Browsing by Author "Vaccari, M"
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Item Effect of the environment on star formation activity and stellar mass for star-forming galaxies in the COSMOS field(2020-11) Hess, K; Vaccari, M; Randriamampandry, SWe investigate the relationship between the environment and the galaxy main sequence (the relationship between stellar mass and star formation rate), as well as the relationship between the environment and radio luminosity (P1.4 GHz), to shed new light on the effects of the environment on galaxies. We use the VLA-COSMOS 3-GHz catalogue, which consists of star-forming galaxies and quiescent galaxies (active galactic nuclei) in three different environments (field, filament, cluster) and for three different galaxy types (satellite, central, isolated). We perform for the first time a comparative analysis of the distribution of star-forming galaxies with respect to the main-sequence consensus region from the literature, taking into account galaxy environment and using radio observations at 0.1 ≤ z ≤ 1.2. Our results corroborate that the star formation rate is declining with cosmic time, which is consistent with the literature. We find that the slope of the main sequence for different z and M∗ bins is shallower than the main-sequence consensus, with a gradual evolution towards higher redshift bins, irrespective of environment. We see no trends for star formation rate in either environment or galaxy type, given the large errors. In addition, we note that the environment does not seem to be the cause of the flattening of the main sequence at high stellar masses for our sample.Item The evolution of the low-frequency radio AGN population to z 1.5 in the ELAIS N1 field(Advance Access publication, 2020-11-13) Ocran, E; Taylor, A; Vaccari, MWe study the cosmic evolution of radio sources out to z 1.5 using a GMRT 610 MHz survey covering ∼1.86 deg2 of the ELAIS N1 field with a minimum/median rms noise 7.1/19.5 μJy beam−1 and an angular resolution of 6 arcsec. We classify sources as star forming galaxies (SFGs), radio-quiet (RQ) and radio-loud (RL) Active Galactic Nuclei (AGNs) using a combination of multiwavelength diagnostics and find evidence in support of the radio emission in SFGs and RQ AGN arising from star formation, rather than AGN-related processes. At high luminosities, however, both SFGs and RQ AGN display a radio excess when comparing radio and infrared star formation rates. The vast majority of our sample lie along the SFR − M ‘main sequence’ at all redshifts when using infrared star formation rates. We derive the 610 MHz radio luminosity function for the total AGN population, constraining its evolution via continuous models of pure density and pure luminosity evolution with ∝ ( 1 + z) (2.25±0.38)−(0.63±0.35)z and L610 MHz ∝ ( 1 + z) (3.45±0.53)−(0.55±0.29)z , respectively. For our RQ and RL AGN, we find a fairly mild evolution with redshift best fitted by pure luminosity evolution with L610 MHz ∝ ( 1 + z) (2.81±0.43)−(0.57±0.30)z for RQ AGN and L610 MHz ∝ ( 1 + z) (3.58±0.54)−(0.56±0.29)z for RL AGN. The 610 MHz radio AGN population thus comprises two differently evolving populations whose radio emission is mostly SF-driven or AGN-driven, respectively.Item Feature Reduction for the Classification of Bruise Damage to Apple Fruit Using a Contactless FT-NIR Spectroscopy with Machine Learning(MDPI, 202) Isingizwe, F; Hussein, E; Vaccari, M; Umezuruike, LSpectroscopy data are useful for modelling biological systems such as predicting quality parameters of horticultural products. However, using the wide spectrum of wavelengths is not practical in a production setting. Such data are of high dimensional nature and they tend to result in complex models that are not easily understood. Furthermore, collinearity between different wavelengths dictates that some of the data variables are redundant and may even contribute noise. The use of variable selection methods is one efficient way to obtain an optimal model, andthis was the aim of this work. Taking advantage of a non-contact spectrometer, near infrared spectral data in the range of 800–2500 nm were used to classify bruise damage in three apple cultivars, namely ‘Golden Delicious’, ‘Granny Smith’ and ‘Royal Gala’. Six prominent machine learning classification algorithms were employed, and two variable selection methods were used to determine the most relevant wavelengths for the problem of distinguishing between bruised and non-bruised fruit. The selected wavelengths clustered around 900 nm, 1300 