4 Contributo in Atti di Convegno (Proceeding)

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Large-scale structural controls on hot spring mineral deposits of geothermal systems (Mt. Amiata, Italy) highlighted by machine learning algorithms?

The cinnabar (±stibnite) deposits of the Mt. Amiata geothermal system and the associated hot springs and gas vents, occur along a N-S directed, narrow longitude region. In this study, we combine a geological and geophysical dataset gathered from the early stages of geothermal exploration of the district with a multivariate statistical analysis carried out by Machine Learning (ML) algorithms to highlight possible correlations between the distribution of the geothermal expressions of Mt. Amiat...

Listenable Maps for Audio Classifiers

Despite the impressive performance of deep learning models across diverse tasks, their complexity poses challenges for interpretation. This challenge is particularly evident for audio signals, where conveying interpretations becomes inherently difficult. To address this issue, we introduce Listenable Maps for Audio Classifiers (L-MAC), a posthoc interpretation method that generates faithful and listenable interpretations. L-MAC utilizes a decoder on top of a pretrained classifier to generate ...

Unsupervised sparse convolutional autoencoder for multi-class change detection in hyperspectral images

Change Detection (CD) approaches for hyperspectral images (HSI) are mainly unsupervised and hierarchically extract the endmembers to determine the multiple change classes but require many parameters to set manually. Recently, HSI CD has been approached with DL methods because of their capacity to learn features of changes automatically, but they require a huge amount of labeled data for weakly or fully supervised training. They mostly perform binary CD only and do not fully exploit the spectr...

Exploring the impact of total ionizing dose on the LAD detectors leakage current

The Large Area Detector (LAD) is an instrument concept for the eXTP and STROBE-X space missions (although for the latter with a different name, HEMA). Based on linear Silicon Drift Detectors (SDD), it performs spectral-timing studies in the 2–30 keV energy range. Due to the harsh particle environment of low Earth orbit, it will be subject to radiation damage in the form of both displacement damage and Total Ionizing Dose (TID). To evaluate the response of the LAD sensor technology to TID, we ...

Improving the eXTP/LAD detector energy resolution with a novel sensor design

The Large Area Detector is built around an array of linear Silicon Drift Detectors read out by dedicated CMOS Application-Specific Integrated Circuits. Strict constraints on power consumption and energy resolution (200 eV FWHM at 6 keV for single-channel events, ≤ 240 eV FWHM overall), impose a tough trade-off. We developed a new sensor architecture to improve the performance following two strategies: firstly by confining the signal charge diffusion during drift to a single channel, and secon...

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