Limits on Clouds and Hazes for the TRAPPIST-1 Planets

The TRAPPIST-1 planetary system is an excellent candidate for study of the evolution and habitability of M-dwarf planets. Transmission spectroscopy observations performed with the Hubble Space Telescope (HST) suggest the innermost five planets do not possess clear hydrogen atmospheres. Here we reassess these conclusions with recently updated mass constraints and expand the analysis to include limits on metallicity, cloud top pressure, and the strength of haze scattering. We connect recent laboratory results of particle size and production rate for exoplanet hazes to a one-dimensional atmospheric model for TRAPPIST-1 transmission spectra. Doing so, we obtain a physically-based estimate of haze scattering cross sections. We find haze scattering cross sections on the order of 1e-26 to 1e-19 cm squared are needed in hydrogen-rich atmospheres for TRAPPIST-1 d, e, and f to match the HST data. For TRAPPIST-1 g, we cannot rule out a clear hydrogen-rich atmosphere. We also modeled the effects an opaque cloud deck and substantial heavy element content have on the transmission spectra. We determine that hydrogen-rich atmospheres with high altitude clouds, at pressures of 12mbar and lower, are consistent with the HST observations for TRAPPIST-1 d and e. For TRAPPIST-1 f and g, we cannot rule out clear hydrogen-rich cases to high confidence. We demonstrate that metallicities of at least 60xsolar with tropospheric (0.1 bar) clouds agree with observations. Additionally, we provide estimates of the precision necessary for future observations to disentangle degeneracies in cloud top pressure and metallicity. Our results suggest secondary, volatile-rich atmospheres for the outer TRAPPIST-1 planets d, e, and f.Comment: 15 pages, 3 figures, 2 tables, accepted in the Astronomical Journa

Effect of the molecular structure of the polymer and nucleation on the optical properties of polypropylene homo- and copolymers.

Two soluble nucleating agents were used to modify the optical properties of nine PP homo- and random copolymers. The ethylene content of the polymers changed between 0 and 5.3 wt%. Chain regularity was characterized by the stepwise isothermal segregation technique (SIST), while optical properties by the measurement of the haze of injection molded samples. Crystallization and melting characteristics were determined by differential scanning calorimetry (DSC). The analysis of the results proved that lamella thickness and change in crystallinity influence haze only slightly. A model was introduced which describes quantitatively the dependence of nucleation efficiency and haze on the concentration of the nucleating agent. The model assumes that the same factors influence the peak temperature of crystallization and optical properties. The analysis of the results proved that the assumption is valid under the same crystallization conditions. The parameters of the model depend on the molecular architecture of the polymer. Chain regularity determines supermolecular structure and thus the dependence of optical properties on nucleation

MODIS: Moderate-resolution imaging spectrometer. Earth observing system, volume 2B

The Moderate-Resolution Imaging Spectrometer (MODIS), as presently conceived, is a system of two imaging spectroradiometer components designed for the widest possible applicability to research tasks that require long-term (5 to 10 years), low-resolution (52 channels between 0.4 and 12.0 micrometers) data sets. The system described is preliminary and subject to scientific and technological review and modification, and it is anticipated that both will occur prior to selection of a final system configuration; however, the basic concept outlined is likely to remain unchanged

Frequency Compensated Diffusion Model for Real-scene Dehazing

Due to distribution shift, deep learning based methods for image dehazing suffer from performance degradation when applied to real-world hazy images. In this paper, we consider a dehazing framework based on conditional diffusion models for improved generalization to real haze. First, we find that optimizing the training objective of diffusion models, i.e., Gaussian noise vectors, is non-trivial. The spectral bias of deep networks hinders the higher frequency modes in Gaussian vectors from being learned and hence impairs the reconstruction of image details. To tackle this issue, we design a network unit, named Frequency Compensation block (FCB), with a bank of filters that jointly emphasize the mid-to-high frequencies of an input signal. We demonstrate that diffusion models with FCB achieve significant gains in both perceptual and distortion metrics. Second, to further boost the generalization performance, we propose a novel data synthesis pipeline, HazeAug, to augment haze in terms of degree and diversity. Within the framework, a solid baseline for blind dehazing is set up where models are trained on synthetic hazy-clean pairs, and directly generalize to real data. Extensive evaluations show that the proposed dehazing diffusion model significantly outperforms state-of-the-art methods on real-world images.Comment: 16 page