Mike S Wang

University of Portsmouth, Institute of Cosmology and Gravitation, Graduate Student

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PhD researcher in Cosmology
Supervisors: Robert Crittenden, Will Percival, Davide Bianchi, and Santiago Avila
Phone: +44 23 9284 5151
Address: Institute of Cosmology and Gravitation
University of Portsmouth
Dennis Sciama Building
Burnaby Road
Portsmouth, PO1 3FX
United Kingdom

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Samantha Youles

Vladimir Avila-Reese

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Mariana Vargas Magaña

Universidad Nacional Autónoma de México

ChangHoon Hahn

Lawrence Berkeley National Laboratory

Juan Jose Downes

Universidad de la República (Uruguay)

Martín López-Corredoira

Instituto de Astrofisica de Canarias

Britt Lundgren

Patrick Petitjean

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Papers by Mike S Wang

Impact of relativistic effects on the primordial non-Gaussianity signature in the large-scale clustering of quasars

Monthly Notices of the Royal Astronomical Society, 2020

Relativistic effects in clustering observations have been shown to introduce scale-dependent corr... more Relativistic effects in clustering observations have been shown to introduce scale-dependent corrections to the galaxy overdensity field on large scales, which may hamper the detection of primordial non-Gaussianity fNL through the scale-dependent halo bias. The amplitude of relativistic corrections depends not only on the cosmological background expansion, but also on the redshift evolution and sensitivity to the luminosity threshold of the tracer population being examined, as parametrized by the evolution bias be and magnification bias s. In this work, we propagate luminosity function measurements from the extended Baryon Oscillation Spectroscopic Survey (eBOSS) to be and s for the quasar (QSO) sample, and thereby derive constraints on relativistic corrections to its power spectrum multipoles. Although one could mitigate the impact on the fNL signature by adjusting the redshift range or the luminosity threshold of the tracer sample being considered, we suggest that, for future surv...

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Cosmological Inference from Galaxy-Clustering Power Spectrum: Gaussianization and Covariance Decomposition

Likelihood fitting to two-point clustering statistics made from galaxy surveys usually assumes a ... more Likelihood fitting to two-point clustering statistics made from galaxy surveys usually assumes a multivariate normal distribution for the measurements, with justification based on the central limit theorem given the large number of overdensity modes. However, this assumption cannot hold on the largest scales where the number of modes is low. Whilst more accurate distributions have previously been developed in idealized cases, we derive a procedure suitable for analysing measured monopole power spectra with window effects, stochastic shot noise, and the dependence of the covariance matrix on the model being fitted all taken into account. A data transformation is proposed to give an approximately Gaussian likelihood, with a variance--correlation decomposition of the covariance matrix to account for its cosmological dependence. By comparing with the modified-t likelihood derived under the usual normality assumption, we find in numerical tests that our new procedure gives more accurate ...

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Primordial Gravitational Waves from Cosmic Inflation

Cosmic inflation generates primordial gravitational waves (PGWs) through the same physical proces... more Cosmic inflation generates primordial gravitational waves (PGWs) through the same physical process that seeds all structure formation in the observable universe. We will demonstrate this mechanism in detail and relate it to the distinctive signatures PGWs could leave in the observable temperature anisotropies and polarisation of the cosmic microwave background (CMB). The detection of primordial gravitational waves is of great significance to validating and understanding inflationary physics, and we shall see why CMB polarisation o ers a promising path. In the end, we will remark on the unique observational challenges and prospects of probing primordial gravitational waves in future experiments.

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Cosmological inference from galaxy-clustering power spectrum: Gaussianization and covariance decomposition

Monthly Notices of the Royal Astronomical Society, 2019

Likelihood fitting to two-point clustering statistics made from galaxy surveys usually assumes a ... more Likelihood fitting to two-point clustering statistics made from galaxy surveys usually assumes a multivariate normal distribution for the measurements, with justification based on the central limit theorem given the large number of overdensity modes. However, this assumption cannot hold on the largest scales where the number of modes is low. Whilst more accurate distributions have previously been developed in idealized cases, we derive a procedure suitable for analysing measured monopole power spectra with window effects, stochastic shot noise and the dependence of the covariance matrix on the model being fitted all taken into account. A data transformation is proposed to give an approximately Gaussian likelihood, with a variance-correlation decomposition of the covariance matrix to account for its cosmological dependence. By comparing with the modified-t likelihood derived under the usual normality assumption, we find in numerical tests that our new procedure gives more accurate constraints on the local non-Gaussianity parameter f NL , which is sensitive to the large-scale power. A simple data analysis pipeline is provided for straightforward application of this new approach in preparation for forthcoming large galaxy surveys such as DESI and Euclid.

