Cosmology and Astrophysics with Intensity Mapping

Intensity mapping (IM) has emerged as a promising technique to probe the largescale structures and galaxy formation and evolution across cosmic history. As IM measures the aggregate emission from all sources, it can overcome the limitation of conventional detection-based observations, where the emission from diffuse populations and high-redshift faint galaxies cannot be resolved individually. As several IM experiments will come online in the next decade, demand for IM modeling and data analysis strategies has increased. In this thesis, we present a range of analysis techniques, theoretical modeling, and data analysis results related to IM. In Chapter 2, we aim to answer the question: When should we use IM? We present a formalism to describe both IM and galaxy detection (GD) approaches, and use it to quantify their individual performance when measuring the large-scale structure (LSS). With this formalism, we can identify the scenarios where each approach is advantageous. We also develop a simple metric for determining the optimal strategy to map the LSS with future experiments. In Chapters 3 and 4, we interrogate methods for improving the line intensity mapping (LIM) analysis. LIM traces the three-dimensional structure of the universe by probing the emission field from a spectral line. One particular challenge for LIM is to separate the target line signals from interloper lines along the line of sight in order to extract the desired cosmological and astrophysical information. Previously proposed methods of line de-blending, such as masking and cross-correlation, rely on the external galaxy tracers, but sometimes a galaxy catalog with sufficient depth and sky coverage does not exist. Therefore, we develop two new methods for performing line de-confusion that do not require any external information. The first method (Chapter 3) uses the distinct shape of large-scale two-dimensional power spectra of signals and interlopers to distinguish the line emission from different redshifts. The second method (Chapter 4) reconstructs the intensity maps of individual lines from LIM data in the phase space, using multiple lines from the same source to identify the source redshift. We show that both of our methods are able to effectively extract desired line signals from the upcoming LIM experiments. In Chapter 5, we discuss the application of IM for studying the extragalactic background light (EBL), the integrated light from all sources of emission in the universe. Previous studies on the fluctuations of the EBL indicate that the intra-halo light (IHL) has a significant contribution to the near-infrared EBL. Chapter 5 presents the results on probing the IHL using a stacking analysis of images from the Cosmic Infrared Background Experiment (CIBER). CIBER is a rocket-borne experiment designed to image and perform photometry of the near-infrared EBL. Our results suggest that at z ∼ 0.3 the IHL comprises a large fraction of light associated with ∼ L∗ galaxies, implying that the IHL accounts for a non-negligible fraction of the near-infrared cosmic radiation budget. In Chapter 6, we present a forecast on the EBL constraints with the upcoming SPHEREx mission. We consider cross correlating SPHEREx intensity maps with galaxy catalogs from several current and future surveys. Our model predicts that the EBL spectrum as a function of redshift can be detected from the local universe to the epoch of reionization. The analysis techniques developed in this thesis can help better extract the information from the IM data; the future IM experiments will extend our current works on investigating the EBL. Therefore, the research in this thesis provides important toolkits and foundations for upcoming IM experiments.</p

Spectral Line De-confusion in an Intensity Mapping Survey

Spectral line intensity mapping has been proposed as a promising tool to efficiently probe the cosmic reionization and the large-scale structure. Without detecting individual sources, line intensity mapping makes use of all available photons and measures the integrated light in the source confusion limit, to efficiently map the three-dimensional matter distribution on large scales as traced by a given emission line. One particular challenge is the separation of desired signals from astrophysical continuum foregrounds and line interlopers. Here we present a technique to extract large-scale structure information traced by emission lines from different redshifts, embedded in a three-dimensional intensity mapping data cube. The line redshifts are distinguished by the anisotropic shape of the power spectra when projected onto a common coordinate frame. We consider the case where high-redshift [CII] lines are confused with multiple low-redshift CO rotational lines. We present a semi-analytic model for [CII] and CO line estimates based on the cosmic infrared background measurements, and show that with a modest instrumental noise level and survey geometry, the large-scale [CII] and CO power spectrum amplitudes can be successfully extracted from a confusion-limited data set, without external information. We discuss the implications and limits of this technique for possible line intensity mapping experiments.Comment: 13 pages, 14 figures, accepted by Ap

The narrative of galaxy morphological classification told through machine learning

