Works

Published Works

Dark Matter
Halo
Galaxy
Neutral
Hydrogen

Finding proto-clusters to trace galaxy evolution: I. The finder and its performance

MNRAS, 2021, stab1608
Kai Wang
Tsinghua University (Beijing, China)
Houjun Mo
UMass Amherst (Mass, USA)
Cheng Li
Tsinghua University (Beijing, China)
Yangyao Chen
Tsinghua University (Beijing, China)
We develop a method to identify proto-clusters based on dark matter halos at high redshift. Our main findings include:
  • The test with N-body simulations shows that our finder has completeness $\sim 85\%$, purity $\geq 90\%$, mass estimates uncertainty $ \leq 0.25 {\rm dex}$.
  • Our method can recover progenitor stellar mass distribution, providing an avenue to link high-z and low-z galaxies in clusters.
Figure: Conditional halo mass function at $z=1$ for protoclusters with $z=0$ cluster mass $[10^{14.2}, 10^{14.5}] h^{-1}M_\odot$. Black solid line is the true from test simulation, with dashed line indicating its most massive member. Other markers are results from our finder in different test cases.

An Extended Halo-based Group/Cluster Finder: Application to the DESI Legacy Imaging Surveys DR8

ApJ, 2021, 909, 143
Xiaohu Yang
SJTU (Shanghai, China)
Haojie Xu
SJTU (Shanghai, China)
Min He
SJTU (Shanghai, China)
Yizhou Gu
SJTU (Shanghai, China)
Antonios Katsianis
SJTU (Shanghai, China)
Jiacheng Meng
Tsinghua University (Beijing, China)
Feng Shi
Korea Astronomy and Space Science Institute (Daejeon, Korea)
Hu Zou
NAOC (Beijing, China)
Youcai Zhang
SHAO (Shanghai, China)
Chengze Liu
SJTU (Shanghai, China)
Zhaoyu Wang
SJTU (Shanghai, China)
Fuyu Dong
Korea Institute for Advanced Study (Seoul, Korea)
Yi Lu
SHAO (Shanghai, China)
Qingyang Li
SJTU (Shanghai, China)
Yangyao Chen
Tsinghua University (Beijing, China)
Huiyuan Wang
USTC (Anhui, China)
Houjun Mo
UMass Amherst (Mass, USA)
Jian Fu
SHAO (Shanghai, China)
Hong Guo
SHAO (Shanghai, China)
Alexie Leauthaud
UCSC (CA, USA)
Yu Luo
PMO (Nanjing, China)
Jun Zhang
SJTU (Shanghai, China)
Ying Zu
SJTU (Shanghai, China)
We extend the halo-based group finder to use data simultaneously with either photometric or spectroscopic redshifts. The performance is evaluated with a mock from N-body simulation. Our main results include:
  • For magnitude $z \leq 21 $ galaxies in DESI, $\geq 60\%$ members in $\sim 90\%$ halos with $M_{\rm h} \geq 10^{12.5} h^{-1}M_\odot$ can be identified. Detected groups with $M_{\rm h} \geq 10^{12} h^{-1}M_\odot$ has purity $\geq 90\%$.
  • Group mass assignment has uncertainty from 0.2 dex (high mass end) to 0.45 dex (low mass end).
  • Group with 10 members has redshift accuracy $\sim 0.08$.
  • A group catalog is provided for DR8.
Figure: The accumulative halo mass functions obtained from Legacy Surveys DR8 in different redshift bins. Markers are from the group finder. Solid lines are from SMT 2001 analytical model.

Relating the Structure of Dark Matter Halos to Their Assembly and Environment

Yangyao Chen
Tsinghua University (Beijing, China)
Houjun Mo
UMass Amherst (Mass, USA)
Cheng Li
Tsinghua University (Beijing, China)
Huiyuan Wang
USTC (Anhui, China)
Xiaohu Yang
SJTU (Shanghai, China)
Youcai Zhang
SHAO (Shanghai, China)
Kai Wang
Tsinghua University (Beijing, China)
We use a large N-body simulation to study the relation of structural properties of dark matter halos to their assembly history and environment. Our main conclusions are:
  • The complexity of individual halo assembly histories can be well described by a small number of principal components, which are preferred over formation times for several reasons.
  • 60%, 10%, 20% of the variances in halo concentration, axis ratio and spin, respectively, can be explained by combining four dominating predictors $\rm PC_{MAH,1}$, $M_{\rm halo}$, $\alpha_\mathcal{T}$, $b$. Degeneracies between predictors are found and analyzed, and are still hold for mass-binned samples.
  • Tidal field provides important environmental information, with $\alpha_\mathcal{T}$ shows strongest assembly bias signal.
Interactive figure: Importances $\mathcal{I}(X)$ from various predictors $X$ to halo structural quantities $Y$: concentration $c$ (red), shape parameter $q_{\rm axis}$ (blue) or the spin parameter $\log\ \lambda_{\rm s}$ (purple). Solid lines connecting circles show the results when using the first MAH PC $\rm PC_{MAH,1}$, halo mass $M_{\rm halo}$, tidal anisotropy $\alpha_\mathcal{T}$ and bias factor $b$ as predictors. Dashed lines connecting triangles show the results when using only $\rm PC_{MAH,1}$ and $M_{\rm halo}$. Their overall performances are also indicated in the panel.