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Hybrid-basis inference for large-scale galaxy clustering: combining spherical and Cartesian Fourier analyses

Journal of Cosmology and Astroparticle Physics, 2020

Future precision cosmology from large-scale structure experiments including the Dark Energy Spect... more Future precision cosmology from large-scale structure experiments including the Dark Energy Spectroscopic Instrument (DESI) and Euclid will probe wider and deeper cosmic volumes than those covered by previous surveys. The Cartesian power spectrum analysis of anisotropic galaxy clustering based on the Fourier plane wave basis makes a number of assumptions, including the local plane-parallel approximation, that will no longer be valid on very large scales and may degrade cosmological constraints. We propose an approach that utilises a hybrid basis: on the largest scales, clustering statistics are decomposed into spherical Fourier modes which respect the natural geometry of both survey observations and physical effects along the line of sight, such as redshift-space distortions, the Alcock-Paczyńsky and light-cone effects; on smaller scales with far more clustering modes, we retain the computational benefit of the power spectrum analysis aided by fast Fourier transforms. This approach is particularly suited to the likelihood analysis of local primordial non-Gaussianity f NL through the scale-dependent halo bias, and we demonstrate its applicability with N -body simulations. We also release our public code harmonia for galaxy clustering likelihood inference in spherical Fourier or hybrid-basis analyses.

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Cosmological inference from galaxy-clustering power spectrum: Gaussianization and covariance decomposition

Monthly Notices of the Royal Astronomical Society, 2019

Likelihood fitting to two-point clustering statistics made from galaxy surveys usually assumes a ... more Likelihood fitting to two-point clustering statistics made from galaxy surveys usually assumes a multivariate normal distribution for the measurements, with justification based on the central limit theorem given the large number of overdensity modes. However, this assumption cannot hold on the largest scales where the number of modes is low. Whilst more accurate distributions have previously been developed in idealized cases, we derive a procedure suitable for analysing measured monopole power spectra with window effects, stochastic shot noise, and the dependence of the covariance matrix on the model being fitted all taken into account. A data transformation is proposed to give an approximately Gaussian likelihood, with a variance–correlation decomposition of the covariance matrix to account for its cosmological dependence. By comparing with the modified-t likelihood derived under the usual normality assumption, we find in numerical tests that our new procedure gives more accurate constraints on the local non-Gaussianity parameter, which is sensitive to the large-scale power. A simple data analysis pipeline is provided for straightforward application of this new approach in preparation for forthcoming large galaxy surveys such as DESI and Euclid.

Impact of relativistic effects on the primordial non-Gaussianity signature in the large-scale clustering of quasars

Monthly Notices of the Royal Astronomical Society, 2020

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Cosmological Inference from Galaxy-Clustering Power Spectrum: Gaussianization and Covariance Decomposition

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Primordial Gravitational Waves from Cosmic Inflation

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Cosmological inference from galaxy-clustering power spectrum: Gaussianization and covariance decomposition

Monthly Notices of the Royal Astronomical Society, 2019

Likelihood fitting to two-point clustering statistics made from galaxy surveys usually assumes a ... more Likelihood fitting to two-point clustering statistics made from galaxy surveys usually assumes a multivariate normal distribution for the measurements, with justification based on the central limit theorem given the large number of overdensity modes. However, this assumption cannot hold on the largest scales where the number of modes is low. Whilst more accurate distributions have previously been developed in idealized cases, we derive a procedure suitable for analysing measured monopole power spectra with window effects, stochastic shot noise and the dependence of the covariance matrix on the model being fitted all taken into account. A data transformation is proposed to give an approximately Gaussian likelihood, with a variance-correlation decomposition of the covariance matrix to account for its cosmological dependence. By comparing with the modified-t likelihood derived under the usual normality assumption, we find in numerical tests that our new procedure gives more accurate constraints on the local non-Gaussianity parameter f NL , which is sensitive to the large-scale power. A simple data analysis pipeline is provided for straightforward application of this new approach in preparation for forthcoming large galaxy surveys such as DESI and Euclid.

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Hybrid-basis inference for large-scale galaxy clustering: combining spherical and Cartesian Fourier analyses

Journal of Cosmology and Astroparticle Physics, 2020

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Cosmological inference from galaxy-clustering power spectrum: Gaussianization and covariance decomposition

Monthly Notices of the Royal Astronomical Society, 2019

Likelihood fitting to two-point clustering statistics made from galaxy surveys usually assumes a ... more Likelihood fitting to two-point clustering statistics made from galaxy surveys usually assumes a multivariate normal distribution for the measurements, with justification based on the central limit theorem given the large number of overdensity modes. However, this assumption cannot hold on the largest scales where the number of modes is low. Whilst more accurate distributions have previously been developed in idealized cases, we derive a procedure suitable for analysing measured monopole power spectra with window effects, stochastic shot noise, and the dependence of the covariance matrix on the model being fitted all taken into account. A data transformation is proposed to give an approximately Gaussian likelihood, with a variance–correlation decomposition of the covariance matrix to account for its cosmological dependence. By comparing with the modified-t likelihood derived under the usual normality assumption, we find in numerical tests that our new procedure gives more accurate constraints on the local non-Gaussianity parameter, which is sensitive to the large-scale power. A simple data analysis pipeline is provided for straightforward application of this new approach in preparation for forthcoming large galaxy surveys such as DESI and Euclid.

Mike S Wang

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