nm, 1500 nm and 1900 nm. The best results were achieved using linear regression and support vector machine based on up to 40 wavelengths: these methods reached precision values in the range of 0.79–0.86, which were all comparable (within error bars) to a classifier based on the entire range of frequencies. The results also provided an open-source based framework that is useful towards the development of multi-spectral applications such as rapid grading of apples based on mechanical damage, and it can also be emulated and applied for other types of defects on fresh produce.Item Identification of single spectral lines in large spectroscopic surveys using UMLAUT: an Unsupervised Machine Learning Algorithm based on Unbiased Topology(The Astrophysical Journal Supplement Series, 2021) Vaccari, MThe identification of an emission line is unambiguous when multiple spectral features are clearly visible in the same spectrum.However, in many cases, only one line is detected, making it difficult to correctly determine the redshift.We developed a freely available unsupervised machine-learning algorithm based on unbiased topology (UMLAUT) that can be used in a very wide variety of contexts, including the identification of single emission lines.To this purpose, the algorithm combines different sources of information, such as the apparent magnitude, size and color of the emitting source, and the equivalent width and wavelength of the detected line.In each specific case, the algorithm automatically identifies the most relevant ones (i.e., those able to minimize the dispersion associated with the output parameter).The outputs can be easily integrated into different algorithms, allowing us to combine supervised and unsupervised techniques and increasing the overall accuracy.We tested our software on WISP (WFC3 IR Spectroscopic Parallel) survey data.WISP represents one of the closest existing analogs to the near-IR spectroscopic surveys that are going to be performed by the future Euclid and Roman missions.These missions will investigate the large-scale structure of the universe by surveying a large portion of the extragalactic sky in near-IR slitless spectroscopy, detecting a relevant fraction of single emission lines.In our tests, UMLAUT correctly identifies real lines in 83.2% of the cases. The accuracy is slightly higher (84.4%) when combining our unsupervised approach with a supervised approach we previously developed.Item SCUBA-2 observations of candidate starbursting protoclusters selected by Planck and Herschel-SPIRE(Royal Astronomical Society, 2019) Cheng, T; Clements, D L; Greenslade, J; Cairns, J; Andreani, P; Bremer, M; Conversi, L; Cooray, A; Dannerbauer, H; De Zotti, G; Eales, S; González-Nuevo, J; Ibar, E; Leeuw, L; Ma, J; Michałowski, M J; Nayyeri, H; Riechers, D A; Scott, D; Temi, P; Vaccari, M; Valtchanov, I; van Kampen, E; Wang, LWe present SCUBA-2 850 μm observations of 13 candidate starbursting protoclusters selected using Planck and Herschel data. The cumulative number counts of the 850 μm sources in 9 of 13 of these candidate protoclusters show significant overdensities compared to the field, with the probability <10−2 assuming the sources are randomly distributed in the sky. Using the 250, 350, 500, and 850 μm flux densities, we estimate the photometric redshifts of individual SCUBA-2 sources by fitting spectral energy distribution templates with an MCMC method. The photometric redshift distribution, peaking at 2 < z < 3, is consistent with that of known z > 2 protoclusters and the peak of the cosmic star formation rate density (SFRD). We find that the 850 μm sources in our candidate protoclusters have infrared luminosities of LIR≳1012L⊙ and star formation rates of SFR = (500–1500) M⊙ yr−1. By comparing with results in the literature considering only Herschel photometry, we conclude that our 13 candidate protoclusters can be categorized into four groups: six of them being high-redshift starbursting protoclusters, one being a lower redshift cluster or protocluster, three being protoclusters that contain lensed dusty star-forming galaxies or are rich in 850 μm sources, and three regions without significant Herschel or SCUBA-2 source overdensities. The total SFRs of the candidate protoclusters are found to be comparable or higher than those of known protoclusters, suggesting our sample contains some of the most extreme protocluster population. We infer that cross-matching Planck and Herschel data is a robust method for selecting candidate protoclusters with overdensities of 850 μm sources.