The 1998 November 14 Occultation of GSC 0622-00345 by Saturn. II. Stratospheric Thermal Profile, Power Spectrum, and Gravity Waves

On 1998 November 14, Saturn and its rings occulted the star GSC 0622-00345. The occultation latitude was 55.5 degrees S. This paper analyzes the 2.3 {\mu}m light curve derived by Harrington & French. A fixed-baseline isothermal fit to the light curve has a temperature of 140 +/- 3 K, assuming a mean molecular mass of 2.35 AMU. The thermal profile obtained by numerical inversion is valid between 1 and 60 {\mu}bar. The vertical temperature gradient is >0.2 K/km more stable than the adiabatic lapse rate, but it still shows the alternating-rounded-spiked features seen in many temperature gradient profiles from other atmospheric occultations and usually attributed to breaking gravity (buoyancy) waves. We conduct a wavelet analysis of the thermal profile, and show that, even with our low level of noise, scintillation due to turbulence in Earth's atmosphere can produce large temperature swings in light-curve inversions. Spurious periodic features in the "reliable" region of a wavelet amplitude spectrum can exceed 0.3 K in our data. We also show that gravity-wave model fits to noisy isothermal light curves can lead to convincing wave "detections". We provide new significance tests for localized wavelet amplitudes, wave model fits, and global power spectra of inverted occultation light curves by assessing the effects of pre- and post-occultation noise on these parameters. Based on these tests, we detect several significant ridges and isolated peaks in wavelet amplitude, to which we fit a gravity wave model. We also strongly detect the global power spectrum of thermal fluctuations in Saturn's atmosphere, which resembles the "universal" (modified Desaubies) curve associated with saturated spectra of propagating gravity waves on Earth and Jupiter.Comment: LaTeX/emulateapj, 13 pages, 7 figure

Is the Machine Smarter than the Theorist: Deriving Formulas for Particle Kinematics with Symbolic Regression

We demonstrate the use of symbolic regression in deriving analytical formulas, which are needed at various stages of a typical experimental analysis in collider phenomenology. As a first application, we consider kinematic variables like the stransverse mass, $M_{T2}$, which are defined algorithmically through an optimization procedure and not in terms of an analytical formula. We then train a symbolic regression and obtain the correct analytical expressions for all known special cases of $M_{T2}$ in the literature. As a second application, we reproduce the correct analytical expression for a next-to-leading order (NLO) kinematic distribution from data, which is simulated with a NLO event generator. Finally, we derive analytical approximations for the NLO kinematic distributions after detector simulation, for which no known analytical formulas currently exist.Comment: 15 pages, 13 figures, 8 table

Searching for Novel Chemistry in Exoplanetary Atmospheres using Machine Learning for Anomaly Detection

The next generation of telescopes will yield a substantial increase in the availability of high-resolution spectroscopic data for thousands of exoplanets. The sheer volume of data and number of planets to be analyzed greatly motivate the development of new, fast and efficient methods for flagging interesting planets for reobservation and detailed analysis. We advocate the application of machine learning (ML) techniques for anomaly (novelty) detection to exoplanet transit spectra, with the goal of identifying planets with unusual chemical composition and even searching for unknown biosignatures. We successfully demonstrate the feasibility of two popular anomaly detection methods (Local Outlier Factor and One Class Support Vector Machine) on a large public database of synthetic spectra. We consider several test cases, each with different levels of instrumental noise. In each case, we use ROC curves to quantify and compare the performance of the two ML techniques.Comment: Submitted to AAS Journals, 30 pages, 14 figure

Identifying the Group-Theoretic Structure of Machine-Learned Symmetries

Deep learning was recently successfully used in deriving symmetry transformations that preserve important physics quantities. Being completely agnostic, these techniques postpone the identification of the discovered symmetries to a later stage. In this letter we propose methods for examining and identifying the group-theoretic structure of such machine-learned symmetries. We design loss functions which probe the subalgebra structure either during the deep learning stage of symmetry discovery or in a subsequent post-processing stage. We illustrate the new methods with examples from the U(n) Lie group family, obtaining the respective subalgebra decompositions. As an application to particle physics, we demonstrate the identification of the residual symmetries after the spontaneous breaking of non-Abelian gauge symmetries like SU(3) and SU(5) which are commonly used in model building.Comment: 10 pages, 8 figures, 2 table

Discovering Sparse Representations of Lie Groups with Machine Learning

Recent work has used deep learning to derive symmetry transformations, which preserve conserved quantities, and to obtain the corresponding algebras of generators. In this letter, we extend this technique to derive sparse representations of arbitrary Lie algebras. We show that our method reproduces the canonical (sparse) representations of the generators of the Lorentz group, as well as the $U(n)$ and $SU(n)$ families of Lie groups. This approach is completely general and can be used to find the infinitesimal generators for any Lie group.Comment: 14 pages, 6 figure

Midlatitude and high‐latitude electron density profiles in the ionosphere of Saturn obtained by Cassini radio occultation observations

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95024/1/jgra19781.pd

Cassini radio occultations of Saturn's ionosphere: Model comparisons using a constant water flux

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94688/1/grl22099.pd

Upper atmospheres and ionospheres of planets and satellites

The upper atmospheres of the planets and their satellites are more directly exposed to sunlight and solar wind particles than the surface or the deeper atmospheric layers. At the altitudes where the associated energy is deposited, the atmospheres may become ionized and are referred to as ionospheres. The details of the photon and particle interactions with the upper atmosphere depend strongly on whether the object has anintrinsic magnetic field that may channel the precipitating particles into the atmosphere or drive the atmospheric gas out to space. Important implications of these interactions include atmospheric loss over diverse timescales, photochemistry and the formation of aerosols, which affect the evolution, composition and remote sensing of the planets (satellites). The upper atmosphere connects the planet (satellite) bulk composition to the near-planet (-satellite) environment. Understanding the relevant physics and chemistry provides insight to the past and future conditions of these objects, which is critical for understanding their evolution. This chapter introduces the basic concepts of upper atmospheres and ionospheres in our solar system, and discusses aspects of their neutral and ion composition, wind dynamics and energy budget. This knowledge is key to putting in context the observations of upper atmospheres and haze on exoplanets, and to devise a theory that explains exoplanet demographics.Comment: Invited Revie

Jupiter Thermospheric General Circulation Model (JTGCM): Global structure and dynamics driven by auroral and Joule heating

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94793/1/jgre1837.pd

Processes of auroral thermal structure at Jupiter: Analysis of multispectral temperature observations with the Jupiter Thermosphere General Circulation Model

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95248/1/jgre2561.pd