Machine learning applications on lunar meteorite minerals:From classification to mechanical properties prediction

Amid the scarcity of lunar meteorites and the imperative to preserve their scientific value,nondestructive testing methods are essential.This translates into the application of microscale rock mechanics experiments and scanning electron microscopy for surface composition analysis.This study explores the application of Machine Learning algorithms in predicting the mineralogical and mechanical properties of DHOFAR 1084,JAH 838,and NWA 11444 lunar meteorites based solely on their atomic percentage compositions.Leveraging a prior-data fitted network model,we achieved near-perfect classification scores for meteorites,mineral groups,and individual minerals.The regressor models,notably the KNeighbor model,provided an outstanding estimate of the mechanical properties—previously measured by nanoindentation tests—such as hardness,reduced Young’s modulus,and elastic recovery.Further considerations on the nature and physical properties of the minerals forming these meteorites,including porosity,crystal orientation,or shock degree,are essential for refining predictions.Our findings underscore the potential of Machine Learning in enhancing mineral identification and mechanical property estimation in lunar exploration,which pave the way for new advancements and quick assessments in extraterrestrial mineral mining,processing,and research.

Low-power SiPM readout BETA ASIC for space applications

The BETA application-specific integrated circuit(ASIC)is a fully programmable chip designed to amplify,shape and digitize the signal of up to 64 Silicon photomultiplier(SiPM)channels,with a power consumption of approximately~1 mW/channel.Owing to its dual-path gain,the BETA chip is capable of resolving single photoelectrons(phes)with a signal-to-noise ratio(SNR)>5 while simultaneously achieving a dynamic range of~4000 phes.Thus,BETA can provide a cost-effective solution for the readout of SiPMs in space missions and other applications with a maximum rate below 10 kHz.In this study,we describe the key characteristics of the BETA ASIC and present an evaluation of the performance of its 16-channel version,which is implemented using 130 nm technology.The ASIC also contains two discriminators that can provide trigger signals with a time jitter down to 400 ps FWHM for 10 phes.The linearity error of the charge gain measurement was less than 2%for a dynamic range as large as 15 bits.

Using fireball networks to track more frequent reentries:Falcon 9 upperstage orbit determination from video recordings

On February 16,2021,an artificial object moving slowly over the Mediterranean was recorded by the Spanish Meteor Network(SPMN).Based on astrometric measurements,we identified this event as the reentry engine burn of a SpaceX Falcon 9 launch vehicle’s upper stage.To study this event in detail,we adapted the plane intersection method for near-straight meteoroid trajectories to analyze the slow and curved orbits associated with artificial objects.To corroborate our results,we approximated the orbital elements of the upper stage using four pieces of“debris”cataloged by the U.S.Government’s Combined Space Operations Center.Based on these calculations,we also estimated the possible deorbit hazard zone using the MSISE90 model atmosphere.We provide guidance regarding the interference that these artificial bolides may generate in fireball studies.Additionally,because artificial bolides will likely become more frequent in the future,we point out the new role that ground-based detection networks can play in the monitoring of potentially hazardous artificial objects in near-Earth space and in determining the strewn fields of artificial space debris.