A set of data from 68 cryogenic detectors operated in the CRESST dark matter search experiment between 2013 and 2019 was collected and labeled to train binary classifiers for data cleaning. Here, we describe the data set and how the trained models can be applied to new data. The data and models are available online.
The first JWST observations of SN 1987A provided clear evidence that a compact object is ionizing the innermost ejecta. Here we analyze a second epoch of JWST NIRSpec and MIRI/MRS observations to better characterize the properties of this region, aided by a higher spectral resolving power for the new NIRSpec data. We confirm the presence of the previously identified narrow lines from the central region; [Ar VI] 4.5292 $\mu$m, [Ar II] 6.9853 $\mu$m, [S IV] 10.5105 $\mu$m, and [S III] 18.7130 $\mu$m, and also identify similar components in [Ca V] 4.1585 $\mu$m, [Cl II] 14.3678 $\mu$m, and possibly [Fe II] 1.6440 $\mu$m. These lines are blueshifted by $\sim$ -250 km/s, while the emission region is spatially unresolved and located southeast of the center. The offset and blueshift could imply a kick velocity of $510 \pm 55$ km/s for the neutron star. We also identify [Ca IV] 3.2068 $\mu$m near the center, but it is displaced to the north and has a redshift of $\sim 700$ km/s. We find that scattering by dust in the ejecta with a typical grain size $\sim 0.3\ \mu$m can explain the [Ca IV] properties and the absence of other narrow lines at shorter wavelengths, while dust absorption is important at $\lambda \gtrsim 8\ \mu$m. Photoionization models for a pulsar wind nebula and a cooling neutron star are both compatible with the observations, with the exception of the [Fe II] feature. The two models primarily differ at short wavelengths, where new lines are expected to emerge over time as the optical depth of dust in the expanding ejecta decreases.
Aperture photometry is a fundamental technique widely used to obtain high-precision light curves in optical survey projects like Tianyu. However, its effectiveness is limited in crowded fields, and the choice of aperture size critically impacts photometric precision. To address these challenges, we propose DeepAP, an efficient and accurate two-stage deep learning framework for aperture photometry. Specifically, for a given source, we first train a Vision Transformer (ViT) model to assess its feasibility of aperture photometry. We then train the Residual Neural Network (ResNet) to predict its optimal aperture size. For aperture photometry feasibility assessment, the ViT model yields an ROC AUC value of 0.96, and achieves a precision of 0.974, a recall of 0.930, and an F1 score of 0.952 on the test set. For aperture size prediction, the ResNet model effectively mitigates biases inherent in classical growth curve methods by adaptively selecting apertures appropriate for sources of varying brightness, thereby enhancing the signal-to-noise ratio (SNR) across a wide range of targets. Meanwhile, some samples in the test set have a higher SNR than those obtained by exhaustive aperture size enumeration because of the finer granularity of aperture size estimation. By integrating ResNet with the ViT network, the DeepAP framework achieves a median total processing time of 18 milliseconds for a batch of 10 images, representing a speed-up of approximately 59000 times compared to exhaustive aperture size enumeration. This work paves the way for the automatic application of aperture photometry in future high-precision surveys such as Tianyu and LSST. The source code and model are available at this https URL.
We assemble a homogeneous database of precise and consistent determinations of effective temperature, surface gravity, projected rotational rate, and macro- and micro-turbulent velocities for over 1800 Galactic stars spanning spectral types O to K and luminosity classes I to V. By carefully minimizing biases due to target selection, data quality, and disparate analysis techniques, we carry out statistical tests and comparative analyses to probe potential dependencies between these parameters and micro-turbulence. Our findings indicate that photospheric micro-turbulence is a genuine physical phenomenon rather than a modelling artifact. A direct comparison between observed micro-turbulent velocities and corresponding theoretical predictions for the turbulent pressure fraction strongly suggests that this phenomenon most likely arises from photospheric motions driven by envelope convection zones, with an additional pulsational component likely operating in main-sequence B stars. We show that neglecting micro-turbulent broadening in Fourier transform analyses can partly explain the dearth of slow rotators and the scarcity of stars with extremely low macro-turbulent velocity. We argue that including micro-turbulent pressure in atmospheric modelling can significantly mitigate (even resolve) the mass discrepancy for less massive O stars. Our database offers a valuable resource for testing and refining theoretical scenarios, particularly those addressing puzzling phenomena in hot massive stars.
this https URL . It is also available with its Git history: this https URL (archived in SoftwareHeritage), and in Zenodo: this https URL
this https URL for the published article