We present an algorithm for inferring the dynamical mass of galaxy clusters directly from their respective phase-space distributions, i.e. the observed line-of-sight velocities and projected distances of galaxies from the cluster centre. Our method employs normalizing flows, a deep neural network capable of learning arbitrary high-dimensional probability distributions, and inherently accounts, to an adequate extent, for the presence of interloper galaxies which are not bounded to a given cluster, the primary contaminant of dynamical mass measurements. We validate and showcase the performance of our neural flow approach to robustly infer the dynamical mass of clusters from a realistic mock cluster catalogue. A key aspect of our novel algorithm is that it yields the probability density function of the mass of a particular cluster, thereby providing a principled way of quantifying uncertainties, in contrast to conventional machine learning approaches. The neural network mass predictions, when applied to a contaminated catalogue with interlopers, have a mean overall logarithmic residual scatter of 0.028 dex, with a log-normal scatter of 0.126 dex, which goes down to 0.089 dex for clusters in the intermediate to high mass range. This is an improvement by nearly a factor of four relative to the classical cluster mass scaling relation with the velocity dispersion, and outperforms recently proposed machine learning approaches. We also apply our neural flow mass estimator to a compilation of galaxy observations of some well-studied clusters with robust dynamical mass estimates, further substantiating the efficacy of our algorithm.
We present a simulation-based inference framework using a convolutional neural network to infer dynamical masses of galaxy clusters from their observed 3D projected phase-space distribution, which consists of the projected galaxy positions in the sky and their line-of-sight velocities. By formulating the mass estimation problem within this simulation-based inference framework, we are able to quantify the uncertainties on the inferred masses in a straightforward and robust way. We generate a realistic mock catalogue emulating the Sloan Digital Sky Survey (SDSS) Legacy spectroscopic observations…
Inferring the values and uncertainties of cosmological parameters in a cosmology model is of paramount importance for modern cosmic observations. In this paper, we use the simulation-based inference (SBI) approach to estimate cosmological constraints from a simplified galaxy cluster observation analysis. Using data generated from the Quijote simulation suite and analytical models, we train a machine learning algorithm to learn the probability function between cosmological parameters and the possible galaxy cluster observables. The posterior distribution of the cosmological parameters at a give…
We present a forward modeling framework for estimating galaxy redshift distributions from photometric surveys. Our forward model is composed of: a detailed population model describing the intrinsic distribution of physical characteristics of galaxies, encoding galaxy evolution physics; a stellar population synthesis model connecting the physical properties of galaxies to their photometry; a data-model characterizing the observation and calibration processes for a given survey; and, explicit treatment of selection cuts, both into the main analysis sample and subsequent sorting into tomographic …
Flexible Simulation Based Inference for Galaxy Photometric Fitting with Synthesizer
University of Manchester（曼彻斯特大学）；Kavli Institute for Cosmology（卡弗里宇宙学研究所）；Institute of Astronomy（天文学研究所）；University of Portsmouth（朴茨茅斯大学）；Institute of Cosmology and Gravitation（宇宙学与引力研究所）；University of Sussex（萨塞克斯大学）；Instituto de Astrofísica de Canarias（加那利天体物理研究所）；Universidad de La Laguna（拉各鲁纳大学）
HaloFlow II: Robust Galaxy Halo Mass Inference with Domain Adaptation
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；International Centre for Radio Astronomy Research, University of Western Australia（西澳大利亚大学国际射电天文研究中心）；Kavli IPMU (WPI), UTIAS, The University of Tokyo（东京大学 Kavli 宇宙物理学与数学研究所（WPI）、UTIAS）
