Nicola Rosario Napolitano | Sun Yat-Sen University (original) (raw)

Papers by Nicola Rosario Napolitano

Astronomy & Astrophysics

The multi-band photometry of the VOICE imaging data, overlapping with 4.9 deg2 of the Chandra Dee... more The multi-band photometry of the VOICE imaging data, overlapping with 4.9 deg2 of the Chandra Deep Field South (CDFS) area, enables both shape measurement and photometric redshift estimation to be the two essential quantities for weak lensing analysis. The depth of magAB is up to 26.1 (5σ limiting) in r-band. We estimate the excess surface density (ESD; ΔΣ) based on galaxy–galaxy measurements around galaxies at lower redshift (0.10 < zl < 0.35) while we select the background sources as those at higher redshift ranging from 0.3 to 1.5. The foreground galaxies are divided into two major categories according to their colour (blue and red), each of which has been further divided into high- and low-stellar-mass bins. The halo masses of the samples are then estimated by modelling the signals, and the posterior of the parameters are sampled using a Monte Carlo Markov chain process. We compare our results with the existing stellar-to-halo mass relation (SHMR) and find that the blue lo...

Monthly Notices of the Royal Astronomical Society

Next-generation surveys will provide photometric and spectroscopic data of millions to billions o... more Next-generation surveys will provide photometric and spectroscopic data of millions to billions of galaxies with unprecedented precision. This offers a unique chance to improve our understanding of the galaxy evolution and the unresolved nature of dark matter (DM). At galaxy scales, the density distribution of DM is strongly affected by feedback processes, which are difficult to fully account for in classical techniques to derive galaxy masses. We explore the capability of supervised machine learning (ML) algorithms to predict the DM content of galaxies from ‘luminous’ observational-like parameters, using the TNG100 simulation. In particular, we use photometric (magnitudes in different bands), structural (the stellar half-mass radius and three different baryonic masses), and kinematic (1D velocity dispersion and the maximum rotation velocity) parameters to predict the total DM mass, DM half-mass radius, and DM mass inside one and two stellar half-mass radii. We adopt the coefficient...

Monthly Notices of the Royal Astronomical Society

The star formation history (SFH) of galaxies is critical for understanding galaxy evolution. Hydr... more The star formation history (SFH) of galaxies is critical for understanding galaxy evolution. Hydrodynamical simulations enable us to precisely reconstruct the SFH of galaxies and establish a link to the underlying physical processes. In this work, we present a model to describe individual galaxies’ SFHs from three simulations: TheThreeHundred, Illustris-1, and TNG100-1. This model divides the galaxy SFH into two distinct components: the ‘main sequence’ and the ‘variation’. The ‘main sequence’ part is generated by tracing the history of the SFR − M* main sequence of galaxies across time. The ‘variation’ part consists of the scatter around the main sequence, which is reproduced by fractional Brownian motions. We find that: (1) the evolution of the main sequence varies between simulations; (2) fractional Brownian motions can reproduce many features of SFHs; however, discrepancies still exist; and (3) the variations and mass-loss rate are crucial for reconstructing the SFHs of the simul...

The Astrophysical Journal

Next-generation large sky surveys will observe up to billions of galaxies for which basic structu... more Next-generation large sky surveys will observe up to billions of galaxies for which basic structural parameters are needed to study their evolution. This is a challenging task that, for ground-based observations, is complicated by seeing-limited point-spread functions (PSFs). To perform a fast and accurate analysis of galaxy surface brightness, we have developed a family of supervised convolutional neural networks (CNNs) to derive Sérsic profile parameters of galaxies. This work presents the first two Galaxy Light profile CNNs (GaLNets) of this family. The first one is trained using galaxy images only (GaLNet-1), and the second is trained with both galaxy images and the local PSF (GaLNet-2). We have compared the results from GaLNets with structural parameters (total magnitude, effective radius, Sérsic index, etc.) derived from a set of galaxies from the Kilo-Degree Survey by 2DPHOT as a representative of the “standard” PSF-convolved Sérsic fitting tools. The comparison shows that Ga...

