Walking along a beach one may notice debris being washed ashore from the vast oceans. Then, turning your head up at night you even might noticed a shooting star or a bright spot passing by. Chances are, that you witnessed space debris, endangering future space flight in lower earth orbit. If it was possible to turn cm-sized debris into shooting stars the problem might be averted. Unfortunately, these fragments counting in the 100 thousands are not controllable. To possibly regain control we demonstrate how to exert forces on a free falling debris object from a distance by ablating material with a high energy ns-laser-system. Thrust effects did scale as expected from simulations and led to speed gains above 0.3 m/s per laser pulse in an evacuated micro-gravity environment.

Small space debris objects of even a few centimeters can cause severe damage to satellites. Powerful lasers are often proposed for pushing small debris by laser-ablative recoil toward an orbit where atmospheric burn-up yields their remediation. We analyze whether laser-ablative momentum generation is safe and reliable concerning predictability of momentum and accumulation of heat at the target. With hydrodynamic simulations on laser ablation of aluminum as the prevalent debris material, we study laser parameter dependencies of thermomechanical coupling. The results serve as configuration for raytracing-based Monte Carlo simulations on imparted momentum and heat of randomly shaped fragments within a Gaussian laser spot. Orbit modification and heating are analyzed exemplarily under repetitive laser irradiation. Short wavelengths are advantageous, yielding momentum coupling up to ∼40 mNs∕kJ, and thermal coupling can be minimized to 7% of the pulse energy using short-laser pulses. Random target orientation yields a momentum uncertainty of 86% and the thrust angle exhibits 40% scatter around 45 deg. Moreover, laser pointing errors at least redouble the uncertainty in momentum prediction. Due to heat accumulation of a few Kelvin per pulse, their number is restricted to allow for intermediate cooldown. Momentum scatter requires a sound collision analysis for conceivable trajectory modifications. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

A fundamental prediction of the ΛCDM cosmology is the hierarchical build-up of structure and therefore the successive merging of galaxies into more massive ones. As one can only observe galaxies at one specific time in cosmic history, this merger history remains in principle unobservable. By using the TNG100 simulation of the IllustrisTNG project, we show that it is possible to infer the unobservable stellar assembly and merger history of central galaxies from their observable properties by using machine learning techniques. In particular, in this first paper of ERGO-ML (Extracting Reality from Galaxy Observables with Machine Learning), we choose a set of 7 observable integral properties of galaxies (i.e. total stellar mass, redshift, color, stellar size, morphology, metallicity, and age) to infer, from those, the stellar ex-situ fraction, the average merger lookback times and mass ratios, and the lookback time and stellar mass of the last major merger. To do so, we use and compare a Multilayer Perceptron Neural Network and a conditional Invertible Neural Network (cINN): thanks to the latter we are also able to infer the posterior distribution for these parameters and hence estimate the uncertainties in the predictions. We find that the stellar ex-situ fraction and the time of the last major merger are well determined by the selected set of observables, that the mass-weighted merger mass ratio is unconstrained, and that, beyond stellar mass, stellar morphology and stellar age are the most informative properties. Finally, we show that the cINN recovers the remaining unexplained scatter and secondary cross-correlations. Our tools can be applied to large galaxy surveys in order to infer unobservable properties of galaxies' past, enabling empirical studies of galaxy evolution enriched by cosmological simulations.

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