arXiv subjects

Chieh-An Lin

Publications and source records attributed to Chieh-An Lin.

16 recordsLinked to original sources

arXiv2022

SDSS-IV MaNGA: Unveiling Galaxy Interaction by Merger Stages with Machine Learning

We use machine learning techniques to classify galaxy merger stages, which can unveil physical processes that drive the star formation and active galactic nucleus (AGN) activities during galaxy interaction. The sample contains 4,690 galaxies from the integral field spectroscopy survey SDSS-IV MaNGA, and can be separated to 1,060 merging galaxies and 3630 non-merging or unclassified galaxies. For the merger sample, there are 468, 125, 293, and 174 galaxies in (1) incoming pair phase, (2) first pericentric passage phase, (3) aproaching or just passing the apocenter, and (4) final coalescence phase or post-mergers. With the information of projected separation, line-of-sight velocity difference, SDSS gri images, and MaNGA Ha velocity map, we are able to classify the mergers and their stages with good precision, which is the most important score to identify interacting galaxies. For the 2-phase classification (binary; non-merger and merger), the performance can be high (precision>0.90) with LGBMClassifier. We find that sample size can be increased by rotation, so the 5-phase classification (non-merger, 1, 2, 3, and 4 merger stages) can be also good (precision>0.85). The most important features come from SDSS gri images. The contribution from MaNGA Ha velocity map, projected separation, and line-of-sight velocity difference can further improve the performance by 0-20%. In other words, the image and the velocity information are sufficient to capture important features of galaxy interactions, and our results can apply to the entire MaNGA data as well as future all-sky surveys.

astro-ph.GA↗

arXiv2021

KiDS-1000 Cosmology: constraints beyond flat $Λ$CDM

We present constraints on extensions to the flat $Λ$CDM cosmological model by varying the spatial curvature $Ω_K$, the sum of the neutrino masses $\sum m_ν$, the dark energy equation of state parameter $w$, and the Hu-Sawicki $f(R)$ gravity $f_{R0}$ parameter. With the combined $3\times2$pt measurements of cosmic shear from the Kilo-Degree Survey (KiDS-1000), galaxy clustering from the Baryon Oscillation Spectroscopic Survey (BOSS), and galaxy-galaxy lensing from the overlap between KiDS-1000, BOSS, and the spectroscopic 2-degree Field Lensing Survey (2dFLenS), we find results that are fully consistent with a flat $Λ$CDM model with $Ω_K=0.011^{+0.054}_{-0.057}$, $\sum m_ν<1.76$ eV (95% CL), and $w=-0.99^{+0.11}_{-0.13}$. The $f_{R0}$ parameter is unconstrained in our fully non-linear $f(R)$ cosmic shear analysis. Considering three different model selection criteria, we find no clear preference for either the fiducial flat $Λ$CDM model or any of the considered extensions. Besides extensions to the flat $Λ$CDM parameter space, we also explore restrictions to common subsets of the flat $Λ$CDM parameter space by fixing the amplitude of the primordial power spectrum to the Planck best-fit value, as well as adding external data from supernovae and lensing of the CMB. Neither the beyond-$Λ$CDM models nor the imposed restrictions explored in this analysis are able to resolve the $\sim 3σ$ tension in $S_8$ between the $3\times2$pt constraints and Planck, with the exception of $w$CDM, where the $S_8$ tension is resolved. The tension in the $w$CDM case persists, however, when considering the joint $S_8$-$w$ parameter space. The joint flat $Λ$CDM CMB lensing and $3\times2$pt analysis is found to yield tight constraints on $Ω_{\rm m}=0.307^{+0.008}_{-0.013}$, $σ_8=0.769^{+0.022}_{-0.010}$, and $S_8=0.779^{+0.013}_{-0.013}$.

astro-ph.CO↗

arXiv2020

KiDS-1000 catalogue: Weak gravitational lensing shear measurements

We present weak lensing shear catalogues from the fourth data release of the Kilo-Degree Survey, KiDS-1000, spanning 1006 square degrees of deep and high-resolution imaging. Our `gold-sample' of galaxies, with well-calibrated photometric redshift distributions, consists of 21 million galaxies with an effective number density of $6.17$ galaxies per square arcminute. We quantify the accuracy of the spatial, temporal, and flux-dependent point-spread function (PSF) model, verifying that the model meets our requirements to induce less than a $0.1σ$ change in the inferred cosmic shear constraints on the clustering cosmological parameter $S_8 = σ_8\sqrt{Ω_{\rm m}/0.3}$. Through a series of two-point null-tests, we validate the shear estimates, finding no evidence for significant non-lensing B-mode distortions in the data. The PSF residuals are detected in the highest-redshift bins, originating from object selection and/or weight bias. The amplitude is, however, shown to be sufficiently low and within our stringent requirements. With a shear-ratio null-test, we verify the expected redshift scaling of the galaxy-galaxy lensing signal around luminous red galaxies. We conclude that the joint KiDS-1000 shear and photometric redshift calibration is sufficiently robust for combined-probe gravitational lensing and spectroscopic clustering analyses.

