arXiv subjects

Zehao Jin

Publications and source records attributed to Zehao Jin.

26 records · Page 2Linked to original sources

arXiv2024

Beyond Causal Discovery for Astronomy: Learning Meaningful Representations with Independent Component Analysis

We present the first steps toward applying causal representation learning to astronomy. Following up on previous work that introduced causal discovery to the field for the first time, here we solve a long standing conundrum by identifying the direction of the causal relation between supermassive black hole (SMBH) mass and their host galaxy properties. This leverages a score-based causal discovery approach with an exact posterior calculation. Causal relations between SMBHs and their host galaxies are further clarified by Independent Component Analysis (ICA). The astrophysical problem we focus on is one of the most important open issues in the field and one that has not seen a definitive resolution in decades. We consider the space of six physical properties of galaxies, subdivided by morphology: elliptical, lenticular, and spiral, plus SMBH mass. We calculate an exact posterior over the space of directed acyclic graphs for these variables based on a flat prior and the Bayesian Gaussian equivalent score. The nature of the causal relation between galaxy properties and SMBH mass is found to vary smoothly with morphology, with galaxy properties determining SMBH mass in ellipticals and vice versa in spirals. This settles a long-standing debate and is compatible with our theoretical understanding of galaxy evolution. ICA reveals a decreasing number of meaningful Independent Components (ICs) from ellipticals and lenticular to spiral. Moreover, we find that only one IC correlates with SMBH mass in spirals while multiple ones do in ellipticals, further confirming our finding that SMBH mass causes galaxy properties in spirals, but the reverse holds in ellipticals.

astro-ph.GA↗

arXiv2024

HELLO project: High-$z$ Evolution of Large and Luminous Objects

We present the High-$z$ Evolution of Large and Luminous Objects (HELLO) project, a set of $\sim\!30$ high-resolution cosmological simulations aimed to study Milky Way analogues ($M_\star\sim10^{10-11}$\,\Msun) at high redshift ($z\sim [2-4]$). Based on the Numerical Investigation of a Hundred Astrophysical Objects (NIHAO), HELLO features an updated scheme for chemical enrichment and the addition of local photoionization feedback. Independently of redshift and mass, our galaxies exhibit a smooth progression along the star formation main sequence until $M_\star \sim\!10^{10.5}$, around which our sample at $z \sim 4$ remains mostly unperturbed while the most massive galaxies at $z \sim 2$ reach their peak star formation rate (SFR) and its subsequent decline, due to a mix of gas consumption and stellar feedback. While AGN feedback remains subdominant with respect to stellar feedback for energy deposition, its localised nature likely adds to the physical processes leading to declining SFRs. The phase in which a galaxy in our mass range can be found at a given redshift is set by its gas reservoir and assembly history. Finally, our galaxies are in excellent agreement with various scaling relations observed with the \textit{Hubble Space Telescope} and the \textit{James Webb Space Telescope}, and hence can be used to provide the theoretical framework to interpret current and future observations from these facilities and shed light on the transition from star-forming to quiescent galaxies.

astro-ph.GA↗

arXiv2024

Identification of Intermediate-mass Black Hole Candidates Among a Sample of Sd Galaxies

We analyzed images of every northern hemisphere Sd galaxy listed in the Third Reference Catalogue of Bright Galaxies (RC3) with a relatively face-on inclination ($θ\leq30°$). Specifically, we measured the spiral arms' winding angle, $ϕ$, in 85 galaxies. We applied a novel black hole mass planar scaling relation involving the rotational velocities (from the literature) and pitch angles of each galaxy to predict central black hole masses. This yielded 23 galaxies, each having at least a 50% chance of hosting a central intermediate-mass black hole (IMBH), $10^2 $1.22% of bright galaxies ($B_{\rm T}\lesssim15.5$ mag) in the local Universe host an IMBH (i.e., the "occupation fraction"), which implies a number density $>$$4.96\times10^{-6}$ Mpc$^{-3}$ for central IMBHs. Finally, we observe that Sd galaxies exhibit an unexpected diversity of properties that resemble the general population of spiral galaxies, albeit with an enhanced signature of the eponymous prototypical traits (i.e., low masses, loosely wound spiral arms, and smaller rotational velocities).

astro-ph.GA↗

arXiv2024

Quantitatively rating galaxy simulations against real observations with anomaly detection

Cosmological galaxy formation simulations are powerful tools to understand the complex processes that govern the formation and evolution of galaxies. However, evaluating the realism of these simulations remains a challenge. The two common approaches for evaluating galaxy simulations is either through scaling relations based on a few key physical galaxy properties, or through a set of pre-defined morphological parameters based on galaxy images. This paper proposes a novel image-based method for evaluating the quality of galaxy simulations using unsupervised deep learning anomaly detection techniques. By comparing full galaxy images, our approach can identify and quantify discrepancies between simulated and observed galaxies. As a demonstration, we apply this method to SDSS imaging and NIHAO simulations with different physics models, parameters, and resolution. We further compare the metric of our method to scaling relations as well as morphological parameters. We show that anomaly detection is able to capture similarities and differences between real and simulated objects that scaling relations and morphological parameters are unable to cover, thus indeed providing a new point of view to validate and calibrate cosmological simulations against observed data.

