Linking Stochastic Self-Propagating Star Formation and Spatio-Temporal Point Processes
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866909739721424896 |
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| author | Zou, Qihan |
| author_facet | Zou, Qihan |
| contents | The stochastic self-propagating star-formation (SSPSF) model is an important theoretical framework for explaining how localised star-formation events trigger subsequent activity across galactic discs. While widely used to interpret spiral and irregular structures, its probabilistic rules have lacked a formal statistical foundation. In this work, we establish a connection between the SSPSF model and spatio-temporal point processes (STPP), which describe events in space and time through history-dependent intensities. We show that the SSPSF update law is equivalent to a separable spatio-temporal Hawkes process, and we derive a simple likelihood function that recovers SSPSF parameters from historical star-formation event data under simplifying assumptions. Beyond the statistical formulation, the framework provides a new approach to analysing the propagation of star formation in galaxies, enabling observational surveys of star-forming regions to be interpreted within the STPP framework. Furthermore, the approach naturally extends to continuous-time models, offering a more realistic representation of galactic dynamics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_12372 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Linking Stochastic Self-Propagating Star Formation and Spatio-Temporal Point Processes Zou, Qihan Astrophysics of Galaxies The stochastic self-propagating star-formation (SSPSF) model is an important theoretical framework for explaining how localised star-formation events trigger subsequent activity across galactic discs. While widely used to interpret spiral and irregular structures, its probabilistic rules have lacked a formal statistical foundation. In this work, we establish a connection between the SSPSF model and spatio-temporal point processes (STPP), which describe events in space and time through history-dependent intensities. We show that the SSPSF update law is equivalent to a separable spatio-temporal Hawkes process, and we derive a simple likelihood function that recovers SSPSF parameters from historical star-formation event data under simplifying assumptions. Beyond the statistical formulation, the framework provides a new approach to analysing the propagation of star formation in galaxies, enabling observational surveys of star-forming regions to be interpreted within the STPP framework. Furthermore, the approach naturally extends to continuous-time models, offering a more realistic representation of galactic dynamics. |
| title | Linking Stochastic Self-Propagating Star Formation and Spatio-Temporal Point Processes |
| topic | Astrophysics of Galaxies |
| url | https://arxiv.org/abs/2508.12372 |