NGC1052-DF2 was recently discovered as the dark-matter deficient galaxy claimed by van Dokkum et al. (2018a, vD18). However, large uncertainties on its dynamical mass estimate have been pointed out, concerning the paucity of sample, statistical methods and distance measurements. In this work, we discuss the effects of the difference in modeling of the tracer profile of this galaxy on the dynamical mass estimate. To do this, we assume that the tracer densities are modeled with power-law and Sérsic profiles, and then we solve the spherical Jeans equation to estimate the dynamical mass. Applying …
Analyzes of next-generation galaxy data require accurate treatment of systematic effects such as the bias between observed galaxies and the underlying matter density field. However, proposed models of the phenomenon are either numerically expensive or too inaccurate to achieve unbiased inferences of cosmological parameters even at mildly-nonlinear scales of the data. As an alternative to constructing accurate galaxy bias models, requiring understanding galaxy formation, we propose to construct likelihood distributions for Bayesian forward modeling approaches that are insensitive to linear, sca…
We present a large-scale Bayesian inference framework to constrain cosmological parameters using galaxy redshift surveys, via an application of the Alcock-Paczyński (AP) test. Our physical model of the non-linearly evolved density field, as probed by galaxy surveys, employs Lagrangian perturbation theory (LPT) to connect Gaussian initial conditions to the final density field, followed by a coordinate transformation to obtain the redshift space representation for comparison with data. We generate realizations of primordial and present-day matter fluctuations given a set of observations. This hi…
Powerful current and future cosmological constraints using high precision measurements of the large-scale structure of galaxies and its weak gravitational lensing effects rely on accurate characterization of the redshift distributions of the galaxy samples using only broadband imaging. We present a framework for constraining both the redshift probability distributions of galaxy populations and the redshifts of their individual members. We use a hierarchical Bayesian model (HBM) which provides full posterior distributions on those redshift probability distributions, and, for the first time, we …
As a large-scale overdensity collapses, it affects the orientation and shape of galaxies that form, by exerting tidal shear along their axes. Therefore, the shapes of elliptical galaxies align with the tidal field of cosmic structures. This intrinsic alignment provides insights into galaxy formation and the primordial universe, complements late-time cosmological probes and constitutes a significant systematic effect for weak gravitational lensing observations. In the present study, we provide constraints on the linear alignment model using a fully Bayesian field-level approach, using galaxy sh…
We present a Bayesian hierarchical inference formalism (Basilisk) to constrain the galaxy-halo connection using satellite kinematics. Unlike traditional methods, Basilisk does not resort to stacking the kinematics of satellite galaxies in bins of central luminosity, and does not make use of summary statistics, such as satellite velocity dispersion. Rather, Basilisk leaves the data in its raw form and computes the corresponding likelihood. In addition, Basilisk can be applied to flux-limited, rather than volume-limited samples, greatly enhancing the quantity and dynamic range of the data. And f…
We show how the angular clustering between gravitational-wave standard sirens and galaxies with known redshifts allows an inference of the Hubble constant, regardless of whether the host galaxies of any of these sirens are present in the galaxy catalog. We demonstrate this for the first time with realistic simulations of gravitational-wave signals from binary black holes in a three-detector network with Advanced LIGO and Advanced Virgo sensitivities. We show that with such a network, the cross-correlation technique can be used to infer the Hubble parameter with a precision of less than 10% (2%…
