AI 中文总结
JUG是一款基于JAX的独立脉冲星计时软件，速度快、易用，可处理大规模数据，弥合频率论与贝叶斯分析鸿沟，性能优于PINT，与Tempo2相当。
AI 中文摘要
我们提出JUG（JAX-based Unified pulsar timinG，基于JAX的统一脉冲星计时工具），这是一款完全独立的、基于JAX的脉冲星计时软件包，强调速度与易用性，旨在可靠处理脉冲星计时领域中日益庞大且复杂的脉冲星计时阵列数据集。JUG自身实现了完整的脉冲星计时流程，从数据处理、时钟校正到计时模型与拟合，无需依赖其他计时软件。它支持Python式编程，具备编译代码的运行速度，可在GPU上运行，且可通过Python API或交互式GUI操作。JUG用户可交互式探索数据、拟合含复杂随机噪声的计时模型、建模连续引力波等确定性信号，并获取其中随机过程参数的准确点估计，从而弥合频率论计时与贝叶斯噪声分析的鸿沟。JUG的速度比PINT快50倍以上，与Tempo2速度相当，可处理数百万个到达时间，与PINT的一致性达到皮秒级，且能可靠恢复已知的计时模型与噪声参数值。本文描述了其设计、性能与验证，并展示了其在脉冲星计时数据分析中的优势。
英文摘要
We present JUG (JAX-based Unified pulsar timinG), a JAX-based, fully independent pulsar timing package emphasising speed and ease of use, designed to confidently handle the increasingly large and complex pulsar timing array datasets that are being created in the pulsar timing field. JUG implements the entire pulsar timing pipeline itself, from data handling and clock corrections through to the timing model and fitting, without relying on other timing software. It enables Pythonic programming at the speed of compiled code, is GPU-capable, and can be operated via a Python API or an interactive GUI. A user of JUG can interactively explore data, fit timing models with complex stochastic noise, model deterministic signals such as continuous gravitational waves, and obtain accurate point estimates of the parameters of the stochastic processes present, thereby bridging frequentist timing and Bayesian noise analysis. JUG is faster than PINT by more than fifty times and is comparably fast to Tempo2, can handle millions of arrival times, agrees with PINT at the picosecond level, and can reliably recover known timing model and noise parameter values. In this paper we describe its design, performance, and validation, and demonstrate its advantages for pulsar timing data analysis.
