Search for broad absorption lines in spectra of stars in the field of supernova remnant RX J0852.0-4622 (Vela Jr.)

Supernova remnant (SNR) RX J0852.0-4622 is one of the youngest and is most likely the closest among known galactic supernova remnants (SNRs). It was detected in X-rays, the 44Ti gamma-line, and radio. We obtain and analyze medium-resolution spectra of 14 stars in the direction towards the SNR RX J0852.0-4622 in an attempt to detect broad absorption lines of unshocked ejecta against background stars. Spectral synthesis is performed for all the stars in the wavelength range of 3740-4020AA to extract the broad absorption lines of Ca II related to the SNR RX J0852.0-4622. We do not detect any broad absorption line and place a 3-sigma upper limit on the relative depths of <0.04 for the broad Ca II absorption produced by the SNR. We detect narrow low and high velocity absorption components of Ca II. High velocity |V(LSR)|=100-140 km/s components are attributed to radiative shocks in clouds engulfed by the old Vela SNR. The upper limit to the absorption line strength combined with the width and flux of the 44Ti gamma-ray line 1.16 MeV lead us to conclude that SNR RX J0852.0-4622 was probably produced by an energetic SN Ic explosion.Comment: 9 pages, 8 figures, accepted in A&

Are SMBHs shrouded by "super-Oort" clouds of comets and asteroids?

The last decade has seen a dramatic confirmation that an in situ star formation is possible inside the inner parsec of the Milky Way. Here we suggest that giant planets, solid terrestrial-like planets, comets and asteroids may also form in these environments, and that this may have observational implications for Active Galactic Nuclei (AGN). Like in debris discs around main sequence stars, collisions of large solid objects should initiate strong fragmentation cascades. The smallest particles in such a cascade - the microscopic dust - may provide a significant opacity. We put a number of observational and physical constraints on AGN obscuring torii resulting from such fragmentation cascades. We find that torii fed by fragmenting asteroids disappear at both low and high AGN luminosities. At high luminosities, $L \sim L_{\rm Edd}$, where $L_{\rm Edd}$ is the Eddington limit, the AGN radiation pressure blows out the microscopic dust too rapidly. At low luminosities, on the other hand, the AGN discs may avoid gravitational fragmentation into stars and solids. We also note that these fragmentation cascades may be responsible for astrophysically "large" dust particles of approximately micrometer sizes that were postulated by some authors to explain unusual absorption properties of the AGN torii.Comment: a typo in the title correcte

White matter diffusion estimates in obsessive-compulsive disorder across 1653 individuals: machine learning findings from the ENIGMA OCD Working Group

White matter pathways, typically studied with diffusion tensor imaging (DTI), have been implicated in the neurobiology of obsessive-compulsive disorder (OCD). However, due to limited sample sizes and the predominance of single-site studies, the generalizability of OCD classification based on diffusion white matter estimates remains unclear. Here, we tested classification accuracy using the largest OCD DTI dataset to date, involving 1336 adult participants (690 OCD patients and 646 healthy controls) and 317 pediatric participants (175 OCD patients and 142 healthy controls) from 18 international sites within the ENIGMA OCD Working Group. We used an automatic machine learning pipeline (with feature engineering and selection, and model optimization) and examined the cross-site generalizability of the OCD classification models using leave-one-site-out cross-validation. Our models showed low-to-moderate accuracy in classifying (1) “OCD vs. healthy controls” (Adults, receiver operator characteristic-area under the curve = 57.19 ± 3.47 in the replication set; Children, 59.8 ± 7.39), (2) “unmedicated OCD vs. healthy controls” (Adults, 62.67 ± 3.84; Children, 48.51 ± 10.14), and (3) “medicated OCD vs. unmedicated OCD” (Adults, 76.72 ± 3.97; Children, 72.45 ± 8.87). There was significant site variability in model performance (cross-validated ROC AUC ranges 51.6–79.1 in adults; 35.9–63.2 in children). Machine learning interpretation showed that diffusivity measures of the corpus callosum, internal capsule, and posterior thalamic radiation contributed to the classification of OCD from HC. The classification performance appeared greater than the model trained on grey matter morphometry in the prior ENIGMA OCD study (our study includes subsamples from the morphometry study). Taken together, this study points to the meaningful multivariate patterns of white matter features relevant to the neurobiology of OCD, but with low-to-moderate classification accuracy. The OCD classification performance may be constrained by site variability and medication effects on the white matter integrity, indicating room for improvement for future research.publishedVersio