Stellar shells observed in many giant elliptical and lenticular as well as a few spiral and dwarf galaxies presumably result from radial minor mergers of galaxies. We show that the line-of-sight velocity distribution of the shells has a quadruple-peaked shape. We found simple analytical expressions that connect the positions of the four peaks of the line profile with the mass distribution of the galaxy, namely, the circular velocity at the given shell radius and the propagation velocity of the shell. The analytical expressions were applied to a test-particle simulation of a radial minor merger…
The ultra-diffuse galaxy in the NGC 5846 group (NGC 5846_UDG1) was shown to have a large number of globular cluster (GC) candidates from deep imaging as part of the VEGAS survey. Recently, Muller et al. published a velocity dispersion, based on a dozen of its GCs. Within their quoted uncertainties, the resulting dynamical mass allowed for either a dark matter free or a dark matter dominated galaxy. Here we present spectra from KCWI which reconfirms membership of the NGC 5846 group and reveals a stellar velocity dispersion for UDG1 of $σ_{GC}$ = 17 $\pm$ 2 km/s. Our dynamical mass, with a reduc…
We propose a new, likelihood-free approach to inferring the primordial matter power spectrum and cosmological parameters from arbitrarily complex forward models of galaxy surveys where all relevant statistics can be determined from numerical simulations, i.e. black-boxes. Our approach, which we call simulator expansion for likelihood-free inference (SELFI), builds upon approximate Bayesian computation using a novel effective likelihood, and upon the linearisation of black-box models around an expansion point. Consequently, we obtain simple "filter equations" for an effective posterior of the p…
The galaxy bispectrum is affected on equality scales and above by relativistic observational effects, at linear and nonlinear order. These lightcone effects include local contributions from Doppler and gravitational potential terms, as well as integrated contributions like lensing, together with all the couplings at nonlinear order. We recently presented the correction to the galaxy bispectrum from all local lightcone effects up to second order in perturbations, using a plane-parallel approximation. Here we update our previous result by including the effects from relativistic nonlinear dynamic…
Detecting and measuring a non-Gaussian signature of primordial origin in the density field is a major science goal of next-generation galaxy surveys. The signal will permit us to determine primordial physics processes and constrain models of cosmic inflation. While traditional approaches utilise a limited set of statistical summaries of the galaxy distribution to constrain primordial non-Gaussianity, we present a field-level approach by Bayesian forward-modelling the entire three-dimensional galaxy survey. Our method naturally and fully self-consistently exploits the entirety of the large-scal…
The combination of galaxy-galaxy lensing (GGL) and galaxy clustering is a promising route to measuring the amplitude of matter clustering and testing modified gravity theories of cosmic acceleration. Halo occupation distribution (HOD) modeling can extend the approach down to nonlinear scales, but galaxy assembly bias could introduce systematic errors by causing the HOD to vary with large scale environment at fixed halo mass. We investigate this problem using the mock galaxy catalogs created by Hearin & Watson (2013, HW13), which exhibit significant assembly bias because galaxy luminosity is ti…
The large-scale redshift-space distortion (RSD) in galaxy clustering can probe $fσ_8$, a combination of the cosmic structure growth rate and the matter fluctuation amplitude, which can constrain dark energy models and test theories of gravity. While the RSD on small scales (e.g. a few to tens of $h^{-1}{\rm Mpc}$) can further tighten the $fσ_8$ constraints, galaxy assembly bias, if not correctly modelled, may introduce systematic uncertainties. Using a mock galaxy catalogue with built-in assembly bias, we perform a preliminary study on how assembly bias may affect the $fσ_8$ inference. We find…
Joint Geometric and Dynamical Constraints on Cosmology from Anisotropies in Galaxy Intrinsic-Alignment Correlations
Academia Sinica Institute of Astronomy and Astrophysics (ASIAA)（中央研究院天文及天文物理研究所）；Kavli IPMU (WPI), UTIAS, The University of Tokyo（卡弗里宇宙物理学起源研究所（世界顶级学术研究中心）、东京大学宇宙射线研究所）