astro-ph.CO↗

arXiv2020

KiDS-1000 Cosmology: Multi-probe weak gravitational lensing and spectroscopic galaxy clustering constraints

We present a joint cosmological analysis of weak gravitational lensing observations from the Kilo-Degree Survey (KiDS-1000), with redshift-space galaxy clustering observations from the Baryon Oscillation Spectroscopic Survey (BOSS), and galaxy-galaxy lensing observations from the overlap between KiDS-1000, BOSS and the spectroscopic 2-degree Field Lensing Survey (2dFLenS). This combination of large-scale structure probes breaks the degeneracies between cosmological parameters for individual observables, resulting in a constraint on the structure growth parameter $S_8=σ_8 \sqrt{Ω_{\rm m}/0.3} = 0.766^{+0.020}_{-0.014}$, that has the same overall precision as that reported by the full-sky cosmic microwave background observations from Planck. The recovered $S_8$ amplitude is low, however, by $8.3 \pm 2.6$ % relative to Planck. This result builds from a series of KiDS-1000 analyses where we validate our methodology with variable depth mock galaxy surveys, our lensing calibration with image simulations and null-tests, and our optical-to-near-infrared redshift calibration with multi-band mock catalogues and a spectroscopic-photometric clustering analysis. The systematic uncertainties identified by these analyses are folded through as nuisance parameters in our cosmological analysis. Inspecting the offset between the marginalised posterior distributions, we find that the $S_8$-difference with Planck is driven by a tension in the matter fluctuation amplitude parameter, $σ_8$. We quantify the level of agreement between the CMB and our large-scale structure constraints using a series of different metrics, finding differences with a significance ranging between $\sim\! 3\,σ$, when considering the offset in $S_{8}$, and $\sim\! 2\,σ$, when considering the full multi-dimensional parameter space.

astro-ph.CO↗

arXiv2020

KiDS-1000 Cosmology: Cosmic shear constraints and comparison between two point statistics

We present cosmological constraints from a cosmic shear analysis of the fourth data release of the Kilo-Degree Survey (KiDS-1000), doubling the survey area with nine-band optical and near-infrared photometry with respect to previous KiDS analyses. Adopting a spatially flat $Λ$CDM model, we find $S_8 = σ_8 (Ω_{\rm m}/0.3)^{0.5} = 0.759^{+0.024}_{-0.021}$ for our fiducial analysis, which is in $3σ$ tension with the prediction of the Planck Legacy analysis of the cosmic microwave background. We compare our fiducial COSEBIs (Complete Orthogonal Sets of E/B-Integrals) analysis with complementary analyses of the two-point shear correlation function and band power spectra, finding results to be in excellent agreement. We investigate the sensitivity of all three statistics to a number of measurement, astrophysical, and modelling systematics, finding our $S_8$ constraints to be robust and dominated by statistical errors. Our cosmological analysis of different divisions of the data pass the Bayesian internal consistency tests, with the exception of the second tomographic bin. As this bin encompasses low redshift galaxies, carrying insignificant levels of cosmological information, we find that our results are unchanged by the inclusion or exclusion of this sample.

astro-ph.CO↗

arXiv2020

KiDS+GAMA: The weak lensing calibrated stellar-to-halo mass relation of central and satellite galaxies

We simultaneously present constraints on the stellar-to-halo mass relation for central and satellite galaxies through a weak lensing analysis of spectroscopically classified galaxies. Using overlapping data from the fourth data release of the Kilo-Degree Survey (KiDS), and the Galaxy And Mass Assembly survey (GAMA), we find that satellite galaxies are hosted by halo masses that are $0.53 \pm 0.39$ dex (68\% confidence, $3σ$ detection) smaller than those of central galaxies of the same stellar mass (for a stellar mass of $\log(M_{\star}/M_{\odot}) = 10.6$). This is consistent with galaxy formation models, whereby infalling satellite galaxies are preferentially stripped of their dark matter. We find consistent results with similar uncertainties when comparing constraints from a standard azimuthally averaged galaxy-galaxy lensing analysis and a two-dimensional likelihood analysis of the full shear field. As the latter approach is somewhat biased due to the lens incompleteness and as it does not provide any improvement to the precision when applied to actual data, we conclude that stacked tangential shear measurements are best-suited for studies of the galaxy-halo connection.