Identifying galaxy groups at high redshift from incomplete spectroscopic data - I. The group finder and application to zCOSMOS

MNRAS, 2020, 499, 1
Kai Wang
Tsinghua University (Beijing, China)
Houjun Mo
UMass Amherst (Mass, USA)
Cheng Li
Tsinghua University (Beijing, China)
Jiacheng Meng
Tsinghua University (Beijing, China)
Yangyao Chen
Tsinghua University (Beijing, China)
High-z spectroscopic surveys, usually incomplete in redshift sampling, present both opportunities and challenges to identifying groups in the high-z Universe. We develop a group finder that is based on incomplete redshift samples combined with photometric data. Our main findings are:
  • Mock test shows that $\geq 90\%$ of groups with $M_{\rm h}\geq 10^{12} h^{-1}{\rm M}_\odot$ are successfully identified.
  • The standard deviation in the halo mass estimation is smaller than 0.25 dex at all masses.
  • We apply our group finder to zCOSMOS-bright and describe basic properties of the group catalog obtained.
Interactive figure: Group finder performance (completeness) tested with our PFS mock galaxy catalog. Dashed lines are obtained from spectroscopic sample, while solid lines are improved by adding photometric sample. In each case, five sampling-rate schemes are presented: ETS (developed to mimic the real target selection in PFS), and Rand-P (uniform random sampling with sampling rate P). Units: halo mass, $h^{-1}{\rm M}_\odot$.

MAHGIC: A Model Adapter for the Halo-Galaxy Inter-Connection

MNRAS, 2021, stab2377
Yangyao Chen
Tsinghua University (Beijing, China)
Houjun Mo
UMass Amherst (Mass, USA)
Cheng Li
Tsinghua University (Beijing, China)
Kai Wang
Tsinghua University (Beijing, China)
Huiyuan Wang
USTC (Anhui, China)
Xiaohu Yang
SJTU (Shanghai, China)
Youcai Zhang
SHAO (Shanghai, China)
Neal Katz
UMass Amherst (Mass, USA)
We develop an empirical model pipeline, MAHGIC, to populate dark matter halos with galaxies. The main features of the model and our main results include:
  • PCA and GBDT learners are used to transform halo properties to galaxy properties.
  • Two sets of hydrodynamic simulations, TNG and EAGLE, are used to train the model, which is then applied to other DMO simulations.
  • The model can reproduce a variety of statistical properties of galaxies. It is verified reliable, flexible and accurate.
Figure: the outline MAHGIC. Halo properties and assembly history $({\bf \rm x}_h,\ {\bf \rm h}_h)$ are transformed into galaxy properties and star formation history $({\bf \rm x}_*,\ {\bf \rm h}_*)$ with a multi-stage, multi-piece pipeline.

How to empirically model star formation in dark matter halos: I. Inferences about central galaxies from numerical simulations

MNRAS, 2021, stab695
Yangyao Chen
Tsinghua University (Beijing, China)
Houjun Mo
UMass Amherst (Mass, USA)
Cheng Li
Tsinghua University (Beijing, China)
Kai Wang
Tsinghua University (Beijing, China)
Our study provides a framework of using hydrodynamic simulations to discover, and to motivate the use of, key ingredients to model galaxy formation using halo properties. Our findings include:
  • The SFH of central galaxies are tightly related to halo MAH.
  • The classification of SF and quenched populations has significant contamination.
  • We propose a multi-stage halo-based empirical model for the star formation in central galaxies, which reproduces many galaxy statistics and galaxy-halo relations including assembly bias.
Figure: the outline of the empirical model for the star formation of central galaxies in dark matter halos. The MAH ($\rm {\bf h}_{halo}$) and other halo properties ($\rm {\bf x}_{halo}$) are transformed to the star formation histories ($\rm {\bf h}_{*}$) through three procedures.