In this thesis, we present a complete study of machine learning applications, in- cluding both supervised and unsupervised, for galaxy morphological classification using calibrated imaging data. Two main topics are approached: (1) classification - we discuss optimal machine learning technique in terms of accuracy, efficiency, and inclusiveness using imaging data for large-scale surveys; (2) exploration - we explore galaxy morphology without human bias and discuss a novel morphological classification scheme defined by machine learning. In the classification task, we first carry out a thorough comparison in accuracy and efficiency between several common supervised methods using the Dark Energy Survey (DES) imaging data (Chapter 2). The morphology labels from the Galaxy Zoo 1 (GZ1) catalogue (Lintott et al., 2008, 2011) are used to train the supervised methods. We conclude that using a combination of linear and gradient images (with the Histogram of Oriented Gradient technique) to train our convolutional neural networks (CNN) shows the most optimal performance in terms of accuracy and efficiency amongst the supervised methods tested using imaging data. Due to the better resolution (0. 263 per pixel) and greater depth (i = 22.51) of DES data than the Sloan Digital Sky survey (SDSS) imag- ing data used in the GZ1 project, we reveal that ∼ 2.5% galaxies in our dataset are mislabeled by the GZ1. After correcting these galaxies’ labels based on the DES imaging data, we reach a final accuracy of over 0.99 for binary classification (ellipticals and spirals) with the CNN (Chapter 3). We then use the CNN to build one of the largest galaxy morphological classification catalogues which in- cludes over 20 million galaxies from the DES Year 3 data (Chapter 4). However, supervised machine learning techniques are biased towards the training set and the human-defined labels. Therefore, we test the possibility of a classification task using unsupervised machine learning techniques (Chapter 5 and Chapter 6). In Chapter 5, the combination of a convolutional autoencoder and a Bayesian Gaussian mixture model successfully distinguishes a variety of lensing features such as different Einstein ring sizes and arcs from galaxy-galaxy strong lensing systems (GGSL). This unsupervised method categorises simulated images from Metcalf et al. (2019a) into 24 classes without human involvement and picks up ∼ 63 percent of lensing images from all lenses in the training set. Additionally, with fewer human judgements involved to classify 24 machine classes, we reach an accuracy of 77.3 ± 0.5% in the binary classification of lensing and non-lensing systems. On the other hand, unsupervised machine learning techniques are used to objectively explore galaxy morphology using the SDSS imaging data in Chapter 6. We improve the efficiency of the unsupervised method used in Chapter 5 by applying a vector quantisation process in the feature learning phase, and achieve a better ‘accuracy’ compared to the current knowledge towards galaxy morphology using an uneven iterative hierarchical clustering (Chapter 6). This unsupervised method can categorise the galaxies in the dataset, which includes 23% early-type galaxies (ETGs) and 77% late-type galaxies (LTGs), into two preliminary classes and reach an accuracy of ∼ 0.87 for binary classification of ETGs and LTGs. To explore galaxy morphology, our method provides 27 classes based on the galaxy shape and structure. We further confirm that regardless of the galaxy morphological mix that existed in the dataset, this unsupervised machine captures consistent features. The 27 machine-defined morphological classes show a solid division on stellar properties such as colour, absolute magnitude, stellar mass, and physical size of the galaxies. Each class has distinctive galaxy features which distinguish each class uniquely from other classes. Moreover, when comparing the machine classes with visual Hubble types, it is clear that a mix of different galaxy struc- tures can exist in one visual morphological Hubble type. This reveals that an intrinsic uncertainty exists in visual classification schemes such as the Hubble sequence in precisely classifying galaxies. With the investigation in Chapter 6, we propose to rethink the current visual morphological classification scheme, and consider the possibility of using a novel classification scheme defined by machine learning to re-approach studies of galaxy evolution and formation from a different perspective

Is the Radio Source Dipole from NVSS Consistent with the CMB and Λ\LambdaCDM?

The dipole moment in the angular distribution of the cosmic microwave background (CMB) is thought to originate from the Doppler Effect and our motion relative to the CMB frame. Observations of large-scale structure (LSS) should show a related "kinematic dipole" and help test the kinematic origin of the CMB dipole. Intriguingly, many previous LSS dipole studies suggest discrepancies with the expectations from the CMB. Here we reassess the apparent inconsistency between the CMB measurements and dipole estimates from the NVSS catalog of radio sources. We find that it is important to account for the shot-noise and clustering of the NVSS sources, as well as kinematic contributions, in determining the expected dipole signal. We use the clustering redshift method and a cross-matching technique to refine estimates of the clustering term. We then derive a probability distribution for the expected NVSS dipole in a standard $\Lambda$CDM cosmological model including all (i.e., kinematic, shot-noise and clustering) dipole components. Our model agrees with most of the previous NVSS dipole measurements in the literature at better than $\lesssim 2\sigma$. We conclude that the NVSS dipole is consistent with a kinematic origin for the CMB dipole within $\Lambda$CDM.Comment: 24 pages, 9 figures, submitted to Ap