astro-ph.CO↗

arXiv2020

KiDS+VIKING-450 and DES-Y1 combined: Mitigating baryon feedback uncertainty with COSEBIs

We present cosmological constraints from a joint cosmic shear analysis of the Kilo-Degree Survey (KV450) and the Dark Energy Survey (DES-Y1), conducted using Complete Orthogonal Sets of E/B-Integrals (COSEBIs). With COSEBIs we isolate any B-modes which have a non-cosmic shear origin and demonstrate the robustness of our cosmological E-mode analysis as no significant B-modes are detected. We highlight how COSEBIs are fairly insensitive to the amplitude of the non-linear matter power spectrum at high $k$-scales, mitigating the uncertain impact of baryon feedback in our analysis. COSEBIs, therefore, allow us to utilise additional small-scale information, improving the DES-Y1 joint constraints on $S_8=σ_8(Ω_{\rm m}/0.3)^{0.5}$ and $Ω_{\rm m}$ by $20\%$. Adopting a flat $Λ$CDM model we find $S_8=0.755^{+0.019}_{-0.021}$, which is in $3.2σ$ tension with the Planck Legacy analysis of the cosmic microwave background.

astro-ph.CO↗

arXiv2019

Cosmology from large-scale structure: Constraining $Λ$CDM with BOSS

We reanalyse the anisotropic galaxy clustering measurement from the Baryon Oscillation Spectroscopic Survey (BOSS), demonstrating that using the full shape information provides cosmological constraints that are comparable to other low-redshift probes. We find $Ω_\mathrm{m} = 0.317^{+0.015}_{-0.019}$, $σ_8 = 0.710\pm 0.049$, and $h = 0.704\pm 0.024$ for flat $Λ$CDM cosmologies using uninformative priors on $Ω_\mathrm{c}h^2$, $100θ_\mathrm{MC}$, $\ln 10^{10} A_{s}$, and $n_{s}$, and a prior on $Ω_\mathrm{b}h^2$ that is much wider than current constraints. We quantify the agreement between the Planck 2018 constraints from the cosmic microwave background and BOSS, finding the two data sets to be consistent within a flat $Λ$CDM cosmology using the Bayes factor as well as the prior-insensitive suspiciousness statistic. Combining two low-redshift probes, we jointly analyse the clustering of BOSS galaxies with weak lensing measurements from the Kilo-Degree Survey (KV450). The combination of BOSS and KV450 improves the measurement by up to 45%, constraining $σ_8 = 0.702\pm 0.029$ and $S_8 = σ_8\sqrt{Ω_\mathrm{m}/0.3} = 0.728\pm 0.026$. Over the full 5D parameter space, the odds in favour of single cosmology describing galaxy clustering, lensing, and the cosmic microwave background are $7\pm2$. The suspiciousness statistic signals a $2.1\pm0.3σ$ tension between the combined low-redshift probes and measurements from the cosmic microwave background.

astro-ph.CO↗

arXiv2019

The effects of varying depth in cosmic shear surveys

We present a semi-analytic model for the shear two-point correlation function of a cosmic shear survey with non-uniform depth. Ground-based surveys are subject to depth variations that primarily arise through varying atmospheric conditions. For a survey like the Kilo-Degree Survey (KiDS), we find that the measured depth variation increases the amplitude of the observed shear correlation function at the level of a few percent out to degree-scales, relative to the assumed uniform-depth case. The impact on the inferred cosmological parameters is shown to be insignificant for a KiDS-like survey. For next-generation cosmic shear experiments, however, we conclude that variable depth should be accounted for.

astro-ph.CO↗

arXiv2019

Validation of Approximate Likelihood and Emulator Models for Computationally Intensive Simulations

Complex phenomena in engineering and the sciences are often modeled with computationally intensive feed-forward simulations for which a tractable analytic likelihood does not exist. In these cases, it is sometimes necessary to estimate an approximate likelihood or fit a fast emulator model for efficient statistical inference; such surrogate models include Gaussian synthetic likelihoods and more recently neural density estimators such as autoregressive models and normalizing flows. To date, however, there is no consistent way of quantifying the quality of such a fit. Here we propose a statistical framework that can distinguish any arbitrary misspecified model from the target likelihood, and that in addition can identify with statistical confidence the regions of parameter as well as feature space where the fit is inadequate. Our validation method applies to settings where simulations are extremely costly and generated in batches or "ensembles" at fixed locations in parameter space. At the heart of our approach is a two-sample test that quantifies the quality of the fit at fixed parameter values, and a global test that assesses goodness-of-fit across simulation parameters. While our general framework can incorporate any test statistic or distance metric, we specifically argue for a new two-sample test that can leverage any regression method to attain high power and provide diagnostics in complex data settings.

