arXiv subjects

Kwok Sun Tang

Publications and source records attributed to Kwok Sun Tang.

7 recordsLinked to original sources

arXiv2023

QUOTAS: A new research platform for the data-driven investigation of black holes

We present QUOTAS, a novel research platform for the data-driven investigation of super-massive black hole (SMBH) populations. While SMBH data sets -- observations and simulations -- have grown rapidly in complexity and abundance, our computational environments and analysis tools have not matured commensurately to exhaust opportunities for discovery. Motivated to explore BH host galaxy and the parent dark matter halo connection, in this pilot version of QUOTAS, we assemble and co-locate the high-redshift, luminous quasar population at $z \geq 3$ alongside simulated data of the same epochs. Leveraging machine learning algorithms (ML) we expand simulation volumes that successfully replicate halo populations beyond the training set. Training ML on the Illustris-TNG300 simulation that includes baryonic physics, we populate the larger LEGACY Expanse dark matter-only box with quasars. Our first science results comparing observational and ML simulated quasars at $z \sim 3$, reveal that while the recovered Black Hole Mass Functions and clustering are in good agreement, simulated SMBHs fail to accrete, shine and grow at high enough rates to match observed quasars. We conclude that sub-grid models of mass accretion and SMBH feedback implemented in Illustris-TNG300 do not reproduce their observed mass growth. QUOTAS, demonstrates the power of ML, both for analyzing large complex datasets, and offering a unique opportunity to interrogate our theoretical model assumptions. We deploy ML again to derive and devise an optimal survey strategy for bringing the undetected lower luminosity quasar population into view. QUOTAS, and all related materials are publicly available at the Google Kaggle platform.

astro-ph.CO↗

arXiv2022

Reduced Order Model for Chemical Kinetics: A case study with Primordial Chemical Network

Chemical kinetics plays an important role in governing the thermal evolution in reactive flows problems. The possible interactions between chemical species increase drastically with the number of species considered in the system. Various ways have been proposed before to simplify chemical networks with an aim to reduce the computational complexity of the chemical network. These techniques oftentimes require domain-knowledge experts to handcraftedly identify important reaction pathways and possible simplifications. Here, we propose a combination of autoencoder and neural ordinary differential equation to model the temporal evolution of chemical kinetics in a reduced subspace. We demonstrated that our model has achieved a close-to 10-fold speed-up compared to commonly used astro-chemistry solver for a 9-species primordial network, while maintaining 1 percent accuracy across a wide-range of density and temperature.

astro-ph.IM↗

arXiv2022

Galaxy Merger Reconstruction with Equivariant Graph Normalizing Flows

A key yet unresolved question in modern-day astronomy is how galaxies formed and evolved under the paradigm of the $Λ$CDM model. A critical limiting factor lies in the lack of robust tools to describe the merger history through a statistical model. In this work, we employ a generative graph network, E(n) Equivariant Graph Normalizing Flows Model. We demonstrate that, by treating the progenitors as a graph, our model robustly recovers their distributions, including their masses, merging redshifts and pairwise distances at redshift z=2 conditioned on their z=0 properties. The generative nature of the model enables other downstream tasks, including likelihood-free inference, detecting anomalies and identifying subtle correlations of progenitor features.

astro-ph.GA↗

arXiv2021

SISE-PC: Semi-supervised Image Subsampling for Explainable Pathology

Although automated pathology classification using deep learning (DL) has proved to be predictively efficient, DL methods are found to be data and compute cost intensive. In this work, we aim to reduce DL training costs by pre-training a Resnet feature extractor using SimCLR contrastive loss for latent encoding of OCT images. We propose a novel active learning framework that identifies a minimal sub-sampled dataset containing the most uncertain OCT image samples using label propagation on the SimCLR latent encodings. The pre-trained Resnet model is then fine-tuned with the labelled minimal sub-sampled data and the underlying pathological sites are visually explained. Our framework identifies upto 2% of OCT images to be most uncertain that need prioritized specialist attention and that can fine-tune a Resnet model to achieve upto 97% classification accuracy. The proposed method can be extended to other medical images to minimize prediction costs.

cs.CV↗

arXiv2019

Physical Properties of the star-forming clusters in NGC 6334

We have studied the high-mass star-forming complex NGC 6334 with ALMA in the continuum emission at a frequency of 87.6 GHz, achieving a spatial resolution of 1300 au. Detecting 142 compact sources distributed over the whole observed area, we then used machine learning algorithms to group the compact cores in different clusters. A total of four main clusters were identified: NGC 6334-E, NGC 6334-I, NGC 6334-I(N) and NGC 6334-I(NW). The typical separations between cluster members (4000-12000 au) together with the core masses (0.2-100 $M_{\odot}$) are in agreement with turbulent fragmentation at scales of 0.1 pc. We find that the CMFs (core mass functions) show an excess of massive cores compared to the IMF. Typical uncertainties in temperature or unresolved multiplicity may mimic the observed excess of massive cores. Evidence of mass segregation in NGC 6334-I and NGC 6334-I(N), with the most massive cores located closer to the center, was found. Correlations between the physical properties of the clusters and their evolutionary stage were searched for, and found to show a larger separation between cores in the more evolved clusters, favouring the role of gas expulsion and stellar ejection over time. From these results, we suggest that NGC 6334-I(N) was primordially segregated, while NGC 6334-I may have become mass-segregated over time due to dynamical effects. Finally, the lack of massive cores in the most evolved cluster suggests that the gas reservoir may be already exhausted, while the less evolved clusters still have access to a larger gas mass reservoir. In general, the fragmentation in NGC 6334 at large scales (about 1 pc) is governed by turbulent pressure, while at smaller scales (a few hundred au), thermal pressure regulates the fragmentation process.

astro-ph.SR↗

arXiv2019

Probing the Turbulence Dissipation Range and Magnetic Field Strengths in Molecular Clouds. II. Directly Probing the Ion-neutral Decoupling Scale

The linewidth of ions has been observed to be systematically narrower than that of the coexisting neutrals in molecular clouds (Houde et al. 2000) and been interpreted as the signature of the decoupling of the neutral turbulence from magnetic fields in partially ionized medium (Li & Houde 2008; Paper I, hereafter). As a sequel of Paper I, here we present further observational evidence that lend support to these earlier proposals with the velocity coordinate spectrum analysis (Lazarian & Pogosyan 2006). We recover the turbulent energy spectra of HCN and HCO+(4-3) in a starless molecular filament in NGC 6334 where magnetic fields play a dynamically important role (Li et al. 2015). Our analysis showed that the neutral spectrum is consistent with Kolmogorov-type (k^-5/3, where k is the wave number), while that of the ions is the same on the large scale but steeper (k^-2) for scales smaller than 0.404pc. We carefully ruled out the possibilities that the spectrum difference can stem from the differences of ion and neutral optical depth and hyper-fine structures.

astro-ph.GA↗

arXiv2014

The Link between Magnetic Fields and Cloud/Star Formation

The question whether magnetic fields play an important role in the processes of molecular cloud and star formation has been debated for decades. Recent observations have revealed a simple picture that may help illuminate these questions: magnetic fields have a tendency to preserve their orientation at all scales that have been probed - from 100-pc scale inter-cloud media down to sub-pc scale cloud cores. This ordered morphology has implications for the way in which self-gravity and turbulence interact with magnetic fields: both gravitational contraction and turbulent velocities should be anisotropic, due to the influence of dynamically important magnetic fields. Such anisotropy is now observed. Here we review these recent observations and discuss how they can improve our understanding of cloud/star formation.

astro-ph.GA↗