Research in Astronomy and Astrophysics

With the advent of new spectroscopic surveys from ground and space, observing up to hundreds of m... more With the advent of new spectroscopic surveys from ground and space, observing up to hundreds of millions of galaxies, spectra classification will become overwhelming for standard analysis techniques. To prepare for this challenge, we introduce a family of deep learning tools to classify features in one-dimensional spectra. As the first application of these Galaxy Spectra neural Networks (GaSNets), we focus on tools specialized in identifying emission lines from strongly lensed star-forming galaxies in the eBOSS spectra. We first discuss the training and testing of these networks and define a threshold probability, P L , of 95% for the high-quality event detection. Then, using a previous set of spectroscopically selected strong lenses from eBOSS, confirmed with the Hubble Space Telescope (HST), we estimate a completeness of ∼80% as the fraction of lenses recovered above the adopted P L . We finally apply the GaSNets to ∼1.3M eBOSS spectra to collect the first list of ∼430 new high-qu...

arXiv (Cornell University), Dec 1, 2007

The Astrophysical Journal, 2021

We present 97 new high-quality strong lensing candidates found in the final ∼350 deg2 that comple... more We present 97 new high-quality strong lensing candidates found in the final ∼350 deg2 that complete the full ∼1350 deg2 area of the Kilo-Degree Survey (KiDS). Together with our previous findings, the final list of high-quality candidates from KiDS sums up to 268 systems. The new sample is assembled using a new convolutional neural network (CNN) classifier applied to r-band (best-seeing) and g, r, and i color-composited images separately. This optimizes the complementarity of the morphology and color information on the identification of strong lensing candidates. We apply the new classifiers to a sample of luminous red galaxies (LRGs) and a sample of bright galaxies (BGs) and select candidates that received a high probability to be a lens from the CNN (P CNN). In particular, setting P CNN > 0.8 for the LRGs, the one-band CNN predicts 1213 candidates, while the three-band classifier yields 1299 candidates, with only ∼30% overlap. For the BGs, in order to minimize the false positive...

The ESO Public Kilo-Degree Survey (KiDS) is an optical wide-field imaging survey carried out with... more The ESO Public Kilo-Degree Survey (KiDS) is an optical wide-field imaging survey carried out with the VLT Survey Telescope and the OmegaCAM camera. KiDS will scan 1,500 deg2 in four optical filters (u, g, r, i). Designed to be a weak lensing survey, it is ideal for galaxy evolution studies, thanks to the high spatial resolution of VST, the excellent seeing and the photometric depth. The surface photometry has provided with structural parameters (e.g. size and Sersic index), aperture and total magnitudes have been used to obtain photometric redshifts from Machine Learning methods and stellar masses/luminositites from stellar population synthesis. Our project aimed at investigating the evolution of the colour and structural properties of galaxies with mass and environment up to redshift \(z \sim 0.5\) and more, to put constraints on galaxy evolution processes, as galaxy mergers.

The Astrophysical Journal, 2020

Symposium - International Astronomical Union, 2003

Planetary nebulae observed in our Milky Way galaxy and in the galaxies of the Local Group do impr... more Planetary nebulae observed in our Milky Way galaxy and in the galaxies of the Local Group do impress us for their morphologies and complexities. But when we look at planetary nebulae outside the Local Group, all this must be forgotten, as they become unresolved and merely points of green light. This paper will review how these green spots of light can be used as probes of the mass distribution and dynamics of elliptical galaxies and nearby clusters.

Symposium - International Astronomical Union, 2004

We have found a correlation between the M / L global gradients and the structural parameters of t... more We have found a correlation between the M / L global gradients and the structural parameters of the luminous components of a sample of 19 early-type galaxies. Such a correlation supports the hypothesis that there is a connection between the dark matter content and the evolution of the baryonic component in such systems.