28 citations · 57 across the 13 of their papers we have counts for
14 papers
Detection of kilosecond hard lags in the new pulsating ULX candidate NGC 7456 ULX-1
W. Leone, F. Pintore, C. Pinto +19
Context. Ultraluminous X-ray sources (ULXs) are thought to be powered, in many cases, by super-Eddington accretion onto compact objects. While soft X-ray lags have been detected in…
MDER-DR: Multi-Hop Question Answering with Entity-Centric Summaries
Riccardo Campi, Nicolò Oreste Pinciroli Vago, Mathyas Giudici +2
Retrieval-Augmented Generation (RAG) over Knowledge Graphs (KGs) suffers from the fact that indexing approaches may lose important contextual nuance when text is reduced to triples…
Augmenting representations with scientific papers
Nicolò Oreste Pinciroli Vago, Rocco Di Tella, Carolina Cuesta-Lázaro +3
Astronomers have acquired vast repositories of multimodal data, including images, spectra, and time series, complemented by decades of literature that analyzes astrophysical source…
DeepRed: an architecture for redshift estimation
Alessandro Meroni, Nicolò Oreste Pinciroli Vago, Piero Fraternali
Estimating redshift is a central task in astrophysics, but its measurement is costly and time-consuming. In addition, current image-based methods are often validated on homogeneous…
A Graph-based RAG for Energy Efficiency Question Answering
Riccardo Campi, Nicolò Oreste Pinciroli Vago, Mathyas Giudici +3
In this work, we investigate the use of Large Language Models (LLMs) within a graph-based Retrieval Augmented Generation (RAG) architecture for Energy Efficiency (EE) Question Answ…
Extracting latent representations from X-ray spectra. Classification, regression, and accretion signatures of Chandra sources
Nicolò Oreste Pinciroli Vago, Juan Rafael Martínez-Galarza, Roberta Amato
Spectral signatures are crucial in the era of large X-ray surveys. Automatic machine learning methods have proven useful in this respect, but so far they have not been applied to l…
An ultraviolet burst oscillation candidate from the low-mass X-ray binary EXO 0748-676
A. Miraval Zanon, F. Ambrosino, G. Illiano +15
X-ray burst oscillations are quasi-coherent periodic signals at frequencies close to the neutron star spin frequency. They are observed during thermonuclear Type I X-ray bursts fro…
The hunt for new pulsating ultraluminous X-ray sources: a clustering approach
Nicolò Oreste Pinciroli Vago, Roberta Amato, Matteo Imbrogno +5
The discovery of fast and variable coherent signals in a handful of ultraluminous X-ray sources (ULXs) testifies to the presence of super-Eddington accreting neutron stars, and dra…
A new pulsating neutron star in the Ultraluminous X-ray source NGC 4559 X7?
F. Pintore, C. Pinto, G. Rodriguez-Castillo +17
Ultraluminous X-ray sources (ULX) are extragalactic objects with X-ray luminosities above the Eddington limit for a 10 Msun black hole (BH). ULXs may host super-Eddington accreting…
The restless population of bright X-ray sources of NCG 3621
A. Sacchi, M. Imbrogno, S. E. Motta +9
We report on the multi-year evolution of the population of X-ray sources in the nuclear region of NGC 3621 based on Chandra, XMM-Newton and Swift observations. Among these, two sou…
Time Series Analysis in Compressor-Based Machines: A Survey
Francesca Forbicini, Nicolò Oreste Pinciroli Vago, Piero Fraternali
In both industrial and residential contexts, compressor-based machines, such as refrigerators, HVAC systems, heat pumps and chillers, are essential to fulfil production and consume…
Predicting machine failures from multivariate time series: an industrial case study
Nicolò Oreste Pinciroli Vago, Francesca Forbicini, Piero Fraternali
Non-neural Machine Learning (ML) and Deep Learning (DL) models are often used to predict system failures in the context of industrial maintenance. However, only a few researches jo…
DeepGraviLens: a Multi-Modal Architecture for Classifying Gravitational Lensing Data
Nicolò Oreste Pinciroli Vago, Piero Fraternali
Gravitational lensing is the relativistic effect generated by massive bodies, which bend the space-time surrounding them. It is a deeply investigated topic in astrophysics and allo…
Using Convolutional Neural Networks for the Helicity Classification of Magnetic Fields
Nicolò Oreste Pinciroli Vago, Ibrahim A. Hameed, Michael Kachelriess
The presence of non-zero helicity in intergalactic magnetic fields is a smoking gun for their primordial origin since they have to be generated by processes that break CP invarianc…