license:apache-2.0 size_categories:n<1K format:imagefolder modality:image library:datasets library:mlcroissant arxiv:2202.04976 doi:10.57967/hf/2084 region:us
---
license: apache-2.0
---

# Dataset Card


## Dataset Details

This dataset is primarily created for the work [Fast muon tracking with machine learning implemented in FPGA](http://dx.doi.org/10.1016/j.nima.2022.167546) ([Arxiv link](https://arxiv.org/abs/2202.04976)) that contains ~3M simulated muon events with Geant4.
Hits in the muon chamber and ground truth of track angle are saved.

### Dataset Description

Please refer to the Section Simulation samples in the referenced work for details.

The file contains 3 keys: 'X', 'Y', and 'corr'.

'X' is a boolean array of size (3072000, 7, 50) used as the input information.

'Y' is a float vector of size (3072000) that contains the ground truth angle to be predicted.

'corr' contains three keys each of size (100, 100) that contains the Pearson correlation factor between the named stations that can be derived from X. Not useful for general purpose.

## Uses

```python
import h5py as h5
with open('dataset.h5','r') as f:
  X = np.array(f['X'])
  Y = np.array(f['Y'])
```

## Dataset Structure

```
<ROOT>
├── X: bool[3072000, 7, 50]
│
├── Y: float64[3072000]
│
└── corr
    ├── 12: float64[100, 100]
    │
    ├── 23: float64[100, 100]
    │
    └── 13: float64[100, 100]
```


## Citation [optional]

You can cite the original work that introduces this dataset.

**BibTeX:**

```
@article{Sun_2023,
   title={Fast muon tracking with machine learning implemented in FPGA},
   volume={1045},
   ISSN={0168-9002},
   url={http://dx.doi.org/10.1016/j.nima.2022.167546},
   DOI={10.1016/j.nima.2022.167546},
   journal={Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment},
   publisher={Elsevier BV},
   author={Sun, Chang and Nakajima, Takumi and Mitsumori, Yuki and Horii, Yasuyuki and Tomoto, Makoto},
   year={2023},
   month=jan, pages={167546}
}
```

