Search for: All records

Giant planets grow by accreting gas through circumplanetary disks, but little is known about the timescale and mechanisms involved in the planet-assembly process because few accreting protoplanets have been discovered. Recent visible and infrared imaging revealed a potential accreting protoplanet within the transition disk around the young intermediate-mass Herbig Ae star, AB Aurigae (AB Aur). Additional imaging in Hαprobed for accretion and found agreement between the line-to-continuum flux ratio of the star and companion, raising the possibility that the emission source could be a compact disk feature seen in scattered starlight. We present new deep Keck/NIRC2 high-contrast imaging of AB Aur to characterize emission in Paβ, another accretion tracer less subject to extinction. Our narrow band observations reach a 5σcontrast of 9.6 mag at 0.″6, but we do not detect significant emission at the expected location of the companion, nor from other any other source in the system. Our upper limit on Paβemission suggests that if AB Aur b is a protoplanet, it is not heavily accreting or accretion is stochastic and was weak during the observations.

 

Brown dwarfs (BDs) in ultra-short-period orbits around white dwarfs (WDs) offer a unique opportunity to study the properties of tidally locked, fast-rotating (1–3 hr), and highly irradiated atmospheres. Here we present phase-resolved spectrophotometry of the WD–BD binary SDSS 1557, which is the fifth WD–BD binary in our six-object sample. Using the Hubble Space Telescope Wide Field Camera 3 Near-infrared G141 instrument, the 1.1–1.7μm phase curves show rotational modulations with semiamplitudes of 10.5% ± 0.1%. We observe a wavelength-dependent amplitude, with longer wavelengths producing larger amplitudes, while no wavelength-dependent phase shifts were identified. The phase-resolved extracted BD spectra exhibit steep slopes and are nearly featureless. A simple radiative energy redistribution atmospheric model re-creates the hemisphere-integrated brightness temperatures at three distinct phases and finds evidence for weak redistribution efficiency. Our model also predicts a higher inclination than previously published. We find that SDSS 1557B, the second most irradiated BD in our sample, is likely dominated by clouds on the nightside, whereas the featureless dayside spectrum is likely dominated by H⁻opacity and a temperature inversion, much like the other highly irradiated BD EPIC 2122B.

 

Prior to fracture, a polyacrylamide hydrogel has a stress-stretch curve of nearly perfect elasticity, but it has been suggested that an inelastic zone exists around a crack tip. This inelastic zone, however, has never been observed directly in a polyacrylamide hydrogel. Here we identify the inelastic zone using digital image correlation (DIC). We prepare a polyacrylamide hydrogel with a precut crack. While a sample of the hydrogel is stretched, the speckle patterns are recorded using a microscope or a camera, with pixel size 2.3 μm and 22.7 μm, respectively. The speckle patterns recorded by the microscope and camera are processed using the DIC software, and merged to provide the deformation field over the entire sample. The measured field of deformation is used to calculate the field of energy density according to the neo-Hookean model. When the body is perfectly elastic, the field of energy density around the crack tip is inversely proportional to the distance from the crack tip. The difference between the measured field and the predicted elastic field identifies the inelastic zone. The measured size of the inelastic zone is ∼ 0.6 mm. We further confirm that, when a sample is much larger than the inelastic zone, an annulus exists, in which the elastic crack tip field prevails. 

Training language-conditioned policies is typically time-consuming and resource-intensive. Additionally, the resulting controllers are tailored to the specific robot they were trained on, making it difficult to transfer them to other robots with different dynamics. To address these challenges, we propose a new approach called Hierarchical Modularity, which enables more efficient training and subsequent transfer of such policies across different types of robots. The approach incorporates Supervised Attention which bridges the gap between modular and end-to-end learning by enabling the re-use of functional building blocks. In this contribution, we build upon our previous work, showcasing the extended utilities and improved performance by expanding the hierarchy to include new tasks and introducing an automated pipeline for synthesizing a large quantity of novel objects. We demonstrate the effectiveness of this approach through extensive simulated and real-world robot manipulation experiments.

 

Clustered regularly interspaced short palindromic repeats (CRISPR) screening coupled with single-cell RNA sequencing has emerged as a powerful tool to characterize the effects of genetic perturbations on the whole transcriptome at a single-cell level. However, due to its sparsity and complex structure, analysis of single-cell CRISPR screening data is challenging. In particular, standard differential expression analysis methods are often underpowered to detect genes affected by CRISPR perturbations. We developed a statistical method for such data, called guided sparse factor analysis (GSFA). GSFA infers latent factors that represent coregulated genes or gene modules; by borrowing information from these factors, it infers the effects of genetic perturbations on individual genes. We demonstrated through extensive simulation studies that GSFA detects perturbation effects with much higher power than state-of-the-art methods. Using single-cell CRISPR data from human CD8⁺T cells and neural progenitor cells, we showed that GSFA identified biologically relevant gene modules and specific genes affected by CRISPR perturbations, many of which were missed by existing methods, providing new insights into the functions of genes involved in T cell activation and neurodevelopment.

 

Abstract The photometric and spectral variability of brown dwarfs probes heterogeneous temperature and cloud distributions and traces the atmospheric circulation patterns. We present a new 42 hr Hubble Space Telescope (HST) Wide Field Camera 3 G141 spectral time series of VHS 1256-1257 b, a late L-type planetary-mass companion that has been shown to have one of the highest variability amplitudes among substellar objects. The light curve is rapidly evolving and best fit by a combination of three sine waves with different periods and a linear trend. The amplitudes of the sine waves and the linear slope vary with the wavelength, and the corresponding spectral variability patterns match the predictions by models invoking either heterogeneous clouds or thermal profile anomalies. Combining these observations with previous HST monitoring data, we find that the peak-to-valley flux difference is 33% ± 2% with an even higher amplitude reaching 38% in the J band, the highest amplitude ever observed in a substellar object. The observed light curve can be explained by maps that are composed of zonal waves, spots, or a mixture of the two. Distinguishing the origin of rapid light curve evolution requires additional long-term monitoring. Our findings underscore the essential role of atmospheric dynamics in shaping brown-dwarf atmospheres and highlight VHS 1256-1257 b as one of the most favorable targets for studying the atmospheres, clouds, and atmospheric circulation of planets and brown dwarfs.