stat.ME↗

arXiv2018

Quantifying systematics from the shear inversion on weak-lensing peak counts

Weak-lensing peak counts provide a straightforward way to constrain cosmology by linking local maxima of the lensing signal to the mass function. Recent applications to data have already been numerous and fruitful. However, the importance of understanding and dealing with systematics increases as data quality reaches an unprecedented level. One of the sources of systematics is the convergence-shear inversion. This effect, inevitable when carrying out a convergence field from observations, is usually neglected by theoretical peak models. Thus, it could have an impact on cosmological results. In this paper, we study the bias from neglecting (mis-modeling) the inversion. Our tests show a small but non-negligible bias. The cosmological dependence of this bias seems to be related to the parameter Sigma_8. When this bias propagates to the parameter estimation, we discovered that constraint contours involving the dark energy equation of state can differ by 2-sigma. Such an effect can be even larger for future high-precision surveys and we argue that the inversion should be properly modeled for theoretical peak models.

astro-ph.CO↗

arXiv2016

Cosmology with weak-lensing peak counts

Weak gravitational lensing (WL) causes distortions of galaxy images and probes massive structures on large scales, allowing us to understand the late-time evolution of the Universe. One way to extract the cosmological information from WL is to use peak statistics. Peaks are tracers of massive halos and therefore probe the mass function. They retain non-Gaussian information and have already been shown as a promising tool to constrain cosmology. In this work, we develop a new model to predict WL peak counts. The model generates fast simulations based on halo sampling and selects peaks from the derived lensing maps. This approach has three main advantages. First, the model is very fast: only several seconds are required to perform a realization. Second, including realistic conditions is straightforward. Third, the model provides the full distribution information because of its stochasticity. We show that our model agrees well with N-body simulations. Then, we study the impacts of the cosmology-dependent covariance on constraints and explore different parameter inference methods. A special focus is put on approximate Bayesian computation (ABC), an accept-reject sampler without the need to estimate the likelihood. We show that ABC is able to yield robust constraints with much reduced time costs. Several filtering techniques are studied to improve the extraction of multiscale information. Finally, the new model is applied to the CFHTLenS, KiDS DR1/2, and DES SV data sets. Our preliminary results agree with the Planck constraints assuming the Lambda-CDM model. Overall, this thesis forges an innovative tool for future WL surveys. The manuscript provides a brief review on WL peak counts.

astro-ph.CO↗

arXiv2016

Cosmological constraints with weak lensing peak counts and second-order statistics in a large-field survey

Peak statistics in weak lensing maps access the non-Gaussian information contained in the large-scale distribution of matter in the Universe. They are therefore a promising complement to two-point and higher-order statistics to constrain our cosmological models. To prepare for the high-precision data of next-generation surveys, we assess the constraining power of peak counts in a simulated Euclid-like survey on the cosmological parameters $Ω_\mathrm{m}$, $σ_8$, and $w_0^\mathrm{de}$. In particular, we study how the Camelus model--a fast stochastic algorithm for predicting peaks--can be applied to such large surveys. We measure the peak count abundance in a mock shear catalogue of ~5,000 sq. deg. using a multiscale mass map filtering technique. We then constrain the parameters of the mock survey using Camelus combined with approximate Bayesian computation (ABC). We find that peak statistics yield a tight but significantly biased constraint in the $σ_8$-$Ω_\mathrm{m}$ plane, indicating the need to better understand and control the model's systematics. We calibrate the model to remove the bias and compare results to those from the two-point correlation functions (2PCF) measured on the same field. In this case, we find the derived parameter $Σ_8=σ_8(Ω_\mathrm{m}/0.27)^α=0.76_{-0.03}^{+0.02}$ with $α=0.65$ for peaks, while for 2PCF the value is $Σ_8=0.76_{-0.01}^{+0.02}$ with $α=0.70$. We therefore see comparable constraining power between the two probes, and the offset of their $σ_8$-$Ω_\mathrm{m}$ degeneracy directions suggests that a combined analysis would yield tighter constraints than either measure alone. As expected, $w_0^\mathrm{de}$ cannot be well constrained without a tomographic analysis, but its degeneracy directions with the other two varied parameters are still clear for both peaks and 2PCF. (abridged)

astro-ph.CO↗

arXiv2016

A new model to predict weak-lensing peak counts III. Filtering technique comparisons

