase.csv │ ├── ATLAS 5 CENTAUR R_B │ │ ├── ... │ ├── CZ-3B R_B │ │ ├── ... │ ├── DELTA 4 R_B │ │ ├── ... │ ├── FALCON 9 R_B │ │ ├── ... │ ├── H-2A R_B │ │ ├── ... ``` ## Data preprocessing To create data sutable for both CNN and RNN based models, the light curves were preprocessed in the following way: 1. Split the light curves if the gap between two consecutive measurements is larger than object's rotational period. 2. Split the light curves to have maximum span 1_000 seconds. 3. Filter out light curves which folded form divided into 100 bins has more than 25% of bins empty. 5. Resample the light curves to 10_000 points with step 0.1 seconds. 4. Filter out light curves with less than 100 measurements. ## Citation [^2]: RoBo6: Standardized MMT Light Curve Dataset for Rocket Body Classification ``` @article{kyselica2024robo6, title={RoBo6: Standardized MMT Light Curve Dataset for Rocket Body Classification}, author={Kyselica, Daniel and {\v{S}}uppa, Marek and {\v{S}}ilha, Ji{\v{r}}{\'\i} and {\v{D}}urikovi{\v{c}}, Roman}, journal={arXiv preprint arXiv:2412.00544}, year={2024} } ``` ## References [^1]: Karpov, S., et al. "Mini-Mega-TORTORA wide-field monitoring system with sub-second temporal resolution: first year of operation." Revista Mexicana de Astronomía y Astrofísica 48 (2016): 91-96.