Search arXivSearch

arXiv · 2206.06693

ET White Paper: To Find the First Earth 2.0

Abstract

We propose to develop a wide-field and ultra-high-precision photometric survey mission, temporarily named "Earth 2.0 (ET)". This mission is designed to measure, for the first time, the occurrence rate and the orbital distributions of Earth-sized planets. ET consists of seven 30cm telescopes, to be launched to the Earth-Sun's L2 point. Six of these are transit telescopes with a field of view of 500 square degrees. Staring in the direction that encompasses the original Kepler field for four continuous years, this monitoring will return tens of thousands of transiting planets, including the elusive Earth twins orbiting solar-type stars. The seventh telescope is a 30cm microlensing telescope that will monitor an area of 4 square degrees toward the galactic bulge. This, combined with simultaneous ground-based KMTNet observations, will measure masses for hundreds of long-period and free-floating planets. Together, the transit and the microlensing telescopes will revolutionize our understandings of terrestrial planets across a large swath of orbital distances and free space. In addition, the survey data will also facilitate studies in the fields of asteroseismology, Galactic archeology, time-domain sciences, and black holes in binaries.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Jian Ge, Hui Zhang, Weicheng Zang, Hongping Deng, Shude Mao, Ji-Wei Xie, Hui-Gen Liu, Ji-Lin Zhou, Kevin Willis, Chelsea Huang, Steve B. Howell, Fabo Feng, Jiapeng Zhu, Xinyu Yao, Beibei Liu, Masataka Aizawa, Wei Zhu, Ya-Ping Li, Bo Ma, Quanzhi Ye, Jie Yu, Maosheng Xiang, Cong Yu, Shangfei Liu, Ming Yang, Mu-Tian Wang, Xian Shi, Tong Fang, Weikai Zong, Jinzhong Liu, Yu Zhang, Liyun Zhang, Kareem El-Badry, Rongfeng Shen, Pak-Hin Thomas Tam, Zhecheng Hu, Yanlv Yang, Yuan-Chuan Zou, Jia-Li Wu, Wei-Hua Lei, Jun-Jie Wei, Xue-Feng Wu, Tian-Rui Sun, Fa-Yin Wang, Bin-Bin Zhang, Dong Xu, Yuan-Pei Yang, Wen-Xiong Li, Dan-Feng Xiang, Xiaofeng Wang, Tinggui Wang, Bing Zhang, Peng Jia, Haibo Yuan, Jinghua Zhang, Sharon Xuesong Wang, Tianjun Gan, Wei Wang, Yinan Zhao, Yujuan Liu, Chuanxin Wei, Yanwu Kang, Baoyu Yang, Chao Qi, Xiaohua Liu, Quan Zhang, Yuji Zhu, Dan Zhou, Congcong Zhang, Yong Yu, Yongshuai Zhang, Yan Li, Zhenghong Tang, Chaoyan Wang, Fengtao Wang, Wei Li, Pengfei Cheng, Chao Shen, Baopeng Li, Yue Pan, Sen Yang, Wei Gao, Zongxi Song, Jian Wang, Hongfei Zhang, Cheng Chen, Hui Wang, Jun Zhang, Zhiyue Wang, Feng Zeng, Zhenhao Zheng, Jie Zhu, Yingfan Guo, Yihao Zhang, Yudong Li, Lin Wen, Jie Feng, Wen Chen, Kun Chen, Xingbo Han. 2022-06-14. ET White Paper: To Find the First Earth 2.0. https://arxiv.org/abs/2206.06693

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Multi-Scale Contrastive Attention for Light-Curve Representation Learning

