相关论文: Multi-Layer Perceptron for Predicting Galaxy Param…
MaNGA (Mapping Nearby Galaxies at Apache Point Observatory) is a 6-year SDSS-IV survey that will obtain resolved spectroscopy from 3600 $\AA$ to 10300 $\AA$ for a representative sample of over 10,000 nearby galaxies. In this paper, we…
We present an accelerated calibration framework for semi-analytic galaxy formation models, demonstrated with Galacticus. Rather than fitting directly to properties such as the low-redshift stellar mass function (SMF) - which requires…
Galaxies play a key role in our endeavor to understand how structure formation proceeds in the Universe. For any precision study of cosmology or galaxy formation, there is a strong demand for huge sets of realistic mock galaxy catalogs,…
We present a multilinear analysis to determine the significant predictors of star formation in galaxies using the combined EDGE-CALIFA sample of galaxies. We analyze 1845 kpc-scale lines of sight across 39 galaxies with molecular line…
The aim of the present paper is to quantify the dependence of the estimates of luminosities and stellar mass of early-type galaxies on the different models and model parameters which can be used to analyze the observational data. The paper…
Star-formation activity is a key property to probe the structure formation and hence characterise the large-scale structures of the universe. This information can be deduced from the star formation rate (SFR) and the stellar mass (Mstar),…
In order to build more realistic single stellar population (SSP) models with variable alpha-enhancement, we have recently determined [Mg/Fe] in a uniform scale with a precision of about 0.1 dex for 752 stars in the MILES empirical library.…
We present a novel method to model galactic scale star formation and the resulting emission from star clusters and the multi-phase interstellar medium. We combine global parameters, such as SFR and CMF, with {\sc warpfield} which provides a…
We devise improved photometric parameters for the morphological classification of galaxies using a bright sample from the First Data Release of the Sloan Digital Sky Survey. In addition to using an elliptical aperture concentration index…
Most existing star-galaxy classifiers use the reduced summary information from catalogs, requiring careful feature extraction and selection. The latest advances in machine learning that use deep convolutional neural networks allow a machine…
Data on galaxies at high redshift, identified by the Lyman-break photometric technique, can teach us about how galaxies form and evolve. The stellar masses and other properties of such Lyman break galaxies (LBGs) depend sensitively on the…
Machine learning is transforming materials discovery by providing rapid predictions of material properties, which enables large-scale screening for target materials. However, such models require training data. While automated data…
In deep learning, Multi-Layer Perceptrons (MLPs) have once again garnered attention from researchers. This paper introduces MC-MLP, a general MLP-like backbone for computer vision that is composed of a series of fully-connected (FC) layers.…
We investigate machine learning (ML) techniques for predicting the number of galaxies (N_gal) that occupy a halo, given the halo's properties. These types of mappings are crucial for constructing the mock galaxy catalogs necessary for…
Determining how galaxies accumulate stellar mass is paramount to understanding the Universe. Two primary mechanisms drive this process: star-formation (SF) & mergers. Our understanding of star formation, and to some degree the processes…
The morphology of a galaxy has been shown to encode the evolutionary history and correlates strongly with physical properties such as stellar mass, star formation rates and past merger events. While the majority of galaxies in the local…
Galaxies represent the visible fabric of the Universe and there has been considerable progress recently in both observational and theoretical studies. The underlying goal is to understand the present-day diversity of galaxy forms, masses…
Massive stars play key roles in many astrophysical processes. Deriving atmospheric parameters of massive stars is important to understand their physical properties and thus are key inputs to trace their evolution. Here we report our work on…
To automate source detection, two-dimensional light-profile Sersic modelling and catalogue compilation in large survey applications, we introduce a new code GALAPAGOS, Galaxy Analysis over Large Areas: Parameter Assessment by GALFITting…
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…