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

We train three convolutional neural networks (CNNs) to classify galaxies with Galaxy Zoo 2 dataset and extract the activations from the last fully connected layer or the last average pooling layer of CNNs to study the high-dimensional…

We present the results of a machine learning study to measure the dust content of galaxies observed with JWST at z > 6 through the use of trained neural networks based on high-resolution IllustrisTNG simulations. Dust is an important…

The WHT Enhanced Area Velocity Explorer (WEAVE) is a new, massively multiplexing spectrograph. This new instrument will be exploited to obtain high S/N spectra of $\sim$25000 galaxies at intermediate redshifts for the WEAVE Stellar…

Feature representation is an important aspect of remote-sensing based image classification. While deep convolutional neural networks are able to effectively amalgamate information, large numbers of parameters often make learned features…

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…

The presence of massive quiescent galaxies at high redshifts is still a challenge for most models of galaxy formation. The aim of this work is to compare the observed number density and properties of these galaxies with the predictions of…

Quiescent galaxies over $3<z<6$ are rare and puzzling. They formed and quenched within two billion years and simulations routinely struggle to predict their observed abundances. Developing a robust identification technique for these…

We present the results of a systematic search for candidate quiescent galaxies in the distant Universe in eleven $JWST$ fields with publicly available observations collected during the first three months of operations and covering an…

Massive quiescent galaxies at high redshift can shed light on the processes of galaxy mass assembly and quenching of the star formation at early epochs. We present observer-frame color-color diagrams designed to identify candidate quiescent…

T-distributed stochastic neighbor embedding (t-SNE) is a well-known algorithm for visualizing high-dimensional data by finding low-dimensional representations. In this paper, we study the convergence of t-SNE with generalized kernels and…

Accurately distinguishing between quiescent and star-forming galaxies is essential for understanding galaxy evolution. Traditional methods, such as spectral energy distribution (SED) fitting, can be computationally expensive and may…

We utilize for the first time HST ACS imaging to examine the structural properties of galaxies in the rest-frame U-V versus V-J diagram (i.e., the UVJ diagram) using a sample at 0.6<z<0.9 that reaches a low stellar mass limit (log…

The two currently largest all-sky photometric datasets, WISE and SuperCOSMOS, were cross-matched by Bilicki et al. (2016) (B16) to construct a novel photometric redshift catalogue on 70% of the sky. Galaxies were therein separated from…

Generative Adversarial Networks (GANs) are powerful generative models that achieved strong results, mainly in the image domain. However, the training of GANs is not trivial, presenting some challenges tackled by different strategies.…

This paper introduces NN-STNE, a neural network using t-distributed stochastic neighbor embedding (t-SNE) as a hidden layer to reduce input dimensions by mapping long time-series data into shapelet membership probabilities. A Gaussian…

The measurements of the number density of galaxy-galaxy strong lenses can be used to put statistical constraints on the foreground mass distributions. Dusty galaxies uncovered in submillimeter surveys are particularly useful in this regard…

Substantial populations of massive quiescent galaxies at $z\ge3$ challenge our understanding of rapid galaxy growth and quenching over short timescales. In order to piece together this evolutionary puzzle, more statistical samples of these…

Stochastic Neighbor Embedding (SNE) is a manifold learning and dimensionality reduction method with a probabilistic approach. In SNE, every point is consider to be the neighbor of all other points with some probability and this probability…

Galaxy edges or truncations are low-surface-brightness (LSB) features located in the galaxy outskirts that delimit the distance up to where the gas density enables efficient star formation. As such, they could be interpreted as a…

We select and characterise a sample of massive (log(M$_{*}/$M$_{\odot})>10.6$) quiescent galaxies (QGs) at $3<z<5$ in the latest COSMOS2020 catalogue. QGs are selected using a new rest-frame colour selection method, based on their…