Archivio della ricerca - Fondazione Bruno Kessler
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Charged-hadron and identified-hadron (K0S, Λ, Ξ−) yield measurements in photonuclear Pb+Pb and p+Pb collisions at √sNN=5.02TeV with ATLAS
No full textThis paper presents the measurement of charged-hadron and identified-hadron (K0
S, Λ, Ξ−) yields in photonuclear collisions using 1.7nb−1 of √sNN=5.02TeV Pb+Pb data collected in 2018 with the ATLAS detector at the Large Hadron Collider. Candidate photonuclear events are selected using a combination of tracking and calorimeter information, including the zero-degree calorimeter. The yields as a function of transverse momentum and rapidity are measured in these photonuclear collisions as a function of charged-particle multiplicity. These photonuclear results are compared with 0.1nb−1 of √sNN=5.02TeV p+Pb data collected in 2016 by ATLAS using similar charged-particle multiplicity selections. These photonuclear measurements shed light on potential quark-gluon plasma formation in photonuclear collisions via observables sensitive to radial flow, enhanced baryon-to-meson ratios, and strangeness enhancement. The results are also compared with the Monte Carlo dpmjet-iii generator and hydrodynamic calculations to test whether such photonuclear collisions may produce small droplets of quark-gluon plasma that flow collectively
Scan-to-EDTs: Automated Generation of Energy Digital Twins from 3D Point Clouds
Get PDFDigital Twins (DTs) are transforming construction and energy management sectors by integrating 3D surveying, monitoring, Building Performance Simulation (BPS), and Building Energy Simulation (BES) from the earliest design or retrofit stages. Moreover, dynamic thermal simulations further support energy performance assessments by modeling indoor conditions to meet comfort and efficiency targets. However, their reliability depends on accurate, standards-compliant 3D building models, which are costly to create. This research introduces a complete framework for automatically generating energy-focused Digital Twins (EDTs) directly from unstructured point clouds. Combining Deep Learning-based instance detection, Scan-to-BIM techniques, and computational geometry, the method produces simulation-ready models without manual intervention. The resulting EDTs streamline early-stage performance evaluation, enable scenario testing, and enhance decision making for energy-efficient retrofits, advancing smart-building design through predictive simulation
The quest to discover supersymmetry at the ATLAS experiment
No full textThe search for supersymmetry with the ATLAS experiment at the CERN Large Hadron Collider intensified after the discovery of the Higgs boson in 2012. The search programme expanded in both breadth and depth, profiting from the increased integrated luminosity and higher centre-of-mass energy for the collision data collected between 2015 and 2018, and gaining new sensitivity to unexplored areas of supersymmetry parameter space through the use of novel experimental signatures and innovative analysis techniques. This report summarises the supersymmetry searches at ATLAS using up to 140 fb−1 of pp
collisions at √s = 13 TeV, including the limits set on the production of gluinos, squarks, and electroweakinos for scenarios with or without R-parity conservation, and including models where some of the supersymmetric particles are long-lived
ASIX: Single-photon, energy resolved X-ray imaging with 50 μm hexagonal hybrid pixel
No full textThe Analog Spectral Imager for X-rays is a technology demonstrator of a small-pixel Hybrid Pixel Detector (HPD) designed for applications such as X-ray diffraction, synchrotron-based material science, and soft X-ray astrophysics requiring energy-resolved imaging. The ASIX architecture aims at mitigating the adverse effects of charge sharing, typical of small-pixel devices. In contrast to other frame-based photon counters or multi-threshold devices, ASIX employs, along with a 50μm pixel, an ultra-low-noise (<30 e− ENC), fully analog, asynchronous, single-photon readout, targeting 10μm spatial resolution and 350 eV FWHM at 8 keV within the same exposure. In 2025, we began developing a small scale (∼5×5mm2) HPD coupling a 300μm-thick, n-on-p, edgeless silicon sensor with 50μm pixels arranged in a hexagonal pattern to a newly designed 65-nm CMOS readout ASIC, featuring single-photon readout and on-chip analog to digital conversion, with a target rate capability of 108ph/s/cm2. While the baseline for the ASIX R&D sensor is silicon for ≤20 keV operation, the design of the readout ASIC is compatible with High-Z materials sensors, such as cadmium-telluride or gallium-arsenide, for higher energies X-rays imaging, enabling potential extension to biomedical and