astro-ph.GA↗

arXiv2023

Causa prima: cosmology meets causal discovery for the first time

In astrophysics, experiments are impossible. We thus must rely exclusively on observational data. Other observational sciences increasingly leverage causal inference methods, but this is not yet the case in astrophysics. Here we attempt causal discovery for the first time to address an important open problem in astrophysics: the (co)evolution of supermassive black holes (SMBHs) and their host galaxies. We apply the Peter-Clark (PC) algorithm to a comprehensive catalog of galaxy properties to obtain a completed partially directed acyclic graph (CPDAG), representing a Markov equivalence class over directed acyclic graphs (DAGs). Central density and velocity dispersion are found to cause SMBH mass. We test the robustness of our analysis by random sub-sampling, recovering similar results. We also apply the Fast Causal Inference (FCI) algorithm to our dataset to relax the hypothesis of causal sufficiency, admitting unobserved confounds. Hierarchical SMBH assembly may provide a physical explanation for our findings.

astro-ph.GA↗

arXiv2023

Discovering Black Hole Mass Scaling Relations with Symbolic Regression

Our knowledge of supermassive black holes (SMBHs) and their relation to their host galaxies is still limited, and there are only around 150 SMBHs that have their masses directly measured and confirmed. Better black hole mass scaling relations will help us reveal the physics of black holes, as well as predict black hole masses that are not yet measured. Here, we apply symbolic regression, combined with random forest to those directly-measured black hole masses and host galaxy properties, and find a collection of higher-dimensional (N-D) black hole mass scaling relations. These N-D black hole mass scaling relations have scatter smaller than any of the existing black hole mass scaling relations. One of the best among them involves the parameters of central stellar velocity dispersion, bulge-to-total ratio, and density at the black hole's sphere-of-influence with an intrinsic scatter of $ε=0.083\,\ \text{dex}$, significantly lower than $ε\sim 0.3\,\ \text{dex}$ for the M-$σ$ relation. These relations will inspire black hole physics, test black hole models implemented in simulations, and estimate unknown black hole masses on an unprecedented precision.

astro-ph.GA↗

arXiv2023

Discovery of a Planar Black Hole Mass Scaling Relation for Spiral Galaxies

Supermassive black holes (SMBHs) are tiny in comparison to the galaxies they inhabit, yet they manage to influence and coevolve along with their hosts. Evidence of this mutual development is observed in the structure and dynamics of galaxies and their correlations with black hole mass ($M_\mathrm{BH}$). For our study, we focus on relative parameters that are unique to only disk galaxies. As such, we quantify the structure of spiral galaxies via their logarithmic spiral-arm pitch angles ($ϕ$) and their dynamics through the maximum rotational velocities of their galactic disks ($v_\mathrm{max}$). In the past, we have studied black hole mass scaling relations between $M_\mathrm{BH}$ and $ϕ$ or $v_\mathrm{max}$, separately. Now, we combine the three parameters into a trivariate $M_\mathrm{BH}$-$ϕ$-$v_\mathrm{max}$ relationship that yields best-in-class accuracy in prediction of black hole masses in spiral galaxies. Because most black hole mass scaling relations have been created from samples of the largest SMBHs within the most massive galaxies, they lack certainty when extrapolated to low-mass spiral galaxies. Thus, it is difficult to confidently use existing scaling relations when trying to identify galaxies that might harbor the elusive class of intermediate-mass black holes (IMBHs). Therefore, we offer our novel relationship as an ideal predictor to search for IMBHs and probe the low-mass end of the black hole mass function by utilizing spiral galaxies. Already with rotational velocities widely available for a large population of galaxies and pitch angles readily measurable from uncalibrated images, we expect that the $M_\mathrm{BH}$-$ϕ$-$v_\mathrm{max}$ fundamental plane will be a useful tool for estimating black hole masses, even at high redshifts.

astro-ph.GA↗

arXiv2020

Learning Principle of Least Action with Reinforcement Learning

Nature provides a way to understand physics with reinforcement learning since nature favors the economical way for an object to propagate. In the case of classical mechanics, nature favors the object to move along the path according to the integral of the Lagrangian, called the action $\mathcal{S}$. We consider setting the reward/penalty as a function of $\mathcal{S}$, so the agent could learn the physical trajectory of particles in various kinds of environments with reinforcement learning. In this work, we verified the idea by using a Q-Learning based algorithm on learning how light propagates in materials with different refraction indices, and show that the agent could recover the minimal-time path equivalent to the solution obtained by Snell's law or Fermat's Principle. We also discuss the similarity of our reinforcement learning approach to the path integral formalism.

cs.LG↗