37 research outputs found

    Characterization of a Silicon Drift Detector for High-Resolution Electron Spectroscopy

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    Silicon Drift Detectors, widely employed in high-resolution and high-rate X-ray applications, are considered here with interest also for electron detection. The accurate measurement of the tritium beta decay is the core of the TRISTAN (TRitium Investigation on STerile to Active Neutrino mixing) project. This work presents the characterization of a single-pixel SDD detector with a mono-energetic electron beam obtained from a Scanning Electron Microscope. The suitability of the SDD to detect electrons, in the energy range spanning from few keV to tens of keV, is demonstrated. Experimental measurements reveal a strong effect of the detector's entrance window structure on the observed energy response. A detailed detector model is therefore necessary to reconstruct the spectrum of an unknown beta-decay source

    The Free and Cued Selective Reminding Test: Discriminative Values in a Naturalistic Cohort

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    Background: Neuropsychological assessment is still the basis for the first evaluation of patients with cognitive complaints. The Free and Cued Selective Reminding Test (FCSRT) generates several indices that could have different accuracy in the differential diagnosis between Alzheimer's disease (AD) and other disorders. Objective: In a consecutive series of naturalistic patients, the accuracy of the FCSRT indices in differentiating patients with either mild cognitive impairment (MCI) due to AD or AD dementia from other competing conditions was evaluated. Methods: We evaluated the accuracy of the seven FCSRT indices in differentiating patients with AD from other competing conditions in 434 consecutive outpatients, either at the MCI or at the early dementia stage. We analyzed these data through the receiver operating characteristics curve, and we then generated the odds-ratio map of the two indices with the best discriminative value between pairs of disorders. Results: The immediate and the delayed free total recall, the immediate total recall, and the index of sensitivity of cueing were the most useful indices and allowed to distinguish AD from dementia with Lewy bodies and psychiatric conditions with very high accuracy. Accuracy was instead moderate in distinguishing AD from behavioral variant frontotemporal dementia, vascular cognitive impairment, and other conditions. Conclusion: By using odd-ratio maps and comparison-customized cut-off scores, we confirmed that the FCSRT represents a useful tool to characterize the memory performance of patients with MCI and thus to assist the clinician in the diagnosis process, though with different accuracy values depending on the clinical hypothesis

    The commissioning of the CUORE experiment: the mini-tower run

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    CUORE is a ton-scale experiment approaching the data taking phase in Gran Sasso National Laboratory. Its primary goal is to search for the neutrinoless double-beta decay in 130Te using 988 crystals of tellurim dioxide. The crystals are operated as bolometers at about 10 mK taking advantage of one of the largest dilution cryostat ever built. Concluded in March 2016, the cryostat commissioning consisted in a sequence of cool down runs each one integrating new parts of the apparatus. The last run was performed with the fully configured cryostat and the thermal load at 4 K reached the impressive mass of about 14 tons. During that run the base temperature of 6.3 mK was reached and maintained for more than 70 days. An array of 8 crystals, called mini-tower, was used to check bolometers operation, readout electronics and DAQ. Results will be presented in terms of cooling power, electronic noise, energy resolution and preliminary background measurements

    Results from the Cuore Experiment

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    The Cryogenic Underground Observatory for Rare Events (CUORE) is the first bolometric experiment searching for neutrinoless double beta decay that has been able to reach the 1-ton scale. The detector consists of an array of 988 TeO2 crystals arranged in a cylindrical compact structure of 19 towers, each of them made of 52 crystals. The construction of the experiment was completed in August 2016 and the data taking started in spring 2017 after a period of commissioning and tests. In this work we present the neutrinoless double beta decay results of CUORE from examining a total TeO2 exposure of 86.3kg yr, characterized by an effective energy resolution of 7.7 keV FWHM and a background in the region of interest of 0.014 counts/ (keV kg yr). In this physics run, CUORE placed a lower limit on the decay half- life of neutrinoless double beta decay of 130Te > 1.3.1025 yr (90% C. L.). Moreover, an analysis of the background of the experiment is presented as well as the measurement of the 130Te 2vo3p decay with a resulting half- life of T2 2. [7.9 :- 0.1 (stat.) :- 0.2 (syst.)] x 10(20) yr which is the most precise measurement of the half- life and compatible with previous results

    Assessment of few-hits machine learning classification algorithms for low energy physics in liquid argon detectors

