R. Gao

ORCID: 0009-0004-1782-7642
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About
Contact & Profiles
Research Areas
  • Particle physics theoretical and experimental studies
  • Quantum Chromodynamics and Particle Interactions
  • High-Energy Particle Collisions Research
  • Neutrino Physics Research
  • Gamma-ray bursts and supernovae
  • Particle Detector Development and Performance
  • Dark Matter and Cosmic Phenomena
  • Atomic and Subatomic Physics Research
  • Pulsars and Gravitational Waves Research
  • Black Holes and Theoretical Physics
  • Computational Physics and Python Applications
  • Medical Imaging Techniques and Applications
  • Superconducting Materials and Applications
  • Radiation Detection and Scintillator Technologies
  • Speech Recognition and Synthesis
  • Domain Adaptation and Few-Shot Learning
  • Particle Accelerators and Free-Electron Lasers
  • Earthquake Detection and Analysis
  • Geological and Geochemical Analysis
  • Radio Astronomy Observations and Technology
  • Multimodal Machine Learning Applications
  • Magnetic confinement fusion research
  • Geochemistry and Geologic Mapping
  • Stochastic processes and statistical mechanics
  • Astrophysics and Cosmic Phenomena

University of Oxford
2022-2025

Texas A&M University
2025

Universitat Ramon Llull
2023-2024

Continual learning has become essential in many practical applications such as online news summaries and product classification. The primary challenge is known catastrophic forgetting, a phenomenon where model inadvertently discards previously learned knowledge when it trained on new tasks. Existing solutions involve storing exemplars from previous classes, regularizing parameters during the fine-tuning process, or assigning different to each task. proposed solution LSEBMCL (Latent Space...

10.48550/arxiv.2501.05495 preprint EN arXiv (Cornell University) 2025-01-09

Abstract Realtime trigger and localization of bursts are the key functions GECAM, an all-sky gamma-ray monitor launched on 2020 December 10. We developed a multifunctional software operating in CPU GECAM Electronic Box. This onboard has following features: high efficiency for real celestial with suppression false triggers caused by charged particle background fluctuation, dedicated algorithm optimized both short long bursts, low time latency information which is downlinked through Global...

10.1088/1674-4527/ad683c article EN Research in Astronomy and Astrophysics 2024-07-27

The Gravitational Wave Burst High-energy Electromagnetic Counterpart All-sky Monitor (GECAM), consists of 2 small satellites that each contain 25 LaBr3 (lanthanum bromide doped with cerium chloride) detectors and 8 plastic scintillator detectors. detector signals are read out using a silicon photomultiplier (SiPM) array. In this study, an acquisition algorithm for in-orbit real-time SiPM array data is designed implemented, the output event packet defined. Finally, algorithm's efficacy verified.

10.48550/arxiv.2112.04786 preprint EN other-oa arXiv (Cornell University) 2021-01-01

10.3799/dqkx.2023.086 article KO Earth Science-Journal of China University of Geosciences 2024-01-01

The Gravitational Wave highly energetic Electromagnetic Counterpart All-sky Monitor (GECAM) is dedicated to detecting gravitational wave gamma-ray bursts. It capable of all-sky monitoring over and discovering bursts new radiation phenomena. GECAM consists two microsatellites, each equipped with 8 charged particle detectors (CPDs) 25 (GRDs). CPD used measure particles in the space environment, monitor energy flow intensity changes, identify between events conjunction GRD. uses plastic...

10.48550/arxiv.2112.05314 preprint EN cc-by arXiv (Cornell University) 2021-01-01

The discovery of gravitational waves and gamma-ray bursts heralds the era multi-messenger astronomy. With adoption two small satellites to achieve all-sky monitoring bursts, wave high-energy electromagnetic counterpart monitor (GECAM) possesses a quasi-real-time early warning ability plays an important role in positioning sources subsequent observations.

10.48550/arxiv.2112.04770 preprint EN cc-by arXiv (Cornell University) 2021-01-01
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