相关论文: Machine Learning Classifiers for Intermediate Reds…

In the era of large sky surveys, photometric redshifts (photo-z) represent crucial information for galaxy evolution and cosmology studies. In this work, we propose a new Machine Learning (ML) tool called Galaxy morphoto-Z with neural…

We examine the evolutionary status of luminous, star-forming galaxies in intermediate-redshift clusters by considering their star formation rates and the chemical and ionsiation properties of their interstellar emitting gas. Our sample…

We present a novel way of using neural networks (NN) to estimate the redshift distribution of a galaxy sample. We are able to obtain a probability density function (PDF) for each galaxy using a classification neural network. The method is…

Dwarf AGN serve as the ideal systems for identifying intermediate mass black holes (IMBHs) down to the most elusive regimes ($\sim 10^3 - 10^4 M_{\odot}$). However, the ubiquitously metal-poor nature of dwarf galaxies gives rise to…

The artificial neural network (ANN) is a well-established mathematical technique for data prediction, based on the identification of correlations and pattern recognition in input training sets. We present the application of ANNs to predict…

Radio synchrotron emission originates from both massive star formation and black hole accretion, two processes that drive galaxy evolution. Efficient classification of sources dominated by either process is therefore essential for fully…

Galaxy emission-line fluxes can be analyzed to determine star formation rates (SFR) and ISM ionization. Here, we investigate rest-frame optical emission lines of 71 star-forming galaxies at redshift 0.7 < z < 7 from the Cosmic Evolution…

We present the mid-infrared (MIR) properties of galaxies within a supercluster in the North Ecliptic Pole region at z?0.087 observed with the AKARI satellite. We use data from the AKARI NEP-Wide (5.4 deg2) IR survey and the CLusters of…

This Letter presents a new, remarkably simple diagnostic specifically designed to derive chemical abundances for high redshift galaxies. It uses only the H \alpha, [N II] and [S II] emission lines, which can usually be observed in a single…

This work presents the first results of the Deep IFS View of Nuclei of Galaxies (DIVING$^\mathrm{3D}$) survey. We analysed the nuclear emission-line spectra of a sub-sample we call mini-DIVING$^\mathrm{3D}$, which includes all Southern…

We present rest frame mid-infrared spectroscopy of a sample of 13 submillimeter galaxies, obtained using the Infrared Spectrograph (IRS) on board the Spitzer Space Telescope. The sample includes exclusively bright objects from blank fields…

We present deep {\it Spitzer} mid-infrared spectroscopy, along with 16, 24, 70, and 850\,$\micron$\ photometry, for 22 galaxies located in the Great Observatories Origins Deep Survey-North (GOODS-N) field. The sample spans a redshift range…

Using new J-band VLT-ISAAC and Keck-NIRSPEC spectroscopy, we have measured Halpha and [NII] line fluxes for 0.47<z<0.92 CFRS galaxies which have [OII], Hbeta and [OIII]a line fluxes available from optical spectroscopy, to investigate how…

We present an analysis of the mid-infrared emission lines for a sample of 12 low metallicity Blue Compact Dwarf (BCD) galaxies based on high resolution observations obtained with Infrared Spectrograph on board the {\rm Spitzer} Space…

Taking advantage of the impressive sensitivity of Spitzer to detect massive galaxies at high redshift, we study the mid-infrared environments of powerful, high-redshift radio galaxies at 1.2<z<3. Galaxy cluster member candidates were…

Accurate and reliable photometric redshift determination is one of the key aspects for wide-field photometric surveys. Determination of photometric redshift for galaxies, has been traditionally solved by use of machine-learning and…

This paper is part of large effort within the J-PAS collaboration that aims to classify point-like sources in miniJPAS, which were observed in 60 optical bands over $\sim$ 1 deg$^2$ in the AEGIS field. We developed two algorithms based on…

We present a new machine learning model for estimating photometric redshifts with improved accuracy for galaxies in Pan-STARRS1 data release 1. Depending on the estimation range of redshifts, this model based on neural networks can handle…

Using the extensive multi-wavelength data in the GOODS-North field, we construct and draw comparisons between samples of optical and near-IR selected star-forming and passively evolving galaxies at redshifts 1.4<z<2.6. We find overlap at…

While we already seem to have a general scenario of the evolution of different types of galaxies, a complete and satisfactory understanding of the processes that led to the formation of all the variety of today's galaxy types is still…