The Distributed Electronic Cosmic-ray Observatory (DECO)

The Distributed Electronic Cosmic-ray Observatory (DECO) is a project that enables users to detect cosmic rays and other ionizing radiation with their own cell phones. The DECO app treats cellphone cameras as silicon track detectors. Event images are uploaded to a web-based database where users and other members of the public can query, download, and analyze them. A convolutional neural network automatically classifies events by morphology for particle identification. DECO detects atmospheric muons through their ionization loss in camera image sensors. It also detects radioactive decay products, including electrons that undergo multiple Coulomb scattering and gamma rays that Compton scatter. Our GEANT-based detector Monte Carlo simulation produces images qualitatively and quantitatively similar to those of DECO experimental data. The simulation is well suited for training image classifiers based on machine learning and quantifying the performance of image classifiers and event reconstruction algorithms. DECO makes otherwise invisible particles and phenomena visible to members of the public using their own devices, applying the same concepts and technologies as professional particle physics detectors. We present an overview of the DECO project, which lies at the nexus of education, outreach, and research.

Authors

Publication Details

Published
2025-09-23
DOI
https://doi.org/10.22323/1.501.1257
Primary Topic
Scientific Computing and Data Management
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The Distributed Electronic Cosmic-ray Observatory (DECO)

Alicia Mand, A. Pizzuto, Runze Li, Justin Vandenbroucke
Scientific Computing and Data Management
article

The Distributed Electronic Cosmic-ray Observatory (DECO)

Alicia Mand, A. Pizzuto, Runze Li, Justin Vandenbroucke
article en

Abstract

The Distributed Electronic Cosmic-ray Observatory (DECO) is a project that enables users to detect cosmic rays and other ionizing radiation with their own cell phones. The DECO app treats cellphone cameras as silicon track detectors. Event images are uploaded to a web-based database where users and other members of the public can query, download, and analyze them. A convolutional neural network automatically classifies events by morphology for particle identification. DECO detects atmospheric muons through their ionization loss in camera image sensors. It also detects radioactive decay products, including electrons that undergo multiple Coulomb scattering and gamma rays that Compton scatter. Our GEANT-based detector Monte Carlo simulation produces images qualitatively and quantitatively similar to those of DECO experimental data. The simulation is well suited for training image classifiers based on machine learning and quantifying the performance of image classifiers and event reconstruction algorithms. DECO makes otherwise invisible particles and phenomena visible to members of the public using their own devices, applying the same concepts and technologies as professional particle physics detectors. We present an overview of the DECO project, which lies at the nexus of education, outreach, and research.

Openalex Percentile: Top 75%
Scientific Computing and Data Management
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

The Distributed Electronic Cosmic-ray Observatory (DECO) — Alicia Mand, A. Pizzuto, et al. · (2025) | TGRS Research Map | TGRS