arXiv subjects

Jiani Chu

Publications and source records attributed to Jiani Chu.

4 recordsLinked to original sources

arXiv2026

The Galaxy Stellar Mass-SFR-Size Relation in EAGLE, TNG100, and Observations

Stellar mass, size, and star formation rate (SFR) are fundamental properties that encode the structural and evolutionary states of galaxies. Observations reveal a mass-SFR-size relation whereby galaxies become more compact both above and below the ridge of the star-forming main sequence (SFMS), linking galaxy structure to star formation activity. We investigate this relation by comparing galaxies from two cosmological hydrodynamical simulations, EAGLE and TNG100, with observational samples from SDSS and CANDELS over three redshift intervals (0 < z < 0.2, 0.5 < z < 1.5, and 1.5 < z < 2.5). Both simulations reproduce the observed trend that galaxy sizes decrease with increasing offset away from the SFMS. This trend, however, weakens and is not detected in the observational sample at 1.5 < z < 2.5, likely due to increased measurement uncertainties. In contrast, the trend persists in both simulations up to z = 2.5. Across all redshifts, EAGLE predicts a stronger size dependence on SFMS offset than observed, whereas TNG100 exhibits a weaker dependence. We discuss how this mass-SFR-size relation can be understood in terms of different time variability in star formation rate across the SFMS.

astro-ph.GA↗

arXiv2026

Dynamical Modelling of Galactic Kinematics using Neural Networks

The advent of integral field data has revolutionised the study of galaxy evolution. A key component of this is dynamical modelling methods which have allowed for crucial insights to be made from kinematic data. Despite this importance, most dynamical models make a number of key assumptions which do not hold for real galaxies. These include assumptions about the geometry (axisymmetry or triaxiality), the shape of the velocity ellipsoid, and the shape of the underlying stellar distribution. At the same time, machine learning methods are becoming increasingly powerful, with many applications appearing in astronomy. As a first step towards building new dynamical modelling methods with machine learning, it is important to understand the types of machine learning architectures that are best fit for dynamical modelling. To investigate this, we construct a training set of dynamical models of early-type galaxies using Jeans Anisotropic Modelling (JAM). We then train a neural network on this data using the parameters of JAM and mock photometry as the input. We are able to accurately model JAM galaxies with relatively simple machine learning architectures, leading to a significant speed increase over traditional JAM modelling.

astro-ph.GA↗

arXiv2023

Galaxy stellar and total mass estimation using machine learning

Conventional galaxy mass estimation methods suffer from model assumptions and degeneracies. Machine learning, which reduces the reliance on such assumptions, can be used to determine how well present-day observations can yield predictions for the distributions of stellar and dark matter. In this work, we use a general sample of galaxies from the TNG100 simulation to investigate the ability of multi-branch convolutional neural network (CNN) based machine learning methods to predict the central (i.e., within $1-2$ effective radii) stellar and total masses, and the stellar mass-to-light ratio $M_*/L$. These models take galaxy images and spatially-resolved mean velocity and velocity dispersion maps as inputs. Such CNN-based models can in general break the degeneracy between baryonic and dark matter in the sense that the model can make reliable predictions on the individual contributions of each component. For example, with $r$-band images and two galaxy kinematic maps as inputs, our model predicting $M_*/L$ has a prediction uncertainty of 0.04 dex. Moreover, to investigate which (global) features significantly contribute to the correct predictions of the properties above, we utilize a gradient boosting machine. We find that galaxy luminosity dominates the prediction of all masses in the central regions, with stellar velocity dispersion coming next. We also investigate the main contributing features when predicting stellar and dark matter mass fractions ($f_*$, $f_{\rm DM}$) and the dark matter mass $M_{DM}$, and discuss the underlying astrophysics.

astro-ph.GA↗

arXiv2023

A model local interpretation routine for deep learning based radio galaxy classification

Radio galaxy morphological classification is one of the critical steps when producing source catalogues for large-scale radio continuum surveys. While many recent studies attempted to classify source radio morphology from survey image data using deep learning algorithms (i.e., Convolutional Neural Networks), they concentrated on model robustness most time. It is unclear whether a model similarly makes predictions as radio astronomers did. In this work, we used Local Interpretable Model-agnostic Explanation (LIME), an state-of-the-art eXplainable Artificial Intelligence (XAI) technique to explain model prediction behaviour and thus examine the hypothesis in a proof-of-concept manner. In what follows, we describe how \textbf{LIME} generally works and early results about how it helped explain predictions of a radio galaxy classification model using this technique.

astro-ph.IM↗