Incorporating Physical Knowledge into Machine Learning for Planetary Space Physics

Recent improvements in data collection volume from planetary and space physics missions have allowed the application of novel data science techniques. The Cassini mission for example collected over 600 gigabytes of scientific data from 2004 to 2017. This represents a surge of data on the Saturn system. Machine learning can help scientists work with data on this larger scale. Unlike many applications of machine learning, a primary use in planetary space physics applications is to infer behavior about the system itself. This raises three concerns: first, the performance of the machine learning model, second, the need for interpretable applications to answer scientific questions, and third, how characteristics of spacecraft data change these applications. In comparison to these concerns, uses of black box or un-interpretable machine learning methods tend toward evaluations of performance only either ignoring the underlying physical process or, less often, providing misleading explanations for it. We build off a previous effort applying a semi-supervised physics-based classification of plasma instabilities in Saturn's magnetosphere. We then use this previous effort in comparison to other machine learning classifiers with varying data size access, and physical information access. We show that incorporating knowledge of these orbiting spacecraft data characteristics improves the performance and interpretability of machine learning methods, which is essential for deriving scientific meaning. Building on these findings, we present a framework on incorporating physics knowledge into machine learning problems targeting semi-supervised classification for space physics data in planetary environments. These findings present a path forward for incorporating physical knowledge into space physics and planetary mission data analyses for scientific discovery.Comment: 25 pages, 7 figures, accepted for publication in Frontiers in Astronomy and Space Sciences for the Research Topic of Machine Learning in Heliophysics at https://www.frontiersin.org/articles/10.3389/fspas.2020.0003

Spectral and Spin Measurement of Two Small and Fast-Rotating Near-Earth Asteroids

In May 2012 two asteroids made near-miss "grazing" passes at distances of a few Earth-radii: 2012 KP24 passed at nine Earth-radii and 2012 KT42 at only three Earth-radii. The latter passed inside the orbital distance of geosynchronous satellites. From spectral and imaging measurements using NASA's 3-m Infrared Telescope Facility (IRTF), we deduce taxonomic, rotational, and physical properties. Their spectral characteristics are somewhat atypical among near-Earth asteroids: C-complex for 2012 KP24 and B-type for 2012 KT42, from which we interpret the albedos of both asteroids to be between 0.10 and 0.15 and effective diameters of 20+-2 and 6+-1 meters, respectively. Among B-type asteroids, the spectrum of 2012 KT42 is most similar to 3200 Phaethon and 4015 Wilson-Harrington. Not only are these among the smallest asteroids spectrally measured, we also find they are among the fastest-spinning: 2012 KP24 completes a rotation in 2.5008+-0.0006 minutes and 2012 KT42 rotates in 3.634+-0.001 minutes.Comment: 4 pages, 3 figures, accepted for publication in Icaru

Microfabrication of Optically Flat Silicon Micro-Mirrors for Fully Programmable Micro-Diffraction Gratings

AbstractWe have fabricated and characterized a Fully Programmable Micro-Diffraction Grating (FPMDG) with 64 silicon micro-mirrors for spectral shaping in the visible and near-infrared wavelength range. The FPMDG arrays of 50μm and 80μm wide and 700μm long silicon micro-mirrors have been fabricated in a process based on anodic bonding of an 8μm-SOI wafer and a borosilicate glass wafer. The detrimental bending of the micro-mirrors during electrostatic actuation has been minimized through separation of the mechanical and optical sections of the device. Flexures incorporating serpentine structures have been used to reduce the actuation dependence on length and thickness. Independent addressing of the micro-mirrors with negligible cross-talk and with bending of the micro-mirrors smaller than 0.14μm over 700μm have been demonstrated

Nonlinear software sensor for monitoring genetic regulation processes with noise and modeling errors

Nonlinear control techniques by means of a software sensor that are commonly used in chemical engineering could be also applied to genetic regulation processes. We provide here a realistic formulation of this procedure by introducing an additive white Gaussian noise, which is usually found in experimental data. Besides, we include model errors, meaning that we assume we do not know the nonlinear regulation function of the process. In order to illustrate this procedure, we employ the Goodwin dynamics of the concentrations [B.C. Goodwin, Temporal Oscillations in Cells, (Academic Press, New York, 1963)] in the simple form recently applied to single gene systems and some operon cases [H. De Jong, J. Comp. Biol. 9, 67 (2002)], which involves the dynamics of the mRNA, given protein, and metabolite concentrations. Further, we present results for a three gene case in co-regulated sets of transcription units as they occur in prokaryotes. However, instead of considering their full dynamics, we use only the data of the metabolites and a designed software sensor. We also show, more generally, that it is possible to rebuild the complete set of nonmeasured concentrations despite the uncertainties in the regulation function or, even more, in the case of not knowing the mRNA dynamics. In addition, the rebuilding of concentrations is not affected by the perturbation due to the additive white Gaussian noise and also we managed to filter the noisy output of the biological systemComment: 21 pages, 7 figures; also selected in vjbio of August 2005; this version corrects a misorder in the last three references of the published versio