The origins of the colors of Trans-Neptunian Objects (TNOs) represent a crucial unresolved question, central to understanding the history of our Solar System. Recent observational surveys have revealed correlations between the eccentricity and inclination of TNOs and their colors. This has rekindled the long-standing debate on whether these colors reflect the conditions of TNO formation or their subsequent collisional evolution. In this study, we address this question with 98.7% certainty, using a model-agnostic, data-driven approach based on causal graphs. First, as a sanity check, we demonstrate how our model can replicate the currently accepted paradigms of TNOs' dynamical history, blindly and without any orbital modeling or physics-based assumptions. In fact, our causal model (with no knowledge of the existence of Neptune) predicts the existence of an unknown perturbing body, i.e., Neptune. We then show how this model predicts, with high certainty, that the color of TNOs is the root cause of their inclination distribution, rather than the other way around. This strongly suggests that the colors of TNOs reflect an underlying dynamical property, most likely their formation location. Moreover, our causal model excludes formation scenarios that invoke substantial color modification by subsequent irradiation. We therefore conclude that the colors of TNOs are predominantly primordial.

Trans-Neptunian objects (TNOs) are small, icy bodies in the outer solar system. They are observed to have a complex orbital distribution that was shaped by the early dynamical history and migration of the giant planets. Comparisons between the different dynamical classes of modeled and observed TNOs can help constrain the history of the outer solar system. Because of the complex dynamics of TNOs, particularly those in and near mean motion resonances with Neptune, classification has traditionally been done by human inspection of plots of the time evolution of orbital parameters. This is very inefficient. The Vera Rubin Observatory's Legacy Survey of Space and Time (LSST) is expected to increase the number of known TNOs by a factor of $\sim$10, necessitating a much more automated process. In this chapter we present an improved supervised machine learning classifier for TNOs. Using a large and diverse training set as well as carefully chosen, dynamically motivated data features calculated from numerical integrations of TNO orbits, our classifier returns results that match those of a human classifier 98% of the time, and dynamically relevant classifications 99.7% of the time. This classifier is dramatically more efficient than human classification, and it will improve classification of both observed and modeled TNO data.

Not much progress is being made in the Netherlands when it comes to making homes sustainable. Many building owners are still trying to find how best to achieve this. At the same time, providers of sustainability solutions are having trouble scaling up. This impasse has not gone unnoticed by TNO, so they are using artificial intelligence to work out on a case-by-case basis which solutions are best suited to which homes. If you would like to know more about the algorithm that will make homes more sustainable, or if you are working on a similar project, please get in touch with Rogier Donkervoort.

The International Alliance for Mobility Testing & Standardization (IAMTS) announced three new members: NVIDIA, TNO and Smart Mobility Norway. The new members bring expertise in simulation, safety and proving ground operations that will benefit the ongoing work of the consortium. IAMTS is a global, membership-based alliance of stakeholders in the testing, standardization and certification of advanced mobility systems and services. "We are thrilled to have NVIDIA, TNO and Smart Mobility Norway add their expertise to build the best practices that support IAMTS," said Peter Doty, secretariat at IAMTS. "Each of these organizations understands that the correlation of virtual- and real-world-testing of connected and autonomous vehicles is key to efficient and safe deployment."