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Fractures are ubiquitous and can lead to the catastrophic material failure of materials. Although fracturing in a two-dimensional plane is well understood, all fractures are extended in and propagate through three-dimensional space. Moreover, their behaviour is complex. Here we show that the forward propagation of a fracture front occurs through an initial rupture, nucleated at some localized position, followed by a very rapid transverse expansion at velocities as high as the Rayleigh-wave speed. We study fracturing in a circular geometry that achieves an uninterrupted extended fracture front and use a fluid to control the loading conditions that determine the amplitude of the forward jump. We find that this amplitude correlates with the transverse velocity. Dynamic rupture simulations capture the observations for only a high transverse velocity. These results highlight the importance of transverse dynamics in the forward propagation of an extended fracture.

 

Abstract X-ray bursts are among the brightest stellar objects frequently observed in the sky by space-based telescopes. A type-I X-ray burst is understood as a violent thermonuclear explosion on the surface of a neutron star, accreting matter from a companion star in a binary system. The bursts are powered by a nuclear reaction sequence known as the rapid proton capture process (rp process), which involves hundreds of exotic neutron-deficient nuclides. At so-called waiting-point nuclides, the process stalls until a slower β + decay enables a bypass. One of the handful of rp process waiting-point nuclides is 64 Ge, which plays a decisive role in matter flow and therefore the produced X-ray flux. Here we report precision measurements of the masses of 63 Ge, 64,65 As and 66,67 Se—the relevant nuclear masses around the waiting-point 64 Ge—and use them as inputs for X-ray burst model calculations. We obtain the X-ray burst light curve to constrain the neutron-star compactness, and suggest that the distance to the X-ray burster GS 1826–24 needs to be increased by about 6.5% to match astronomical observations. The nucleosynthesis results affect the thermal structure of accreting neutron stars, which will subsequently modify the calculations of associated observables. 

Abstract We present mid-infrared spectroscopic observations of the nucleus of the nearby Seyfert galaxy NGC 7469 taken with the MIRI instrument on the James Webb Space Telescope (JWST) as part of Directors Discretionary Time Early Release Science program 1328. The high-resolution nuclear spectrum contains 19 emission lines covering a wide range of ionization. The high-ionization lines show broad, blueshifted emission reaching velocities up to 1700 km s −1 and FWHM ranging from ∼500 to 1100 km s −1 . The width of the broad emission and the broad-to-narrow line flux ratios correlate with ionization potential. The results suggest a decelerating, stratified, AGN-driven outflow emerging from the nucleus. The estimated mass outflow rate is 1–2 orders of magnitude larger than the current black hole accretion rate needed to power the AGN. Eight pure rotational H 2 emission lines are detected with intrinsic widths ranging from FWHM ∼125 to 330 km s −1 . We estimate a total mass of warm H 2 gas of ∼1.2 × 10 7 M ⊙ in the central 100 pc. The PAH features are extremely weak in the nuclear spectrum, but a 6.2 μ m PAH feature with an equivalent width of ∼0.07 μ m and a flux of 2.7 × 10 −17 W m −2 is detected. The spectrum is steeply rising in the mid-infrared, with a silicate strength of ∼0.02, significantly smaller than seen in most PG QSOs but comparable to other Seyfert 1s. These early MIRI mid-infrared IFU data highlight the power of JWST to probe the multiphase interstellar media surrounding actively accreting supermassive black holes. 

While energy-based models (EBMs) exhibit a number of desirable properties, training and sampling on high-dimensional datasets remains challenging. Inspired by recent progress on diffusion probabilistic models, we present a diffusion re- covery likelihood method to tractably learn and sample from a sequence of EBMs trained on increasingly noisy versions of a dataset. Each EBM is trained with recovery likelihood, which maximizes the conditional probability of the data at a certain noise level given their noisy versions at a higher noise level. Optimizing re- covery likelihood is more tractable than marginal likelihood, as sampling from the conditional distributions is much easier than sampling from the marginal distribu- tions. After training, synthesized images can be generated by the sampling process that initializes from Gaussian white noise distribution and progressively samples the conditional distributions at decreasingly lower noise levels. Our method gener- ates high fidelity samples on various image datasets. On unconditional CIFAR-10 our method achieves FID 9.58 and inception score 8.30, superior to the majority of GANs. Moreover, we demonstrate that unlike previous work on EBMs, our long-run MCMC samples from the conditional distributions do not diverge and still represent realistic images, allowing us to accurately estimate the normalized density of data even for high-dimensional datasets. Our implementation is avail- able at https://github.com/ruiqigao/recovery_likelihood.