Space sustainability

Trillium is committed to make space safer and allow a sustainable growth of spacecraft operations and satellites in orbit. The company has years of experience in space traffic management (STM) and space situational awareness (SSA). Experts in spacecraft collision avoidance and machine learning have helped the company to build a portfolio of cutting edge solutions to manage space traffic and operations effectively. The developed tools currently revolve on spacecraft collision avoidance, with the Python package Kessler, and thermospheric density modelling via space weather indices and raw measurements, with the Python package Karman.

Kessler

Kessler is a Python package aiming at improving Space Situational Awareness (SSA) and Space Traffic Management (STM). It was originally developed by the FDL Europe Constellations Team, in partnership with the European Space Operations Centre (ESOC) of the European Space Agency and the University of Oxford. Kessler can provide simulation-based inference and machine learning tools for space collision avoidance and assessment and the authors have worked on several case studies published on international conferences which showcase the use of the software for assisting operators and researchers in a sustainable and safe use of space [1,2,3,4]. It is named in honor of NASA scientist Donald J. Kessler, known for his studies regarding space debris and proposing the Kessler syndrome.

Karman

Karman is another Python package that was developed in collaboration with the Heliophysics Division of NASA. Its main goal is to improve our understanding and modelling of thermospheric density variations due to the Sun’s influence [5]. It supports a large variety of heliophysics and geomagnetic data: from EUV measurements, to solar irradiance proxies, geomagnetic measurements and indexes, and precise orbit determination-derived thermospheric density data. It can be used both for hosting state-of-the-art empirical and ML thermospheric density models, as well as for benchmarking ther performances at various geographical locations, times, geomagnetic strom, and solar irradiance conditions. This makes Karman a first-of-its-kind software framework that can be used by both the research and operational communities to develop, benchmark and validate their models, before they are deployed.

Read and download Intelligence Age #002: The Tragedy of the Cosmos: Sustainability in Orbit and Beyond below:

[1] Giacomo Acciarini, Edward Brown, Tom Berger, Madhulika Guhathakurta, James Parr, Christopher Bridges, Atılım Güneş Baydin Improving Thermospheric Density Predictions in Low-Earth Orbit With Machine Learning. https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023SW003652

[2] Giacomo Acciarini, Francesco Pinto, Francesca Letizia, José A. Martinez-Heras, Klaus Merz, Christopher Bridges, and Atılım Güneş Baydin. 2021. “Kessler: a Machine Learning Library for Spacecraft Collision Avoidance.” In 8th European Conference on Space Debris. https://conference.sdo.esoc.esa.int/proceedings/list.

[3] Giacomo Acciarini, Francesco Pinto, Sascha Metz, Sarah Boufelja, Sylvester Kaczmarek, Klaus Merz, José A. Martinez-Heras, Francesca Letizia, Christopher Bridges, and Atılım Güneş Baydin. 2020. “Spacecraft Collision Risk Assessment with Probabilistic Programming.” In Third Workshop on Machine Learning and the Physical Sciences (NeurIPS 2020), Vancouver, Canada. arXiv:2012.10260

[4] Francesco Pinto, Giacomo Acciarini, Sascha Metz, Sarah Boufelja, Sylvester Kaczmarek, Klaus Merz, José A. Martinez-Heras, Francesca Letizia, Christopher Bridges, and Atılım Güneş Baydin. 2020. “Towards Automated Satellite Conjunction Management with Bayesian Deep Learning.” In AI for Earth Sciences Workshop at NeurIPS 2020, Vancouver, Canada. arXiv:2012.12450

[5] Giacomo Acciarini, Nicola Baresi, Christopher Bridges, Leonard Felicetti, Stephen Hobbs, and Atılım Güneş Baydin. 2023. “Observation Strategies and Megaconstellations Impact on Current LEO Population”. In 2nd NEO and Debris Detection Conference at European Space Operations Centre, Darmstadt, Germany. https://conference.sdo.esoc.esa.int/proceedings/list?search=&conference=3.

[6] Acciarini, Giacomo, Edward Brown, Tom Berger, Madhulika Guhathakurta, James Parr, Christopher Bridges, and Atılım Güneş Baydin. "Improving Thermospheric Density Predictions in Low‐Earth Orbit With Machine Learning." Space Weather 22, no. 2 (2024): e2023SW003652.

[7] Acciarini, Giacomo, Edward Brown, Chris Bridges, Atılım Günes Baydin, Thomas E. Berger, and Madhulika Guhathakurta. "Karman-a machine learning software package for benchmarking thermospheric density models." In Advanced Maui Optical and Space Surveillance Technologies Conference (AMOS). 2023.

[8] Malik, Shreshth A., James Walsh, Giacomo Acciarini, Thomas E. Berger, and Atılım Güneş Baydin. "High-Cadence Thermospheric Density Estimation enabled by Machine Learning on Solar Imagery." In Bayesian Deep Learning Workshop, NeurIPS. 2023.

[9] Bonasera, Stefano, Giacomo Acciarini, J. Pérez-Hernández, Bernard Benson, Edward Brown, Eric Sutton, Moriba K. Jah, Christopher Bridges, and Atılım Günes Baydin. "Dropout and ensemble networks for thermospheric density uncertainty estimation." In Bayesian Deep Learning Workshop, NeurIPS. 2021.