Current and next-generation time-domain surveys demand automated techniques capable of analyzing millions of light curves, observed in multiple filters, without relying on exhaustive human annotation or scarce spectroscopic follow-up. We present Astra-CLR, an attention-based, self-supervised contrastive learning framework which enables the representation of raw light curves into a highly discriminative latent space. Pre-trained on $\sim$2.1 million unlabeled Zwicky Transient Facility light curves, the framework utilizes partial light curves as input sequences to generate asymmetric, multi-scale temporal views (explicitly contrasting shorter sequences against longer ones) forcing the network to learn a robust "local-to-global" mapping strategy. Furthermore, we introduce a novel multi-view late fusion architecture that extends the model to efficiently handle longer light curves with larger numbers of observations while accommodating the different cadences associated with each filter. The discriminatory power of the resulting representations was evaluated by using them as input to a Multinomial Logistic Regression classifier, trained to identify 12 broad classes of variability. Final accuracy achieved $\sim 0.70$. When applying a label-efficient, partial top-layer fine-tuning strategy, the topological structure of the latent space is significantly refined, boosting results to $\sim$0.77. Astra-CLR is the first publicly available multi-filter time-series Transformer trained exclusively on real ZTF light curves. Results presented here demonstrate that it provides an ideal foundation for the development of end-to-end pipelines, taking into account color evolution and respecting the inhomogeneous nature of astronomical light curve sampling.

astro-ph.IM

Combining astrometry with pulsar timing: the first joint analysis of very low frequency gravitational waves

The pHz to sub-nHz GW regime remains largely unexplored but is crucial for mapping the early inspiral stage of SMBHBs and probing early-Universe physics. Astrometry and pulsar timing offer orthogonal and deeply complementary secular observables to investigate this frequency band. We aim to present the first joint data analysis combining real astrometric proper motions with binary pulsar timing, to search for and constrain continuous gravitational waves (CWs) sourced by SMBHBs in the ultra-low-frequency regime ($10^{-12} \le f_{GW} \le10^{-9} Hz$). We employ a Bayesian model selection and upper limit estimation framework to combine apparent proper motion displacements of $\sim 1.5 \times 10^6$ quasars from the Gaia CRF3 catalog with the line-of-sight orbital period derivatives ($\dot{P}_b$) of 11 high-precision binary pulsars. To prevent spurious detections, we heavily model instrumental and astrophysical systematics: we propagate the Galactic potential uncertainty for pulsars via Monte Carlo simulations and perform a Vector Spherical Harmonics (VSH) decomposition up to the octupole order (l=3) for quasars. We find no statistically significant evidence for a CWs signal in the joint analysis ($\ln B_{joint} = -0.42 \pm 0.03$). In the absence of detection, we set the tightest constraints to date on CW strain in the pHz band, yielding a 95% upper limit of $h_0\le6.4x10^{-11}$ at a reference frequency of $f_{ref} = 4 \times 10^{-10}$ Hz. The combined dataset achieves full sky coverage and improves single-dataset upper limits by 20%-30%. Combining orthogonal observables successfully breaks spatial degeneracies intrinsic to isolated searches. Furthermore, forecasts from inj.-rec. indicate that with the extended temporal baseline and reduced uncertainties of the upcoming Gaia DR4, this joint framework is poised to break the $h_0 < 10^{-11}$ upper limit barrier for sub-nHz CWs.

astro-ph.IM

Bayesian classification of astronomical spectra with class uncertainties

Context: We developed a probabilistic machine learning method with the aim of performing the O(10)-way classification of low- and high-resolution spectra of stellar and extragalactic targets for the upcoming 4MOST survey. In fulfilment of the survey requirements, this method should be able to express uncertainty in the input data as well as uncertainty introduced in its prediction. Aims: Four different methods are explored: (1) convolutional neural networks (CNNs), (2) the Dirichlet distribution, (3) Monte Carlo dropout (MCD), (4) Bayesian neural Networks (BNNs) + variational inference (VI). Training and validation was performed using labelled spectra from the SDSS database and a custom 4MOST mock dataset. All the methods were compared in terms of the same metrics: accuracy, area under the curve (AUC), expected calibration error (ECE), Shannon entropy, negative log-likelihood (NLL), Brier score, training time, and inference time. Methods: A CNN with simple architecture and about 20,000 parameters was trained to achieve classification accuracies of 91.5% on SDSS data and 92.8% on 4MOST mock data. The direct Dirichlet prediction and VI models tested provide uncertainties on class membership probabilities, but they confuse classes more often. The MCD on a CNN is found to be the most suitable; it boosts the point-estimate accuracies to 92.6% and 93.9%, while still providing fast training and sufficiently fast inference. Compared to a standard CNN, the method additionally provides well-calibrated uncertainties at marginal extra cost.

astro-ph.IM