Identifying RR Lyrae in the ZTF DR3 dataset

We present a RR Lyrae (RRL) catalogue based on the combination of the third data release of the Zwicky Transient Facility (ZTF DR3) and \textit{Gaia} EDR3. We use a multi-step classification pipeline relying on the Fourier decomposition fitting to the multi-band ZTF light curves and random forest classification. The resulting catalogue contains 71,755 RRLs with period and light curve parameter measurements and has completeness of 0.92 and purity of 0.92 with respect to the SOS \textit{Gaia} DR2 RRLs. The catalogue covers the Northern sky with declination $\geq -28^\circ$, its completeness is $\gtrsim 0.8$ for heliocentric distance $\leq 80$~kpc, and the most distant RRL at 132~kpc. Compared with several other RRL catalogues covering the Northern sky, our catalogue has more RRLs around the Galactic halo and is more complete at low Galactic latitude areas. Analysing the spatial distribution of RRL in the catalogue reveals the previously known major over-densities of the Galactic halo, such as the Virgo over-density and the Hercules-Aquila Cloud, with some evidence of an association between the two. We also analyse the Oosterhoff fraction differences throughout the halo, comparing it with the density distribution, finding increasing Oosterhoff I fraction at the elliptical radii between 16 and 32 kpc and some evidence of different Oosterhoff fractions across various halo substructures

Search for globular clusters associated with the Milky Way dwarf galaxies using Gaia DR2

We report the result of searching for globular clusters (GCs) around 55 Milky Way satellite dwarf galaxies within the distance of 450 kpc from the Galactic Center except for the Large and Small Magellanic Clouds and the Sagittarius dwarf. For each dwarf, we analyze the stellar distribution of sources in Gaia DR2, selected by magnitude, proper motion, and source morphology. Using the kernel density estimation of stellar number counts, we identify eleven possible GC candidates. Crossed-matched with existing imaging data, all eleven objects are known either GCs or galaxies and only Fornax GC 1-6 among them are associated with the targeted dwarf galaxy. Using simulated GCs, we calculate the GC detection limit $M_{\rm V}^{\rm lim}$ that spans the range from $M_{\rm V}^{\rm lim} \sim -7$ for distant dwarfs to $M_{\rm V}^{\rm lim} \sim 0$ for nearby systems. Assuming a Gaussian GC luminosity function, we compute that the completeness of the GC search is above 90 percent for most dwarf galaxies. We construct the 90 percent credible intervals/upper limits on the GC specific frequency $S_{\rm N}$ of the MW dwarf galaxies: $12 < S_{\rm N} < 47$ for Fornax, $S_{\rm N} < 20$ for the dwarfs with $-12 < M_{\rm V} < -10$, $S_{\rm N} < 30$ for the dwarfs with $-10 < M_{\rm V} < -7$, and $S_{\rm N} < 90$ for the dwarfs with $M_{\rm V} > -7$. Based on $S_{\rm N}$, we derive the probability of galaxies hosting GCs given their luminosity, finding that the probability of galaxies fainter than $M_{\rm V} = -9$ to host GCs is lower than 0.1

Self-Augmentation Improves Zero-Shot Cross-Lingual Transfer

Zero-shot cross-lingual transfer is a central task in multilingual NLP, allowing models trained in languages with more sufficient training resources to generalize to other low-resource languages. Earlier efforts on this task use parallel corpora, bilingual dictionaries, or other annotated alignment data to improve cross-lingual transferability, which are typically expensive to obtain. In this paper, we propose a simple yet effective method, SALT, to improve the zero-shot cross-lingual transfer of the multilingual pretrained language models without the help of such external data. By incorporating code-switching and embedding mixup with self-augmentation, SALT effectively distills cross-lingual knowledge from the multilingual PLM and enhances its transferability on downstream tasks. Experimental results on XNLI and PAWS-X show that our method is able to improve zero-shot cross-lingual transferability without external data. Our code is available at https://github.com/luka-group/SALT.Comment: AACL 202