相关论文: A Method to Distinguish Quiescent and Dusty Star-f…

$t$-SNE is an embedding method that the data science community has widely Two interesting characteristics of t-SNE are the structure preservation property and the answer to the crowding problem, where all neighbors in high dimensional space…

The dimensionality reduction has been widely introduced to use the high-dimensional data for regression, classification, feature analysis, and visualization. As the one technique of dimensionality reduction, a stochastic neighbor embedding…

We present a new method to classify the broad band optical-NIR spectral energy distributions (SEDs) of galaxies using three shape parameters (super-colours) based on a Principal Component Analysis of model SEDs. As well as providing a…

This paper presents a kernelized version of the t-SNE algorithm, capable of mapping high-dimensional data to a low-dimensional space while preserving the pairwise distances between the data points in a non-Euclidean metric. This can be…

One of the most striking properties of galaxies is the bimodality in their star-formation rates. A major puzzle is why any given galaxy is star-forming or quiescent, and a wide range of physical mechanisms have been proposed as solutions.…

Our understanding of galaxy evolution is derived from large surveys designed to maximize efficiency by only observing the minimum amount needed to infer properties for a typical galaxy. However, for a few percent of galaxies in every…

We present a systematic search for wide-separation (Einstein radius >1.5"), galaxy-scale strong lenses in the 30 000 sq.deg of the Pan-STARRS 3pi survey on the Northern sky. With long time delays of a few days to weeks, such systems are…

We introduce Deep-CEE (Deep Learning for Galaxy Cluster Extraction and Evaluation), a proof of concept for a novel deep learning technique, applied directly to wide-field colour imaging to search for galaxy clusters, without the need for…

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…

In recent work (arXiv:1101.0002) we have suggested that the high-redshift (z ~ 2-4) bright submillimetre galaxy (SMG) population is heterogeneous, with major mergers contributing both at early stages, where quiescently star-forming discs…

A new set of color selection criteria (VJL) analogous with the BzK method is designed to select both star-forming galaxies (SFGs) and passively-evolving galaxies (PEGs) at 2.3<z<3.5 by using rest-frame UV--optical (V-J vs. J-L) colors. The…

We investigate and demonstrate the use of convolutional neural networks (CNNs) for the task of distinguishing between merging and non-merging galaxies in simulated images, and for the first time at high redshifts (i.e. $z=2$). We extract…

Ground-based optical surveys such as PanSTARRS, DES, and LSST, will produce large catalogs to limiting magnitudes of r > 24. Star-galaxy separation poses a major challenge to such surveys because galaxies---even very compact…

Aims: We present a new method that uses luminosity or stellar mass functions combined with clustering measurements to select samples of galaxies at different redshifts likely to follow a progenitor-to-descendant relationship. As the method…

In modern astrophysics, the machine learning has increasingly gained more popularity with its incredibly powerful ability to make predictions or calculated suggestions for large amounts of data. We describe an application of the supervised…

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…

Reproducing color-magnitude diagrams (CMDs) of star-resolved galaxies is one of the most precise methods for measuring the star formation history (SFH) of nearby galaxies back to the earliest time. The upcoming big data era poses challenges…

Traditional spectral energy distribution (SED)-fitting methods for stellar mass estimation face persistent challenges including systematic biases and computational constraints. We present a controlled comparison of machine learning (ML) and…

We present rest-frame optical spectra from Keck/MOSFIRE and Keck/NIRES of 16 candidate ultramassive galaxies targeted as part of the Massive Ancient Galaxies at $z>3$ Near-Infrared (MAGAZ3NE) Survey. These candidates were selected to have…

In this second paper in a series of papers based on the most-up-to-date catalogue of symbiotic stars (SySts), we present a new approach for identifying and distinguishing SySts from other Halpha emitters in photometric surveys using machine…