preclinical research. This paper describes the ASIX imager architecture and reports on the development and testing of two Minimum Viable Products (MVPs), developed by coupling XPOL-III, a readily available 180-nm CMOS readout ASIC, to a 300μm thick silicon sensor with 50μm pixels and to a 750μm thick CdTe sensor with 100μm pixels, respectively. The MVPs achieved estimated energy resolution of 780eV FWHM at 17.5 keV (CdTe), and 620eV FWHM at 9.7keV (silicon) and spatial resolution of 20μm (CdTe) and 7μm (silicon). These results confirm our preliminary models predicting the feasibility of simultaneous high energy and spatial resolution in such a small-pixel devices, thus securing the ASIX specifications. Finally, the paper highlights the technology gaps that ASIX would potentially fill in both terrestrial and space applications
A Simple Nondestructive Radio Frequency Measurement Technique for the Analysis of Liquids
No full textAbstract—Dielectric resonators (DRs) are widely used in microwave and millimeter-wave applications, including antennas and bandpass filters. In this study, we exploit the properties of these resonators in a different way, demonstrating how a common container filled with liquid can be assimilated to a DR. The typical properties of DRs are used to investigate the physical and chemical properties of the content and their evolution, providing a contactless, noninvasive, and nondestructive evaluation (NDE)
of its characteristics. The level of the liquid, its conductivity, and dielectric constant can be easily determined in real time, and used as parameters for in-line monitoring, quality control, and chemical composition assessment. Results are reported using a common wine bottle as a container, but are very easily transferable to other types of liquid containers, including plastic
bottles or larger containers
Optimizing Multi-Camera Mobile Mapping Systems with Pose Graph and Feature-Based Approaches
Get PDFMulti-camera Visual Simultaneous Localization and Mapping (V-SLAM) increases spatial coverage through multi-view image streams, improving localization accuracy and reducing data acquisition time. Despite its speed and generally robustness, V-SLAM often struggles to achieve precise camera poses necessary for accurate 3D reconstruction, especially in complex environments. This study introduces two novel multi-camera optimization methods to enhance pose accuracy, reduce drift, and ensure loop closures. These methods refine multi-camera V-SLAM outputs within existing frameworks and are evaluated in two configurations: (1) multiple independent stereo V-SLAM instances operating on separate camera pairs; and (2) multi-view odometry processing all camera streams simultaneously. The proposed optimizations include (1) a multi-view feature-based optimization that integrates V-SLAM poses with rigid inter-camera constraints and bundle adjustment; and (2) a multi-camera pose graph optimization that fuses multiple trajectories using relative pose constraints and robust noise models. Validation is conducted through two complex 3D surveys using the ATOM-ANT3D multi-camera fisheye mobile mapping system. Results demonstrate survey-grade accuracy comparable to traditional photogrammetry, with reduced computational time, advancing toward near real-time 3D mapping of challenging environments
The TouCAN Codebook Detecting Textual Misunderstanding in Doctor-Patient Communication with the Philosophy of Language Tools
No full textAzimuthal anisotropies of charged particles with high transverse momentum in Pb+Pb collisions at √sNN=5.02 TeV with the ATLAS detector
No full textA measurement is presented of elliptic (v2) and triangular (v3) azimuthal anisotropy coefficients for charged particles produced in Pb+Pb collisions at √sNN=5.02 TeV using a dataset corresponding to an integrated luminosity of 0.44nb−1 collected with the ATLAS detector at the LHC in 2018. The values of v2 and v3 are measured for charged particles over a wide range of transverse momentum (pT), 1–400 GeV, and Pb+Pb collision centrality, 0–60%, using the scalar-product and multiparticle cumulant methods. These methods are sensitive to event-by-event fluctuations and nonflow effects in the measurements of azimuthal anisotropies. Positive values of v2 are observed up to a pT of approximately 100 GeV from both methods across all centrality intervals. Positive values of v3 are observed up to approximately 25 GeV using both methods, though the application of the three-subevent technique to the multiparticle cumulant method leads to significant changes at the highest pT. At high pT (pT⪆10 GeV), charged particles are dominantly from jet fragmentation. These jets, and hence the measurements presented here, are sensitive to the path-length dependence of parton energy loss in the quark-gluon plasma produced in Pb+Pb collisions
