EncoTESS：基于原始TESS光变曲线的年龄敏感编码

中文摘要 AI 辅助

光谱型从晚型F到M的主序星在测光光变曲线中表现出系统性的变异性，尤其是在年轻阶段。星斑的自转调制表现为准正弦变异性，可用于测量自转周期；变异性也可能是随机的，如恒星耀斑，但由于随机过程的测量依赖于观测时间，通常噪声更大。考虑到不同变异性表现具有独特的观测细微差别，能自然统一这些特性的模型对恒星表征极为有用。为此，我们开发了EncoTESS：一种在TESS 2分钟光变曲线子集上训练的时间序列基础模型（TSFM）。EncoTESS专门设计用于处理TESS数据常见的观测噪声、异方差测量、不规则采样和大数据间隙，其规模约为文献中典型TSFM的1%，可在现代笔记本电脑上轻松运行。EncoTESS将光变曲线编码为固定大小的潜在参数空间，可用于推断恒星物理性质并良好恢复光变曲线的汇总统计量。对于尚未收敛到慢自转序列的恒星，EncoTESS的表现优于自转周期和变异性振幅作为年龄指标；这类恒星大致包括年龄小于约1亿年的K型星和M型星，以及年龄小于10亿年的M型星。本工作中我们将年龄推断作为EncoTESS的应用重点，但也可探索恒星分类等其他下游任务。EncoTESS的架构使其未来可扩展到所有采样频率的TESS光变曲线，以及Kepler等其他巡天和即将到来的PLATO任务。EncoTESS的核心框架及本工作所用恒星生成的编码库可在指定公开链接获取。

英文摘要

Main sequence stars of spectral types late F through M exhibit systematic variability in photometric light curves, particularly when they are young. Rotational modulation of starspots manifests as quasi-sinusoidal variability, which enables the measurement of rotation periods. Variability can also be stochastic, as in stellar flaring. However, since measurements of stochastic processes depend on the time of observation, they are typically noisier. Considering that different manifestations of variability have unique observational nuances, models that naturally unify these are incredibly useful for stellar characterization. Towards this goal, we have developed EncoTESS: a Time Series Foundation Model (TSFM) trained on a subset of TESS 2-min light curves. EncoTESS is specifically designed to handle the observational noise, heteroskedastic measurements, irregular sampling, and large data gaps common to TESS data. It is also ~1% of the size of a typical literature TSFM, so can be run easily on a modern laptop. EncoTESS encodes light curves into a fixed-size latent parameter space, which can be used to infer physical stellar properties and recovers light curve summary statistics well. EncoTESS outperforms rotation period and variability amplitude as age indicators for stars that have not converged onto the slow rotator sequence yet; broadly these include K and M stars less than ~100 Myr, and M stars less than ~1 Gyr. We focus on age inference as an application of EncoTESS in this work, but other downstream tasks such as stellar classification could also be explored. The architecture of EncoTESS enables its future extension to TESS light curves of all cadences, and additional surveys such as Kepler and the upcoming PLATO mission. The core EncoTESS framework and library of encodings produced for the stars used in this work are publicly available at https://github.com/philvanlane/encotess.

发表机构

• University of Toronto（多伦多大学）
• Dunlap Institute for Astronomy & Astrophysics, University of Toronto（多伦多大学邓拉普天文与天体物理研究所）
• University of California San Diego（加利福尼亚大学圣迭戈分校）
• McMaster University（麦克马斯特大学）
• Data Sciences Institute, University of Toronto（多伦多大学数据科学研究所）

机构由 AI 辅助整理，请以论文原文为准。

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36 pages, 23 figures (including appendices)