This is the third in a series of papers that develop a new and flexible model to predict weak-lensing (WL) peak counts, which have been shown to be a very valuable non-Gaussian probe of cosmology. In this paper, we compare the cosmological information extracted from WL peak counts using different filtering techniques of the galaxy shear data, including linear filtering with a Gaussian and two compensated filters (the starlet wavelet and the aperture mass), and the nonlinear filtering method MRLens. We present improvements to our model that account for realistic survey conditions, which are masks, shear-to-convergence transformations, and non-constant noise. We create simulated peak counts from our stochastic model, from which we obtain constraints on the matter density $Ω_\mathrm{m}$, the power spectrum normalisation $σ_8$, and the dark-energy parameter $w_0$. We use two methods for parameter inference, a copula likelihood, and approximate Bayesian computation (ABC). We measure the contour width in the $Ω_\mathrm{m}$-$σ_8$ degeneracy direction and the figure of merit to compare parameter constraints from different filtering techniques. We find that starlet filtering outperforms the Gaussian kernel, and that including peak counts from different smoothing scales helps to lift parameter degeneracies. Peak counts from different smoothing scales with a compensated filter show very little cross-correlation, and adding information from different scales can therefore strongly enhance the available information. Measuring peak counts separately from different scales yields tighter constraints than using a combined peak histogram from a single map that includes multiscale information. Our results suggest that a compensated filter function with counts included separately from different smoothing scales yields the tightest constraints on cosmological parameters from WL peaks.

astro-ph.CO↗

arXiv2015

A new model to predict weak-lensing peak counts II. Parameter constraint strategies

Peak counts have been shown to be an excellent tool to extract the non-Gaussian part of the weak lensing signal. Recently, we developped a fast stochastic forward model to predict weak-lensing peak counts. Our model is able to reconstruct the underlying distribution of observables for analyses. In this work, we explore and compare various strategies for constraining parameter using our model, focusing on the matter density $Ω_\mathrm{m}$ and the density fluctuation amplitude $σ_8$. First, we examine the impact from the cosmological dependency of covariances (CDC). Second, we perform the analysis with the copula likelihood, a technique which makes a weaker assumption compared to the Gaussian likelihood. Third, direct, non-analytic parameter estimations are applied using the full information of the distribution. Fourth, we obtain constraints with approximate Bayesian computation (ABC), an efficient, robust, and likelihood-free algorithm based on accept-reject sampling. We find that neglecting the CDC effect enlarges parameter contours by 22%, and that the covariance-varying copula likelihood is a very good approximation to the true likelihood. The direct techniques work well in spite of noisier contours. Concerning ABC, the iterative process converges quickly to a posterior distribution that is in an excellent agreement with results from our other analyses. The time cost for ABC is reduced by two orders of magnitude. The stochastic nature of our weak-lensing peak count model allows us to use various techniques that approach the true underlying probability distribution of observables, without making simplifying assumptions. Our work can be generalized to other observables where forward simulations provide samples of the underlying distribution.

astro-ph.CO↗

arXiv2015

A new model to predict weak-lensing peak counts I. Comparison with $N$-body Simulations

Weak-lensing peak counts has been shown to be a powerful tool for cosmology. It provides non-Gaussian information of large scale structures, complementary to second order statistics. We propose a new flexible method to predict weak lensing peak counts, which can be adapted to realistic scenarios, such as a real source distribution, intrinsic galaxy alignment, mask effects, photo-$z$ errors from surveys, etc. The new model is also suitable for applying the tomography technique and non-linear filters. A probabilistic approach to model peak counts is presented. First, we sample halos from a mass function. Second, we assign them NFW profiles. Third, we place those halos randomly on the field of view. The creation of these "fast simulations" requires much less computing time compared to $N$-body runs. Then, we perform ray-tracing through these fast simulation boxes and select peaks from weak-lensing maps to predict peak number counts. The computation is achieved by our \textsc{Camelus} algorithm, which we make available at http://www.cosmostat.org/software/camelus/ . We compare our results to $N$-body simulations to validate our model. We find that our approach is in good agreement with full $N$-body runs. We show that the lensing signal dominates shape noise and Poisson noise for peaks with SNR between 4 and 6. Also, counts from the same SNR range are sensitive to $Ω_\mathrm{m}$ and $σ_8$. We show how our model can discriminate between various combinations of those two parameters. In summary, we offer a powerful tool to study weak lensing peaks. The potential of our forward model is its high flexibility, making the use of peak counts under realistic survey conditions feasible.

astro-ph.CO↗