Michele Delli Veneri

INFN - Istituto Nazionale di Fisica Nucleare | INFN · Naples

The Euclid mission is expected to image millions of galaxies at high resolution, providing an extensive dataset with which to study galaxy evolution. Because galaxy morphology is both a fundamental parameter and one that is hard to determine for large samples, we investigate the application of deep learning in predicting the detailed morphologies o...

Deep learning has revolutionized the field of hyperspectral image (HSI) analysis, enabling the extraction of complex and hierarchical features. While convolutional neural networks (CNNs) have been the backbone of HSI classification, their limitations in capturing global contextual features have led to the exploration of Vision Transformers (ViTs)....

We performed differential number counts down to 4.25 sigma using ALMA Band 3 calibrator images, which are known for their high dynamic range and susceptibility to various types of contamination. Estimating the fraction of contaminants is an intricate process due to correlated non-Gaussian noise, and it is often compounded by the presence of false p...

This work is focused on a deep learning model–U-Net convolutional neural network–with the purpose of segmenting relevant imagery classes, for detecting mining areas using hyperspectral images of the PRISMA Earth Observation mission, funded by the Italian Space Agency (ASI). To avoid the typical problem of hyperspectral data redundancy and to improv...

The Square Kilometre Array Observatory (SKAO) will explore the radio sky to new depths in order to conduct transformational science. SKAO data products made available to astronomers will be correspondingly large and complex, requiring the application of advanced analysis techniques to extract key science findings. To this end, SKAO is conducting a...

The light curve of Gaia23bab (=SPICY 97589) shows two significant (Δ G > 2 mag) brightening events, one in 2017 and an ongoing event starting in 2022. The source’s quiescent spectral energy distribution indicates an embedded ( A V > 5 mag) pre-main-sequence star, with optical accretion emission and mid-infrared disk emission. This characterization...

The Square Kilometre Array Observatory (SKAO) will explore the radio sky to new depths in order to conduct transformational science. SKAO data products made available to astronomers will be correspondingly large and complex, requiring the application of advanced analysis techniques to extract key science findings. To this end, SKAO is conducting a...

We present a Deep Learning pipeline for the detection of astronomical sources within radiointerferometric simulated data cubes. Our pipeline is constituted by two Deep Learning models: a Convolutional Autoencoder for the detection of sources within the spatial domain of the cube, and a RNN for the denoising and detection of emission peaks in the fr...

We present a Deep-Learning (DL) pipeline developed for the detection and characterization of astronomical sources within simulated Atacama Large Millimeter/submillimeter Array (ALMA) data cubes. The pipeline is composed of six DL models: a Convolutional Autoencoder for source detection within the spatial domain of the integrated data cubes, a Recur...

We present a Deep-Learning (DL) pipeline developed for the detection and characterization of astronomical sources within simulated Atacama Large Millimeter/submillimeter Array (ALMA) data cubes. The pipeline is composed of six DL models: a Convolutional Autoencoder for source detection within the spatial domain of the integrated data cubes, a Recur...

The Atacama Large Millimeter/submillimeter Array with the planned electronic upgrades will deliver an unprecedented amount of deep and high resolution observations. Wider fields of view are possible with the consequential cost of image reconstruction. Alternatives to commonly used applications in image processing have to be sought and tested. Advan...

This work is focused on a deep learning model - U-Net convolutional neural network - with the purpose of segmenting relevant imagery classes, for detecting mining areas using hyperspectral images of the PRISMA Earth Observation mission, funded by the Italian Space Agency (ASI). To avoid the typical problem of hyperspectral data redundancy and to im...

We present a citation pattern analysis between astronomical papers and 13 other disciplines, based on the arXiv database over the past decade (2010–2020). We analyze 12,600 astronomical papers citing over 14,531 unique publications outside astronomy. Two striking patterns are unraveled. First, general relativity recently became the most cited field...

We present a citation pattern analysis between astronomical papers and 13 other disciplines, based on the arXiv database over the past decade ($2010 - 2020$). We analyze 12,600 astronomical papers citing over 14,531 unique publications outside astronomy. Two striking patterns are unraveled. First, general relativity recently became the most cited f...

Traditional supervised classification models aim to approximate the functional mapping between instance attributes and their class labels. These models, however, do not consider the interdependence between instances and global characteristics of data and thus often they lead to poor classification results. In this work, we present a novel hybrid cl...

The Probability Density Function (PDF) provides an estimate of the photometric redshift (zphot) prediction error. It is crucial for current and future sky surveys, characterized by strict requirements on the zphot precision, reliability and completeness. The present work stands on the assumption that properly defined rejection criteria, capable of...

Astrometric detection involves precise measurements of stellar positions, and it is widely regarded as the leading concept presently ready to find Earth-mass planets in temperate orbits around nearby sun-like stars. The TOLIMAN space telescope [39] is a low-cost, agile mission concept dedicated to narrow-angle astrometric monitoring of bright binar...

Objective The objective of this study was to assess the effectiveness and safety of dupilumab in treating elderly patients with atopic dermatitis from baseline to 52 weeks.MethodsA retrospective observational real-life study was conducted in a group of elderly patients with severe atopic dermatitis treated with dupilumab for 52 weeks. Inclusion cri...

Several approaches were proposed to describe the geomorphology of drainage networks and the abiotic/biotic factors determining their morphology. There is an intrinsic complexity of the explicit qualification of the morphological variations in response to various types of control factors and the difficulty of expressing the cause-effect links. Tradi...

Several approaches were proposed to describe the geomorphology of drainage networks and the abiotic/biotic factors determining their morphology. There is an intrinsic complexity of the explicit qualification of the morphological variations in response to various types of control factors and the difficulty of expressing the cause-effect links. Tradi...

The Probability Density Function (PDF) provides an estimate of the photometric redshift (zphot) prediction error. It is crucial for current and future sky surveys, characterized by strict requirements on the zphot precision, reliability and completeness. The present work stands on the assumption that properly defined rejection criteria, capable of...

Astrometric detection involves a precise measurement of stellar positions, and is widely regarded as the leading concept presently ready to find earth-mass planets in temperate orbits around nearby sun-like stars. The TOLIMAN space telescope[39] is a low-cost, agile mission concept dedicated to narrow-angle astrometric monitoring of bright binary s...

Stellar Formation Rates or SFRs are crucial to constrain theories of galaxy formation and evolution. SFRs are usually estimated via spectroscopic observations requiring large amounts of telescope time. We explore an alternative approach based on the photometric estimation of global SFRs for large samples of galaxies, by using methods such as automa...

Global Stellar Formation Rates or SFRs are crucial to constrain theories of galaxy formation and evolution. SFR's are usually estimated via spectroscopic observations which require too much previous telescope time and therefore cannot match the needs of modern precision cosmology. We therefore propose a novel method to estimate SFRs for large sampl...