Measuring galaxy abundance and clustering at high redshift from incomplete spectroscopic data: Tests on mock catalogs and application to zCOSMOS

Jiacheng Meng
Tsinghua University (Beijing, China)
Cheng Li
Tsinghua University (Beijing, China)
Houjun Mo
UMass Amherst (Mass, USA)
Yangyao Chen
Tsinghua University (Beijing, China)
Kai Wang
Tsinghua University (Beijing, China)
We build mock galaxy catalogs for high-z galaxy surveys, and we propose methods to measure GLFs, GSMFs and 2PCFs at high-z Universe. Our findings include:
  • Our methods of estimating GLFs, GSMFs and 2PCFs reliably cancel the bias from target selection and sample imcompleteness.
  • Mock catalogs are constructed for zCOSMOS-bright sample and PFS galaxy evolution survey.
  • We quantify the cosmic variance using the mocks, and find the cosmic variance is reduced by a factor of 3-4 in PFS compared with zCOSMOS.
Interactive figure: B-band luminosity functions estimated from real zCOSMOS-bright sample at different redshifts using our method. Units: luminosity function, $h^3 {\rm Mpc}^{-3} {\rm mag}^{-1}$.

ELUCID. VI. Cosmic Variance of the Galaxy Distribution in the Local Universe

ApJ, 2019, 872, 180
Yangyao Chen
Tsinghua University (Beijing, China)
Houjun Mo
UMass Amherst (Mass, USA)
Cheng Li
Tsinghua University (Beijing, China)
Huiyuan Wang
USTC (Anhui, China)
Xiaohu Yang
SJTU (Shanghai, China)
Shuang Zhou
Tsinghua University (Beijing, China)
Youcai Zhang
SHAO (Shanghai, China)
We propose a method based on conditional stellar mass functions to estimate global GSMF. Our findings include:
  • We extend the halo merger trees from N-body simulation to a higher resolution.
  • We use constrained N-body simuation and empirical approach to construct a 'real' mock catalog, which recovers the galaxy distribution in the local Universe (SDSS volume).
  • The low-mass end GSMF estimated from SDSS sample can be significantly affected by the Cosmic Variance (CV).
  • We propose a new method based on CGSMF, provide unbiased estimate of GSMF which show significant upture below $M_* \leq 10^{9.5} h^{-1}{\rm M}_\odot$ and is missed in many earlier works.
Interactive figure: The galaxy stellar mass function (GSMF) obtained from the SDSS sample in this paper after correction of cosmic variance(red), in comparison with the results published earlier: Li C. & White S. 2009 (blue), He Y.-Q. 2013 (purple). Significant upturn at the low-stellar-mass end can be seen. Units: stellar mass, $h^{-1}{\rm M}_\odot$, GSMF, $h^3 {\rm Mpc}^{-3} {\rm dex}^{-1}$.

The Breakdown Scale of HI Bias Linearity

ApJ, 2021, 907, 4
Zhenyuan Wang
Tsinghua & PSU (Penn, USA)
Yangyao Chen
Tsinghua University (Beijing, China)
Yi Mao
Tsinghua University (Beijing, China)
Houjun Mo
UMass Amherst (Mass, USA)
Huiyuan Wang
USTC (Anhui, China)
Hong Guo
SHAO (Shanghai, China)
Cheng Li
Tsinghua University (Beijing, China)
Jian Fu
SHAO (Shanghai, China)
Yipeng Jing
SJTU (Shanghai, China)
Jing Wang
PKU (Beijing, China)
Xiaohu Yang
SJTU (Shanghai, China)
Zheng Zheng
Utah (Utah, USA)
By employing three approaches to generate the mock HI density from an N-body simulation at low z, we check the assumption that HI gas traces the matter density distribution linearly on large scales. Our main findings are:
  • the assumption of HI linearity is valid at the scale corresponding to the first BAO peak, but breaks down at $k \geq 0.1 h {\rm Mpc}^{−1}$.
  • The nonlinear effects of halo clustering and HI content modulation counteract each other at small scales, and their competition results in a model-dependent “sweet-spot” redshift near z=1 where the HI bias is scale-independent down to small scales.
  • The linear HI bias scales approximately linearly with redshift for z ≤ 3.
Interactive figure: The bias of halo mass density fluctuations (red) and HI mass density fluctuations from three models (blue, purple and green) at $z = 0$, w.r.t. matter density fluctuations. Models are star formation models (L for an empirical model, T for TNG, H for HOD) + HI models (K for Krumholz, A for ALFALFA). Dashed is for linear bias, while thin and thick solids are for shot-noise corrected and uncorrected bias. Vertical lines indicate the scales of 1st and 2nd BAO peaks. Units: k, $h {\rm Mpc}^{-1}$.

Academic Activity

Academic Meetings

Confenrences
Workshops
Informal
Talks
中国天文学会2016年年会
Astronomy
Nov. 1-4, 2016洪山宾馆,武汉,湖北
SUGAR-RUSH 2018
LSSGalaxy Formation
June 11-15, 2018SJTU, Shanghai, China
第20届郭守敬学术研讨会
Galaxy FormationLSS
July 2, 2018卢氏县, 三门峡, 河南
The 2nd East Asian Workshop on Astrostatistics
StatisticsR lang
July 9, 2018PMO, Nanjing, China
EGG Workshop
GasGalaxy Formation
July 17, 2018Tsinghua DOA
THU-Phys 2018年博士生论坛
PhD科研成果分享
Sept. 5, 2018稻香湖景酒店, 北京
Report: Cosmic Variance
星系结构、形成和演化项目启动会议
Galaxy Formation & Evolution
Sept. 10, 2018NAOC
Report: Cosmic Variance
HUBS 2018 Workshop
X-rayWarm-hot IGM
Oct. 15, 2018Chongming Island, Shanghai, China
PFS 2018 Collab. Meeting
Galaxy Survey
Dec. 10, 2018SJTU, Shanghai, China
Report 1: Cosmic Variance
Report 2: Empirical Model
PFUNT-2018五校联盟论坛
PhD科研成果分享
Dec. 13-15, 2018FJU, Shanghai, China
Report: Cosmic Variance
第21届郭守敬学术研讨会
Galaxy FormationLSS
May 10-13, 2019五缘水乡酒店, 厦门, China
Report: Cosmic Variance
星系结构、形成和演化项目2019会议
Galaxy Formation & Evolution
Aug. 26-27, 2019NAOC
Report: Cosmic Variance
THU-Phys 2019年博士生论坛
PhD科研成果分享
Aug. 30-31, 2019新华联丽景酒店,北京顺义
PFS 2019 Collab. Meeting
Galaxy Survey
Dec. 9-14, 2019Caltech, Pasadena, USA
Report: Protocluster identification
Statistical Learning in a Nutshell
ML Models & pipelines
May 16, 2019Tsinghua DOA
Talk: Statistical Learning in a Nutshell
Abstract: In previous group meetings, many examples of statistical learning algorithm, e.g., SVMs, CNNs, ensemble methods based on random forest and K-Means, etc., are presented in details. Although there are almost countless algorithms, the hard core of statistical learning is simple. In this talk, I will give an overall framework of statistical learning, list the general procedure of implementing a statistical learning model, and build connections between different models, with emphasis on the MOST important parts that we should always concern about to avoid pitfalls.
An Introduction to the ELUCID Project
Density fieldReconstruction
May 30, 2019Tsinghua DOA
Talk: An Introduction to the ELUCID Project
Abstract: ELUCID prject is a series of works carried out by Wang H. et al. It provides a framework to reconstruct the underlying initial density field from the galaxy surveys. In this talk I will introduce the idea, the algorithms, the main components, and the pipeline behind the reconstruction, including the galaxy group finder, the halo domain method, the HMCMC sampling, and the high-resolution N-body forwarding. I hope this talk can help you understand how the ELUCID pipeline works and eventually you use this database to do more science, e.g., the environmental effect on galaxy and gas, the cosmic variance on the galaxy statistics.
THCA Student Seminar: Dark Energy Model
Dark Energy Theory
Dec. 15, 2017THCA
Talk: DE Model
2017 Personal Summary
Personal Summary
Jan. 18, 2018THCA
Talk: 2017 Personal Summary
THCA Student Seminar: CALET
CALET DetectorHigh Energy
March 30, 2018THCA
Talk: CALET
2018 THCA AMD Scholarship Defense
Personal Summary
Oct. 25, 2018THCA
Talk: 2018 Personal Summary
THCA Student Seminar: Herschel
Herschel Space Telescope
Dec. 11, 2018THCA
Talk: The Herschel Space Telescope
Paper Sharing: K-Means Clustering
ClusteringML
Jan. 1, 2019THCA
Talk: Introduce the paper "Reproducible k-means clustering in galaxy feature data from the GAMA survey"
DOA Student Seminar: Magnetic Reconnection Experiment
MR experiments
May 10, 2019Tsinghua DOA
Talk: MR Experiments
Paper Sharing: Hierarchical Bayesian
BayesianSatellite Kinematics
Sept. 20, 2019Tsinghua DOA
Talk: Introduce the paper "BASILISK: Bayesian Hierarchical Inference of the Galaxy-halo Connection using Satellite Kinematics - I. Method and Validation"
Researches on Star Formation History
SFHGalaxy
July 24, 2020Zoom
Talk: Researches on SFH
2020 Science Jamboree
Aug. 25, 2020UMass, USA
Talk: Introduction to my research interests
2020国奖答辩
Oct. 16, 2020Tsinghua DOA
Talk Slides
The MAHGIC Model
July 7, 2021IPMU, Japan
Talk Slides
2021 Science Jamboree
Sept. 9, 2021UMass, USA
Talk: Introduction to my research interests
文明上网,理性发言
Talk in a scientific way keep lawyers away