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Herein we report the direct observation of C–H bond activation at an isolated mononuclear Pd( iii ) center. The oxidation of the Pd( ii ) complex ( Me N4)Pd II (neophyl)Cl (neophyl = –CH 2 C(CH 3 ) 2 Ph; Me N4 = N , N ′-dimethyl-2,11-diaza[3.3](2,6)pyridinophane) using the mild oxidant ferrocenium hexafluorophosphate (FcPF 6 ) yields the stable Pd( iii ) complex [( Me N4)Pd III (neophyl)Cl]PF 6 . Upon the addition of an acetate source, [( Me N4)Pd III (neophyl)Cl]PF 6 undergoes Csp 2 –H bond activation to yield the cyclometalated product [( Me N4)Pd III (cycloneophyl)]PF 6 . This metalacycle can be independently prepared, allowing for a complete characterization of both the starting and final Pd( iii ) complexes. The C–H activation step can be monitored directly by EPR and UV-Vis spectroscopies, and kinetic isotope effect (KIE) studies suggest that either a pre-association step such as an agostic interaction may be rate limiting, or that the C–H activation is partially rate-limiting in conjunction with ligand rearrangement. Density functional theory calculations support that the reaction proceeds through a κ 3 ligand coordination and that the flexible ligand structure is important for this transformation. Overall, this study represents the first example of discrete C–H bond activation occurring at a Pd( iii ) center through a concerted metalation–deprotonation mechanism, akin to that observed for Pd( ii ) and Pd( iv ) centers. 

The 1D power spectrum P1D of the Ly α forest provides important information about cosmological and astrophysical parameters, including constraints on warm dark matter models, the sum of the masses of the three neutrino species, and the thermal state of the intergalactic medium. We present the first measurement of P1D with the quadratic maximum likelihood estimator (QMLE) from the Dark Energy Spectroscopic Instrument (DESI) survey early data sample. This early sample of 54 600 quasars is already comparable in size to the largest previous studies, and we conduct a thorough investigation of numerous instrumental and analysis systematic errors to evaluate their impact on DESI data with QMLE. We demonstrate the excellent performance of the spectroscopic pipeline noise estimation and the impressive accuracy of the spectrograph resolution matrix with 2D image simulations of raw DESI images that we processed with the DESI spectroscopic pipeline. We also study metal line contamination and noise calibration systematics with quasar spectra on the red side of the Ly α emission line. In a companion paper, we present a similar analysis based on the Fast Fourier Transform estimate of the power spectrum. We conclude with a comparison of these two approaches and discuss the key sources of systematic error that we need to address with the upcoming DESI Year 1 analysis.

 

A new Cloud Imaging and Particle Size (CIPS) gravity wave (GW) variance data set is available that facilitates automated analysis of GWs entering the mesosphere. This work examines several years of CIPS GW variances from 50 to 55 km in the context of the Arctic and Antarctic polar vortices. CIPS observes highest GW activity in the vortex edge region where horizontal wind speeds are largest, consistent with previously published GW climatologies in the stratosphere and mesosphere. CIPS observes the well‐documented planetary wave (PW)‐1 patterns in GW activity in both hemispheres. In the Northern Hemisphere, maximum GW activity occurs over the North Atlantic and western Europe. In the Southern Hemisphere, maximum GW activity stretches from the Andes over the South Atlantic and Indian Oceans, as expected. In the NH, CIPS GW spatial patterns are highly correlated with horizontal wind speed. In the SH, CIPS GW patterns are less positively correlated with the winds due to increased zonal symmetry and orographic forcing. The Andes Mountains and Antarctic Peninsula, South Georgia Island, Kerguelen/Heard Islands, New Zealand, and Tasmania are persistent sources of orographic GWs. Atmospheric Infrared sounder observations of stratospheric GWs are analyzed alongside CIPS to explore vertical GW coherence and to infer GW propagation and sources. NH midlatitude GW activity is reduced during the January 2021 SSW, as expected. This reduction in GWs leads to a simultaneous reduction in traveling ionospheric disturbances (TIDs), providing more evidence that weak polar vortex events with weak GW activity leads to reduced daytime TID activity.

 

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.

 

Scientists of the Steward Observatory and Wyant College of Optical Sciences at the University of Arizona created a highly energy efficient adaptable method of forming precision freeform metal panels utilizing the combined effects of induction heating, electromagnetic force, an adjustable mold assembly, and infrared and visible metrology. This transformative component-level manufacturing technology has a broad application in industries requiring custom shaped high accuracy metal sheets (radio communication, automotive, aerospace, renewable energy, architecture). 

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.