# Conjunction Data Messages behave as a Poisson Process

arXiv: https://arxiv.org/abs/2105.08509 · HTML: https://mlsanity.com/paper/2105.08509/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 22 references that are in the map.
- First posted on arXiv 14 May 2021.

## Builds on (most cited first, 3 of 3)

- [An Introduction to Variational Autoencoders](https://mlsanity.com/paper/1906.02691/) (2019)
- [Interpretable machine learning: definitions, methods, and applications](https://mlsanity.com/paper/1901.04592/) (2019)
- [Towards Automated Satellite Conjunction Management with Bayesian Deep Learning](https://mlsanity.com/paper/2012.12450/) (2020)

## Built on by (most cited first, 1 of 1)

- [Statistical Learning of Conjunction Data Messages Through a Bayesian Non-Homogeneous Poisson Process](https://mlsanity.com/paper/2311.05426/) (2023)

## Abstract (arXiv)

Space debris is a major problem in space exploration. International bodies continuously monitor a large database of orbiting objects and emit warnings in the form of conjunction data messages. An important question for satellite operators is to estimate when fresh information will arrive so that they can react timely but sparingly with satellite maneuvers. We propose a statistical learning model of the message arrival process, allowing us to answer two important questions: (1) Will there be any new message in the next specified time interval? (2) When exactly and with what uncertainty will the next message arrive? The average prediction error for question (2) of our Bayesian Poisson process model is smaller than the baseline in more than 4 hours in a test set of 50k close encounter events.