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We apply the color–magnitude intercept calibration method (CMAGIC) to the Nearby Supernova Factory SNe Ia spectrophotometric data set. The currently existing CMAGIC parameters are the slope and intercept of a straight line fit to the linear region in the color–magnitude diagram, which occurs over a span of approximately 30 days after maximum brightness. We define a new parameter,ω_XY, the size of the “bump” feature near maximum brightness for arbitrary filtersXandY. We find a significant correlation between the slope of the linear region,β_XY, in the CMAGIC diagram andω_XY. These results may be used to our advantage, as they are less affected by extinction than parameters defined as a function of time. Additionally,ω_XYis computed independently of templates. We find that current empirical templates are successful at reproducing the features described in this work, particularly SALT3, which correctly exhibits the negative correlation between slope and “bump” size seen in our data. In 1D simulations, we show that the correlation between the size of the “bump” feature andβ_XYcan be understood as a result of chemical mixing due to large-scale Rayleigh–Taylor instabilities.

 

Abstract We construct a physically parameterized probabilistic autoencoder (PAE) to learn the intrinsic diversity of Type Ia supernovae (SNe Ia) from a sparse set of spectral time series. The PAE is a two-stage generative model, composed of an autoencoder that is interpreted probabilistically after training using a normalizing flow. We demonstrate that the PAE learns a low-dimensional latent space that captures the nonlinear range of features that exists within the population and can accurately model the spectral evolution of SNe Ia across the full range of wavelength and observation times directly from the data. By introducing a correlation penalty term and multistage training setup alongside our physically parameterized network, we show that intrinsic and extrinsic modes of variability can be separated during training, removing the need for the additional models to perform magnitude standardization. We then use our PAE in a number of downstream tasks on SNe Ia for increasingly precise cosmological analyses, including the automatic detection of SN outliers, the generation of samples consistent with the data distribution, and solving the inverse problem in the presence of noisy and incomplete data to constrain cosmological distance measurements. We find that the optimal number of intrinsic model parameters appears to be three, in line with previous studies, and show that we can standardize our test sample of SNe Ia with an rms of 0.091 ± 0.010 mag, which corresponds to 0.074 ± 0.010 mag if peculiar velocity contributions are removed. Trained models and codes are released at https://github.com/georgestein/suPAErnova. 

Abstract The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program User Facility produces ground-based long-term continuous unique measurements for atmospheric state, precipitation, turbulent fluxes, radiation, aerosol, cloud, and the land surface, which are collected at multiple sites. These comprehensive datasets have been widely used to calibrate climate models and are proven to be invaluable for climate model development and improvement. This article introduces an evaluation package to facilitate the use of ground-based ARM measurements in climate model evaluation. The ARM data-oriented metrics and diagnostics package (ARM-DIAGS) includes both ARM observational datasets and a Python-based analysis toolkit for computation and visualization. The observational datasets are compiled from multiple ARM data products and specifically tailored for use in climate model evaluation. In addition, ARM-DIAGS also includes simulation data from models participating the Coupled Model Intercomparison Project (CMIP), which will allow climate-modeling groups to compare a new, candidate version of their model to existing CMIP models. The analysis toolkit is designed to make the metrics and diagnostics quickly available to the model developers. 

We calibrate spectrophotometric optical spectra of 32 stars commonly used as standard stars, referenced to 14 stars already on the Hubble Space Telescope–based CALSPEC flux system. Observations of CALSPEC and non-CALSPEC stars were obtained with the SuperNova Integral Field Spectrograph over the wavelength range 3300–9400 Å as calibration for the Nearby Supernova Factory cosmology experiment. In total, this analysis used 4289 standard-star spectra taken on photometric nights. As a modern cosmology analysis, all presubmission methodological decisions were made with the flux scale and external comparison results blinded. The large number of spectra per star allows us to treat the wavelength-by-wavelength calibration for all nights simultaneously with a Bayesian hierarchical model, thereby enabling a consistent treatment of the Type Ia supernova cosmology analysis and the calibration on which it critically relies. We determine the typical per-observation repeatability (median 14 mmag for exposures ≳5 s), the Maunakea atmospheric transmission distribution (median dispersion of 7 mmag with uncertainty 1 mmag), and the scatter internal to our CALSPEC reference stars (median of 8 mmag). We also check our standards against literature filter photometry, finding generally good agreement over the full 12 mag range. Overall, the mean of our system is calibrated to the mean of CALSPEC at the level of ∼3 mmag. With our large number of observations, careful cross-checks, and 14 reference stars, our results are the best calibration yet achieved with an integral-field spectrograph, and among the best calibrated surveys.

 

Solar module recycling is unprofitable today. In this paper potential revenues from waste Si modules are analyzed. The biggest revenue potential comes from the Si cells, extracted intact or broken. The second revenue source is the bulky materials in the modules including Al frame, Cu wiring and glass. The total revenue is estimated between US$11–30/module depending on the percentage of cells extracted intact. This revenue is 4–10 times better than today’s recycling process that recovers only the bulky materials. Experimentally a special furnace has been demonstrated to successfully separate thin commercial Si cells of 160 microns from glass unbroken. From damaged cells a new chemistry has been developed to recover solar-grade Si and Ag. It requires fewer steps than today’s recycling process, with Ag recovery of 97% and Si recovery of 90%. A prototype recycling line is needed to assess the cost of the new process. 

Solar module recycling is unprofitable today. In this paper potential revenues from waste Si modules are analyzed. The biggest revenue potential comes from the Si cells, extracted intact or broken. The second revenue source is the bulky materials in the modules including Al frame, Cu wiring and glass. The total revenue is estimated between US$11–30/module depending on the percentage of cells extracted intact. This revenue is 4–10 times better than today’s recycling process that recovers only the bulky materials. Experimentally a special furnace has been demonstrated to successfully separate thin commercial Si cells of 160 microns from glass unbroken. From damaged cells a new chemistry has been developed to recover solar-grade Si and Ag. It requires fewer steps than today’s recycling process, with Ag recovery of 97% and Si recovery of 90%. A prototype recycling line is needed to assess the cost of the new process. 

The dissipation processes which transform electromagnetic energy into kinetic particle energy in space plasmas are still not fully understood. Of particular interest is the distribution of the dissipated energy among different species of charged particles. The Jovian magnetosphere is a unique laboratory to study this question because outflowing ions from the moon Io create a high diversity in ion species. In this work, we use multispecies ion observations and magnetic field measurements by the Galileo spacecraft. We limit our study to observations of plasmoids in the Jovian magnetotail, because there is strong ion acceleration in these structures. Our model predicts that electromagnetic turbulence in plasmoids plays an essential role in the acceleration of oxygen, sulfur, and hydrogen ions. The observations show a decrease of the oxygen and sulfur energy spectral indexγat ∼30 to ∼400 keV/nuc with the wave power indicating an energy transfer from electromagnetic waves to particles, in agreement with the model. The wave power threshold for effective acceleration is of the order of 10 nT²Hz⁻¹, as in terrestrial plasmoids. However, this is not observed for hydrogen ions, implying that processes other than wave‐particle interaction are more important for the acceleration of these ions or that the time and energy resolution of the observations is too coarse. The results are expected to be confirmed by improved plasma measurements by the Juno spacecraft.