相关论文: Neural Deprojection of Galaxy Stellar Mass Profile…
Statistical modeling of data sets by neural-network techniques is offered as an alternative to traditional semiempirical approaches to global modeling of nuclear properties. New results are presented to support the position that such novel…
We present a dynamical analysis of 673 galactic Planetary Nebulae, using a two-integral axisymmetric model with a Kuzmin-Kutuzov St\"{a}ckel potential. The method fits the kinematics to the projected moments of a distribution function, by…
Current ground-based cosmological surveys, such as the Dark Energy Survey (DES), are predicted to discover thousands of galaxy-scale strong lenses, while future surveys, such as the Vera Rubin Observatory Legacy Survey of Space and Time…
Stellar astrophysics relies on diverse observational modalities-primarily photometric light curves and spectroscopic data from which fundamental stellar properties are inferred. While machine learning (ML) has advanced analysis within…
To fully understand, analyze, and determine the behavior of dynamical systems, it is crucial to identify their intrinsic modal coordinates. In nonlinear dynamical systems, this task is challenging as the modal transformation based on the…
Multi-object and multi-fiber spectrographs on 4 and 8 meter telescopes make it possible to use extragalactic planetary nebulae (PNe) in the outer halos of early-type galaxies as kinematical tracers, where classical techniques based on…
Black holes and neutron stars present extreme forms of matter that cannot be created as such in a laboratory on Earth. Instead, we have to observe and analyze the experiments that are ongoing in the Universe. The most telling observations…
We investigate whether combining gas and stellar kinematic maps provides measurable advantages in recovering galaxy mass profiles, compared to using single-component maps alone. While traditional methods struggle to integrate multi-tracer…
The galaxy total mass inside the effective radius encode important information on the dark matter and galaxy evolution model. Total "central" masses can be inferred via galaxy dynamics or with gravitational lensing, but these methods have…
We present a learning-based dynamics model for granular material manipulation. Inspired by the Eulerian approach commonly used in fluid dynamics, our method adopts a fully convolutional neural network that operates on a density field-based…
One of the most relevant problems in the extraction of scientifically useful information from wide field astronomical images (both photographic plates and CCD frames) is the recognition of the objects against a noisy background and their…
With the advent of future big-data surveys, automated tools for unsupervised discovery are becoming ever more necessary. In this work, we explore the ability of deep generative networks for detecting outliers in astronomical imaging…
Fitting parameterized models to images of galaxies has become the standard for measuring galaxy morphology. This forward modelling technique allows one to account for the PSF to effectively study semi-resolved galaxies. However, using a…
Modern cosmological surveys such as the Hyper Suprime-Cam (HSC) survey produce a huge volume of low-resolution images of both distant galaxies and dim stars in our own galaxy. Being able to automatically classify these images is a…
Mergers are an important aspect of galaxy formation and evolution. We aim to test whether deep learning techniques can be used to reproduce visual classification of observations, physical classification of simulations and highlight any…
Galaxies can be described by features of their optical spectra such as oxygen emission lines, or morphological features such as spiral arms. Although spectroscopy provides a rich description of the physical processes that govern galaxy…
Galaxy mergers, the dynamical process during which two galaxies collide, are among the most spectacular phenomena in the Universe. During this process, the two colliding galaxies are tidally disrupted, producing significant visual features…
The efficiency of the transport of angular momentum and chemical elements inside intermediate-mass stars lacks proper calibration, thereby introducing uncertainties on a star's evolutionary pathway. Improvements require better estimation of…
With the advent of billion-galaxy surveys with complex data, the need of the hour is to efficiently model galaxy spectral energy distributions (SEDs) with robust uncertainty quantification. The combination of Simulation-Based inference…
Dynamical models are used to study dark matter (DM) in galaxies, how galaxies assemble through mergers, and to test galaxy formation models. Despite its widespread use, there has been no systematic study quantifying how much information can…