Calorimetry Reconstruction Eval

---
name: calorimetry-reconstruction-eval
description: Evaluates deep learning models for high-energy physics calorimetry tasks, specifically particle shower generation and particle reconstruction (identification and energy regression) using simulated detector data. Use when the user wants to benchmark on LCD Calorimeter Dataset (GEN & REC), or asks about evaluating this task. Reports accuracy.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 1912.06794
  bibtex_key: [REDACTED]
  confidence: medium
---

# calorimetry-reconstruction-eval

> Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics — Belayneh et al. (2019) (arXiv:1912.06794, 2019)

## What this evaluates

Evaluates deep learning models for high-energy physics calorimetry tasks, specifically particle shower generation and particle reconstruction (identification and energy regression) using simulated detector data.

## Datasets

- **LCD Calorimeter Dataset (GEN & REC)** — total ?; splits: GEN (-1), REC (-1)

## Metrics

- `accuracy` **(primary)** — range: percent
  - Classification accuracy for particle type identification (e, γ, π, π⁰). Exact formula not specified in provided text.
- `regression error` — range: other
  - Error metric for regressed particle energy against GEANT4 ground truth. Exact formula not specified in provided text.

## Input / output format

**Input**: 3D arrays of energy deposits in calorimeter cells. GEN input: 51×51×25 ECAL window. REC input: 25×25×25 ECAL slice concatenated with 11×11×60 HCAL slice.

**Output**: GEN: 3D voxel image representing the calorimeter shower. REC: Discrete particle type label and continuous regressed energy value.

## Scoring recipe

```python
def score(predictions, gold):
    correct = sum(1 for p, g in zip(predictions, gold) if p[0] == g[0])
    accuracy = correct / len(gold)
    energy_errors = [(p[1] - g[1])**2 for p, g in zip(predictions, gold)]
    mse = sum(energy_errors) / len(energy_errors)
    return {'accuracy': accuracy, 'mse': mse}
```

## Common pitfalls

- Dataset is entirely GEANT4-simulated, lacking real detector noise or calibration effects.
- Task-specific filtering (HCAL/ECAL ratio < 0.1 for pions, opening angle < 0.01 rad for π⁰) removes easy cases, creating a biased evaluation subset.
- Fixed window sizes may truncate shower tails for high-energy particles, affecting generalization.

## Evidence (verbatim from paper)

> When training classification models on these data, a negligible accuracy increase was observed when moving to larger windows, as described in Appendix[A]... We also compute a set of expert features, as described in Ref.[NIPS]. These features are used to train alternative benchmark algorithms (see Appendices[C] and[D])...

## Citation

```bibtex
@misc{belayneh2019calorimetry,
  title={Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics},
  author={Belayneh et al. (2019)},
  year={2019},
  note={arXiv:1912.06794}
}
```

- arXiv: 1912.06794