IW And-type State in IM Eridani

Taichi Kato,Yasuyuki Wakamatsu，Berto Monard,Shawn Dvorak

Publication of the Astronomical Society of Japan（2019）

Cited 10|Views23

Abstract

IW And stars are a recently recognized group of dwarf novae which are characterized by a repeated sequence of brightening from a standstill-like phase with damping oscillations followed by a deep dip. Kimura et al. (2019, PASJ, submitted) recently proposed a model based on thermal-viscous disk instability in a tilted disk to reproduce the IW And-type characteristics. IM Eri experienced the IW And-type phase in 2018 and we recorded three cycles of the (damping) oscillation phase terminated by brightening. We identified two periods during the IW And-type state: 4-5 d small-amplitude (often damping) oscillations and a 34-43 d long cycle. This behavior is typical for an IW And-type star. The object gradually brightened within the long cycle before the next brightening, which terminated the (damping) oscillation phase. This observation agrees with the increasing disk mass during the long cycle predicted by the Kimura et al. model of thermal-viscous disk instability in a tilted disk. We did not, however, succeed in detecting negative superhumps, which are considered to be the signature of a tilted disk.

Translated text

Key words

accretion, accretion disks,stars: dwarf novae,stars: individual (IM Eridani),stars: novae, cataclysmic variables

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Try using models to generate summary,it takes about 60s

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper