Works
Published Works
Dark Matter
Halo
Halo
Galaxy
Neutral
Hydrogen
Hydrogen
Finding proto-clusters to trace galaxy evolution: I. The finder and its performance
MNRAS,
2021,
stab1608
Kai Wang
Houjun Mo
Cheng Li
Yangyao Chen
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
Haojie Xu
Min He
Yizhou Gu
Antonios Katsianis
Jiacheng Meng
Feng Shi
Hu Zou
Youcai Zhang
Chengze Liu
Zhaoyu Wang
Fuyu Dong
Yi Lu
Qingyang Li
Yangyao Chen
Huiyuan Wang
Houjun Mo
Jian Fu
Hong Guo
Alexie Leauthaud
Yu Luo
Jun Zhang
Ying Zu
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
ApJ,
2020,
899, 81
Tool: Halo Structure Calculator
Yangyao Chen
Houjun Mo
Cheng Li
Huiyuan Wang
Xiaohu Yang
Youcai Zhang
Kai Wang
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
Houjun Mo
Cheng Li
Jiacheng Meng
Yangyao Chen
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
Houjun Mo
Cheng Li
Kai Wang
Huiyuan Wang
Xiaohu Yang
Youcai Zhang
Neal Katz
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
Houjun Mo
Cheng Li
Kai Wang
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
arXiv,
2020,
2008.13733
Catalog: Mocks
Measurement: GLFs & GSMFs & 2PCFs
Jiacheng Meng
Cheng Li
Houjun Mo
Yangyao Chen
Kai Wang
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
Houjun Mo
Cheng Li
Huiyuan Wang
Xiaohu Yang
Shuang Zhou
Youcai Zhang
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
Yangyao Chen
Yi Mao
Houjun Mo
Huiyuan Wang
Hong Guo
Cheng Li
Jian Fu
Yipeng Jing
Jing Wang
Xiaohu Yang
Zheng Zheng
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
Talks
中国天文学会2016年年会
Astronomy
Nov. 1-4, 2016洪山宾馆，武汉，湖北
EGG Workshop
GasGalaxy Formation
July 17, 2018Tsinghua DOA
星系结构、形成和演化项目启动会议
Galaxy Formation & Evolution
Sept. 10, 2018NAOC
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
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
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
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.
文明上网，理性发言
Talk in a scientific way keep lawyers away