Fiducial Stellar Population Sequences for the ugriz System

We describe an extensive observational project that has obtained high-quality and homogeneous photometry for a number of different Galactic star clusters (including M 92, M 13, M 3, M 71, and NGC 6791) spanning a wide range in metallicity (-2.3<[Fe/H]<+0.4), as observed in the ugriz passbands with the MegaCam wide-field imager on the Canada-France-Hawaii Telescope. By employing these purest of stellar populations, fiducial sequences have been defined from color-magnitude diagrams that extend from the tip of the red-giant branch down to approximately 4 magnitudes below the turnoff: these sequences have been accurately calibrated to the standard ugriz system via a set of secondary photometric standards located within these same clusters. Consequently, they can serve as a valuable set of empirical fiducials for the interpretation of stellar populations data in the ugriz system.

Characterizing spectral continuity in SDSS ugriz asteroid photometry

Context. The 4th release of the SDSS Moving Object Catalog (SDSSMOC) is presently the largest photometric dataset of asteroids. Up to this point, the release of large asteroid datasets has always been followed by a redefinition of asteroid taxonomy. In the years that followed the release of the first SDSSMOC, several classification schemes using its data were proposed, all using the taxonomic classes from previous taxonomies. However, no successful attempt has been made to derive a new taxonomic system directly from the SDSS dataset. Aims. The scope of the work is to propose a different interpretation scheme for gauging u0g0r0i0z0 asteroid observations based on the continuity of spectral features. The scheme is integrated into previous taxonomic labeling, but is not dependent on them. Methods. We analyzed the behavior of asteroid sampling through principal components analysis to understand the role of uncertainties in the SDSSMOC. We identified that asteroids in this space follow two separate linear trends using reflectances in the visible, which is characteristic of their spectrophotometric features. Results. Introducing taxonomic classes, we are able to interpret both trends as representative of featured and featureless spectra. The evolution within the trend is connected mainly to the band depth for featured asteroids and to the spectral slope for featureless ones. We defined a different taxonomic system that allowed us to only classify asteroids by two labels. Conclusions. We have classified 69% of all SDSSMOC sample, which is a robustness higher than reached by previous SDSS classifications. Furthermore, as an example, we present the behavior of asteroid (5129) Groom, whose taxonomic labeling changes according to one of the trends owing to phase reddening. Now, such behavior can be characterized by the variation of one single parameter, its position in the trend.

Theoretical isochrones in several photometric systems II. The Sloan Digital Sky Survey ugriz system

Following Paper I, we provide extended tables of bolometric corrections, extinction coefficients, stellar isochrones, and integrated magnitudes and colours of single-burst stellar populations, for the Sloan Digital Sky Survey (SDSS) ugriz photometric system. They are tested on comparisons with DR1 data for a few stellar systems, namely the Palomar 5 and NGC 2419 globular clusters and the Draco dSph galaxy.

Forecast for cosmological parameter estimation with gravitational-wave standard sirens from the LISA-Taiji network

LISA and Taiji are expected to form a space-based gravitational-wave (GW) detection network in the future. In this work, we make a forecast for the cosmological parameter estimation with the standard siren observation from the LISA-Taiji network. We simulate the standard siren data based on a scenario with configuration angle of $40^{circ}$ between LISA and Taiji. Three models for the population of massive black hole binary (MBHB), i.e., pop III, Q3d, and Q3nod, are considered to predict the events of MBHB mergers. We find that, based on the LISA-Taiji network, the number of electromagnetic (EM) counterparts detected is almost doubled compared with the case of single Taiji mission. Therefore, the LISA-Taiji networks standard siren observation could provide much tighter constraints on cosmological parameters. For example, solely using the standard sirens from the LISA-Taiji network, the constraint precision of $H_0$ could reach $1.3%$. Moreover, combined with the CMB data, the GW-EM observation based on the LISA-Taiji network could also tightly constrain the equation of state of dark energy, e.g., the constraint precision of $w$ reaches about $4%$, which is comparable with the result of CMB+BAO+SN. It is concluded that the GW standard sirens from the LISA-Taiji network will become a useful cosmological probe in understanding the nature of dark energy in the future.

Standard Plane Detection in 3D Fetal Ultrasound Using an Iterative Transformation Network

Standard scan plane detection in fetal brain ultrasound (US) forms a crucial step in the assessment of fetal development. In clinical settings, this is done by manually manoeuvring a 2D probe to the desired scan plane. With the advent of 3D US, the entire fetal brain volume containing these standard planes can be easily acquired. However, manual standard plane identification in 3D volume is labour-intensive and requires expert knowledge of fetal anatomy. We propose a new Iterative Transformation Network (ITN) for the automatic detection of standard planes in 3D volumes. ITN uses a convolutional neural network to learn the relationship between a 2D plane image and the transformation parameters required to move that plane towards the location/orientation of the standard plane in the 3D volume. During inference, the current plane image is passed iteratively to the network until it converges to the standard plane location. We explore the effect of using different transformation representations as regression outputs of ITN. Under a multi-task learning framework, we introduce additional classification probability outputs to the network to act as confidence measures for the regressed transformation parameters in order to further improve the localisation accuracy. When evaluated on 72 US volumes of fetal brain, our method achieves an error of 3.83mm/12.7 degrees and 3.80mm/12.6 degrees for the transventricular and transcerebellar planes respectively and takes 0.46s per plane. Source code is publicly available at https://github.com/yuanwei1989/plane-detection.