Zhixiang | Searcharxiv

arXiv2023

Orbital parameters for an ELM white dwarf with a white dwarf companion: LAMOST J033847.06+413424.2

Double white dwarf systems are of great astrophysical importance in the field of gravitational wave and Type Ia supernova. While the binary fraction of CO core white dwarf is about a few percents, the extremely low mass white dwarfs are all thought to be within binary systems. In this work, we report the orbital solution of a double degenerate system: J033847.06+413424.24, an extremely low mass He core white dwarf orbiting a CO core white dwarf. With LAMOST and P200, time domain spectroscopic observations have been made and spectral atmosphere parameters are estimated to be $T_{\rm eff}\sim22500$ K and log $g\sim5.6$ dex. Combining Gaia parallax, 3D extinction, and evolution tracks, we estimate a radius of $\sim0.12$ $R_{\odot}$ and a mass of $\sim0.22$ $M_{\odot}$. With the 37 single exposure spectra, the radial velocities are measured and the orbital parameters are estimated to be $P=0.1253132(1)$ days, $K1=289\pm4$ km/s and $V_{sys}=-41\pm3$ km/s. The radial velocity based system ephemeris is also provided. The light curves from several photometric surveys show no orbital modulation. The orbital solution suggests that the invisible companion has a minimum mass of about 0.60 $M_{\odot}$ and is $\sim0.79$ $M_{\odot}$ for an inclination of $60.0^{\circ}$, indicating most probably a CO core white dwarf. The system is expected to merge in about 1 Gyr. With present period and distance ($\sim596$ pc) it can not irradiate strong enough gravitational wave for LISA. More double degenerate systems are expected to be discovered and parameterized as the LAMOST survey goes on.

astro-ph.SR↗

arXiv2013

An alternative text representation to TF-IDF and Bag-of-Words

In text mining, information retrieval, and machine learning, text documents are commonly represented through variants of sparse Bag of Words (sBoW) vectors (e.g. TF-IDF). Although simple and intuitive, sBoW style representations suffer from their inherent over-sparsity and fail to capture word-level synonymy and polysemy. Especially when labeled data is limited (e.g. in document classification), or the text documents are short (e.g. emails or abstracts), many features are rarely observed within the training corpus. This leads to overfitting and reduced generalization accuracy. In this paper we propose Dense Cohort of Terms (dCoT), an unsupervised algorithm to learn improved sBoW document features. dCoT explicitly models absent words by removing and reconstructing random sub-sets of words in the unlabeled corpus. With this approach, dCoT learns to reconstruct frequent words from co-occurring infrequent words and maps the high dimensional sparse sBoW vectors into a low-dimensional dense representation. We show that the feature removal can be marginalized out and that the reconstruction can be solved for in closed-form. We demonstrate empirically, on several benchmark datasets, that dCoT features significantly improve the classification accuracy across several document classification tasks.

cs.IR↗