Offline data processing software for the High Energy cosmic-Radiation Detection facility

The High Energy cosmic-Radiation Detection (HERD) facility was proposed as a space cosmic-ray and gamma-ray detector onboard China’s Space Station around 2027. The primary scientific objectives of HERD include searching for dark matter particles, studying the energy spectrum and composition of cosmic rays, and conducting high energy gamma-ray astronomy. The HERD offline software (HERDOS) is designed and developed based on SNiPER, a lightweight framework for high energy physics (HEP) experiments. HERDOS is designed to provide the common data processing functionalities and also responsible for building the offline data processing chain, which encompasses detector simulation, calibration, reconstruction, and data analysis. Several common HEP software toolkits, such as Geant4, ROOT, DD4hep, PODIO, and TBB, have also been integrated into HERDOS. This paper describes the design and implementation of the core software of the HERDOS, including the event data and detector data management system, parallelized simulation framework and database system. Currently, HERDOS operates at full capacity to support the conceptual design of detectors, conduct detector performance studies, and physics potential study.

Authors

Institutions

Publication Details

Journal
EPJ Web of Conferences
Published
2025-01-01
DOI
https://doi.org/10.1051/epjconf/202533701155
Citations
1
Primary Topic
Particle physics theoretical and experimental studies
Type
article
Field-Weighted Citation Impact
1.87

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Offline data processing software for the High Energy cosmic-Radiation Detection facility

Qianqian Shi, Teng Li, X. T. Huang
1 citations
EPJ Web of Conferences
Particle physics theoretical and experimental studies
1.87
article

Offline data processing software for the High Energy cosmic-Radiation Detection facility

Qianqian Shi, Teng Li, X. T. Huang
article en
1 citations

Abstract

The High Energy cosmic-Radiation Detection (HERD) facility was proposed as a space cosmic-ray and gamma-ray detector onboard China’s Space Station around 2027. The primary scientific objectives of HERD include searching for dark matter particles, studying the energy spectrum and composition of cosmic rays, and conducting high energy gamma-ray astronomy. The HERD offline software (HERDOS) is designed and developed based on SNiPER, a lightweight framework for high energy physics (HEP) experiments. HERDOS is designed to provide the common data processing functionalities and also responsible for building the offline data processing chain, which encompasses detector simulation, calibration, reconstruction, and data analysis. Several common HEP software toolkits, such as Geant4, ROOT, DD4hep, PODIO, and TBB, have also been integrated into HERDOS. This paper describes the design and implementation of the core software of the HERDOS, including the event data and detector data management system, parallelized simulation framework and database system. Currently, HERDOS operates at full capacity to support the conceptual design of detectors, conduct detector performance studies, and physics potential study.

EPJ Web of ConferencesVol. 337
Shandong University (CN)
National Natural Science Foundation of China, National Key Research and Development Program of China
Openalex Percentile: Top 11%
Particle physics theoretical and experimental studies
1.87
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.

Offline data processing software for the High Energy cosmic-Radiation Detection facility — Qianqian Shi, Teng Li, et al. · EPJ Web of Conferences (2025) | TGRS Research Map | TGRS