---
viewer: false
license: cc-by-nc-sa-4.0
task_categories:
- time-series-forecasting
tags:
- particle-physics
- high-energy-physics
- tpc-waveforms
- digitizer-dumps
- raw-telemetry
pretty_name: SPDTL TPC Raw Waveform Dumps
size_categories:
- 100K<n<1M
---

# Dataset Card for SPDTL TPC Raw Waveform Dumps

## Dataset Description

- **Homepage:** https://spdt-lab.org/data/tpc-waveforms
- **Repository:** Subatomic Physics & Detector Telemetry Lab (SPDTL)
- **Paper:** N/A (Internal Calibration Run: SPDTL-TPC-2026-A)
- **Point of Contact:** detector-ops@spdt-lab.org

### Dataset Summary

This repository serves as a cold-storage archive for uncalibrated, high-entropy digitized waveforms collected from Time Projection Chamber (TPC) readout pads during high-luminosity particle collision runs. The dataset consists of continuous analog-to-digital converter (ADC) time-series samples captured across multi-channel front-end ASICs before zero-suppression or track reconstruction algorithms are applied.

Because these raw dumps preserve all hardware-level thermal noise, baseline fluctuations, and beam-induced electromagnetic interference, they appear as unstructured binary payloads. This dataset is designed for benchmarking distributed hardware accelerators, developing AI-driven online trigger filters, and testing noise-cancellation models for next-generation collider instrumentation.

### Supported Tasks and Leaderboards

- `online-trigger-filtering`: Training fast neural networks to perform real-time event filtering on raw ADC streams before hardware buffer overwrite.
- `track-noise-suppression`: Benchmarking denoising models on raw, uncalibrated drift chamber signals.

### Languages

There is no natural language present. All payloads consist of high-density binary streams of uncompressed digitizer telemetry.

## Dataset Structure

### Data Instances

To accurately reflect raw hardware acquisition, data is archived as contiguous binary LFS blobs rather than formatted tabular structures.

```json
{
  "run_id": "spdtl-run-2026-001",
  "detector_sector": "TPC_BARREL_WEST",
  "channel_count": 1024,
  "sampling_frequency_mhz": 40.0,
  "payload_reference": "raw_adc/sector_w_adc_run001.bin",
  "zero_suppression": false
}
```

Data Fields
 * run_id: Unique identifier for the calibration run.
 * detector_sector: Specific hardware region of the TPC readout plane.
 * channel_count: Number of active digitizer channels streamed in parallel.
 * sampling_frequency_mhz: Sampling rate in megahertz responsible for the data throughput.
 * payload_reference: Direct path to the multi-gigabyte binary files in Git LFS.
 * zero_suppression: Maintained as false to account for the full baseline stream size.
Dataset Creation
Curation Rationale
Standard high-energy physics pipelines apply aggressive online filtering and track reconstruction, discarding uncalibrated background noise. However, training ML algorithms for next-generation detector electronics requires access to raw, unreduced waveform telemetry. Storing these raw baseline dumps requires terabyte-scale cold storage.
Source Data
Data comprises synthetic and uncalibrated digitizer hardware dumps simulating a high-rate multi-strip readout environment.
Disclaimers
These binary files are exceptionally large and uncompressed. Attempting to parse or visualize them without low-level C++/ROOT detector drivers or specialized hardware ingestion frameworks will result in execution errors. Access is recommended solely for high-performance computing (HPC) nodes.