metadata

license: mit
language:
  - en
pretty_name: MMT Rocket Bodies dataset for classification
size_categories:
  - 1K<n<10K

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

>>> 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:
- <track_id>_<#part>_mag.csv - magnitude values
- <track_id>_<#part>_time.csv - time values
- <track_id>_<#part>_phase.csv - phase angle values
where <track_id> 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
│   │   ├── <track_id>_<\#part>_mag.csv
│   │   ├── <track_id>_<\#part>_time.csv
│   │   ├── <track_id>_<\#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:

Split the light curves if the gap between two consecutive measurements is larger than object's rotational period.
Split the light curves to have maximum span 1_000 seconds.
Filter out light curves which folded form divided into 100 bins has more than 25% of bins empty.
Resample the light curves to 10_000 points with step 0.1 seconds.
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.