TreC_Metha: A Digital Application to Enhance Patient Agency, Therapy Compliance and Quality of Life in Metastatic Breast Cancer Patients
No full textUnlabelled: The prognosis for Hormonal Receptor positive-HER2-negative (HR+ HER2-negative) metastatic breast cancer (mBC) has significantly improved by advances in hormone therapies, targeted drugs, and antibody-drug conjugates (ADCs). Nevertheless, maintaining quality of life (QoL), managing symptoms, and reducing treatment-related toxicity remain essential. Background: eHealth solutions offer new opportunities to enhance patient engagement and well-being through digital tools. This paper aims to delineate the fundamental functionalities and objectives of TreC_Metha, a technologically advanced instrument to provide effective support during all care process of patients diagnosed with HR+HER2-negative mBC able to proactively change its configuration depending on the treatment line or on the intra-line treatment phase the patient undergoes, as set by the healthcare team. Methods: The TreC_Metha platform was developed through a structured, evidence-based four-phase process aimed at scalability, usability, and clinical relevance. The development began with a formal analysis of the metastatic breast cancer (mBC) care pathway using BPMN modeling to map phases, activities, and stakeholders, highlighting differences from early-stage breast cancer. This analysis informed the identification of key points where digital support could enhance care. Patient needs were assessed through a web-based questionnaire (N = 20) and two focus groups (N = 11), enabling a participatory design approach. Based on these insights, the platform's functional and non-functional requirements were defined, leading to the design and implementation of a patient-facing mobile app and a clinical dashboard tailored to mBC-specific needs. Results: Preliminary findings from the web survey focus groups revealed significant gaps in communication and information delivery during the mBC care journey, contributing to patient anxiety and reduced confidence. Participants expressed a preference for digital and printed resources to improve understanding and facilitate interactions with healthcare providers. These insights informed the development of the TreC_Metha platform. The clinical dashboard enables real-time monitoring and decision-making, while the mobile app supports bidirectional communication, therapy adherence, and patient-reported data collection. A system prototype is currently under refinement and will undergo usability testing with a small cohort of users. Following this phase, the pilot study will evaluate the platform's impact on QoL, aiming for a ≥10% improvement in outcome measures and contributing to a more patient-centered care model in the mBC setting. Conclusions: TreC_Metha represents an innovative tool that may enable involvement and active participation in the mBC care process for both a multidisciplinary care team of professionals and the patient, and that can be easily adapted to other cancer types and chronic diseases
From Conflict to Concealment: The Role of Generative AI in Creating a Digital Utopia
Get PDFHuman-machine interaction with large language models (LLMs) is built on an implicit trust in their ability to provide reliable, objective, and neutral information- an assumption that contrasts sharply with human-human interactions, where bias, conflict, and subjectivity naturally arise from embodied perspectives. Because LLMs are disembodied entities, they are often perceived as impartial and free from contradiction. This paper argues that such perceptions reflect a longstanding human aspiration: the utopian ideal of accessing "pure" knowledge—information unmediated by human subjectivity and as close to reality as possible. However, we challenge this assumption by demonstrating that bias and conflict remain structurally embedded within the data that LLMs process, reinterpret and generate. Rather than eliminating ambiguity, LLMs conceal it through a process of complexity reduction and an illusion of truth. Through a transdisciplinary analysis of LLM responses to culturally sensitive prompts, we reveal how ambiguity and conflict are systematically smoothed over in human-machine interactions. By examining empirical cases involving fine-tuning, dataset selection, and trigger-based interactions, we argue that LLMs are deliberately designed to produce responses that align with an idealized notion of ’universal humanity’, a neutral, conflict-free, and harmonious representation of knowledge. This shaping of interactions reinforces a curated, utopian version of reality, influencing how users perceive and engage with AI-generated information