In a novel approach employing implicit likelihood inference (ILI), also known as likelihood-free inference, we calibrate the parameters of cosmological hydrodynamic simulations against observations, which has previously been unfeasible due to the high computational cost of these simulations. For computational efficiency, we train neural networks as emulators on ~1000 cosmological simulations from the CAMELS project to estimate simulated observables, taking as input the cosmological and astrophysical parameters, and use these emulators as surrogates to the cosmological simulations. Using the co…
Hierarchical inference of the relationship between Concentration and Mass in Galaxy Groups and Clusters
质量是星系群和星系团的基本属性。在理论上，弱引力透镜将使一个近似无偏的质量测量，但参数方法提取集群质量的数据需要额外的知识的浓度。质量和浓度的测量都受到两个参数之间简并度的限制，特别是在低质量、高红移的系统中，信噪比很低。在本文中，我们开发了一个层次模型的质量和浓度的质量推断，我们测试我们的方法对玩具数据，然后将其应用到一个样本的星系群和贫穷的集群下降到质量的$\sim$ 1e13 M $_\odot$。我们的拟合和模型给出了质量，浓度和红移之间的关系，允许从不完整和嘈杂的未来测量预测这些参数。此外，潜在的人口可以用来推断一个基于观测的浓度质量关系。我们的方法等价于一个准叠加方法，叠加度由数据设定。我们还表明，来自纯堆叠的质量和浓度可以从人口平均值与不同的值取决于堆叠的方法抵消。
Set-based Implicit Likelihood Inference of Galaxy Cluster Mass
McWilliams Center for Cosmology and Astrophysics, Department of Physics, Carnegie Mellon University（麦卡威廉宇宙学与天体物理学中心，物理系，卡内基梅隆大学）；Entertainment Technology Center, Carnegie Mellon University（娱乐技术中心，卡内基梅隆大学）；Kavli IPMU (WPI), UTIAS, The University of Tokyo（Kavli IPMU（WPI），UTIAS，东京大学）；Center for Data-Driven Discovery, Kavli IPMU (WPI), Japan（数据驱动发现中心，Kavli IPMU（WPI），日本）
提出结合 Deep Sets 与条件归一化流的集合式框架，利用投影星系动力学推断星系团质量后验，降低离散度并给出可靠不确定性。
Strong Gravitational Lensing Posterior Sampling in Pixel-Space Using Diffusion Models and Recurrent Inference Machines
Department of Physics, Universit\'e de Montr\'eal, Montreal QC, Canada；Mila - Quebec AI Institute, Montreal QC, Canada；Ciela Institute, Institute for Astrophysics；Trottier Space Institute, McGill University, Montreal QC, Canada；Center for Computational Astrophysics, Flatiron Institute, New York, USA；Perimeter Institute for Theoretical Physics, Waterloo ON, Canada
Galaxy clusters are a promising probe of late-time structure growth, but constraints on cosmology from cluster abundances are currently limited by systematics in their inferred masses. One unmitigated systematic effect in weak-lensing mass inference is ignoring the presence of baryons and treating the entire cluster as a dark matter halo. In this work we present a new flexible model for cluster densities that captures both the baryonic and dark matter profiles, a new general technique for calculating the lensing signal of an arbitrary density profile, and a methodology for stacking those lensi…
We study the connection between morphology and dynamical state of the simulated galaxy clusters in $z\in[0,1.031]$ from THE THREE HUNDRED Project. We quantify cluster dynamical state using a combination of dynamical indicators from theoretical measures and compare this combined parameter, $χ$, with the results from morphological classifications. The dynamical state of the cluster sample shows a continuous distribution from dynamically relaxed, more abundant at lower redshift, to hybrid and disturbed. The dynamical state presents a clear dependence on the radius, with internal regions more rela…
We present high-fidelity cosmology results from a blinded joint analysis of galaxy-galaxy weak lensing ($Δ\!Σ$) and projected galaxy clustering ($w_{\rm p}$) measured from the Hyper Suprime-Cam Year-1 (HSC-Y1) data and spectroscopic Sloan Digital Sky Survey (SDSS) galaxy catalogs in the redshift range $0.15<z<0.7$. We define luminosity-limited samples of SDSS galaxies to serve as the tracers of $w_{\rm p}$ in three spectroscopic redshift bins, and as the lens samples for $Δ\!Σ$. For the $Δ\!Σ$ measurements, we select a single sample of 4 million source galaxies over 140 deg$^2$ from HSC-Y1 wit…
The transmission of Lyman-α (Lyα) in the spectra of distant quasars depends on the density, temperature, and ionization state of the intergalactic medium (IGM). Therefore, high-redshift (z > 5) Lyα forests could be invaluable in studying the late stages of the epoch of reionization (EoR), as well as properties of the sources that drive it. Indeed, high-quality quasar spectra have now firmly established the existence of large-scale opacity fluctuations at z > 5, whose physical origins are still debated. Here we introduce a Bayesian framework capable of constraining the EoR and galaxy properties…