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    The physics potential of massive liquid argon TPCs in the low-energy regime is still to be fully reaped because few-hits events encode information that can hardly be exploited by conventional classification algorithms. Machine learning (ML) techniques give their best in these types of classification problems. In this paper, we evaluate their performance against conventional (deterministic) algorithms. We demonstrate that both Convolutional Neural Networks (CNN) and Transformer-Encoder methods outperform deterministic algorithms in one of the most challenging classification problems of low-energy physics (single- versus double-beta events). We discuss the advantages and pitfalls of Transformer-Encoder methods versus CNN and employ these methods to optimize the detector parameters, with an emphasis on the DUNE Phase II detectors ("Module of Opportunity")

    Assessment of few-hits machine learning classification algorithms for low energy physics in liquid argon detectors

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    The physics potential of massive liquid argon TPCs in the low-energy regime is still to be fully reaped because few-hits events encode information that can hardly be exploited by conventional classification algorithms. Machine learning (ML) techniques give their best in these types of classification problems. In this paper, we evaluate their performance against conventional (deterministic) algorithms. We demonstrate that both Convolutional Neural Networks (CNN) and Transformer-Encoder methods outperform deterministic algorithms in one of the most challenging classification problems of low-energy physics (single- versus double-beta events). We discuss the advantages and pitfalls of Transformer-Encoder methods versus CNN and employ these methods to optimize the detector parameters, with an emphasis on the DUNE Phase II detectors ("Module of Opportunity")

    Right posterior hypometabolism in Pisa syndrome of Parkinson's disease: a key to explain body schema perception deficit?

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    Background: Pisa syndrome (PS) is a trunk postural abnormality in Parkinson's disease (PD). Its pathophysiology is still debated: peripheral and central mechanisms have been hypothesized. Objective: To investigate the role of nigrostriatal dopaminergic deafferentation and of brain metabolism impairment in the onset PS in PD patients. Methods: We retrospectively selected 34 PD patients who developed PS (PS+) and who had previously undergone dopamine transporter (DaT)-SPECT and/or brain F-18 fluorodeoxyglucose PET (FDG-PET). PS + patients were divided considering leaning body side in left ((l)PS+) or right ((r)PS+). DaT-SPECT specific-to-non-displaceable binding ratio (SBR) of striatal regions (BasGan V2 software) were compared between 30 PS+ and 60 PD patients without PS (PS-) as well as between 16 (l)PS+ and 14 (r)PS + patients. Voxel-based analysis (SPM12) was used to compare FDG-PET among 22 PS+, 22 PS- and 42 healthy controls (HC) and between 9 (r)PS+ and 13 (l)PS+. Results: No significant DaT-SPECT SBR differences were found between PS+ and PS- groups or between (r)PD+ and (l)PS + subgroups. Compared to HC, significant hypometabolism in PS+ was found in bilateral temporal-parietal regions, mainly in the right hemisphere, whereas the right Brodmann area 39 (BA39) was relatively hypometabolic both in the (r)PS+ and in the (l)PS+. BA39 and bilateral posterior cingulate cortex were significantly hypometabolic in PS + than in PS- group. Conclusions: As a hub of the network supervising the body schema perception, the involvement of the right posterior hypometabolism supports the hypothesis PS is a result of a somatosensory perceptive deficit rather than a nigrostriatal dopaminergic unbalance

    Dopaminergic and Serotonergic Degeneration and Cortical [18 F]Fluorodeoxyglucose Positron Emission Tomography in De Novo Parkinson's Disease

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    Abstract Background: Degeneration of the nigrostriatal dopaminergic (DA) and the raphe-thalamic serotonergic (SE) systems is among the earliest changes observed in Parkinson's disease (PD). The consequences of those changes on brain metabolism, especially regarding their impact on the cortex, are poorly understood. Objectives: Using multi-tracer molecular imaging, we assessed in a cohort of drug-naive PD patients the association between cortical metabolism and DA and SE system deafferentation of either striatum or thalamus, and we explored whether this association was mediated by either striatum or thalamus metabolism. Methods: We recruited 96 drug-naive PD patients (aged 71.9 \ub1 7.5 years) who underwent [123 I]ioflupane single-photon emission computed tomography ([123 I]FP-CIT-SPECT) and brain [18 F]fluorodeoxyglucose positron emission tomography ([18 F]FDG-PET). We used a voxel-wise analysis of [18 F]FDG-PET images to correlate regional metabolism with striatal DA and thalamic SE innervation as assessed using [123 I]FP-CIT-SPECT. Results: We found that [123 I]FP-CIT specific to nondisplaceable binding ratio (SBR) and glucose metabolism positively correlated with one another in the deep gray matter (thalamus: P = 0.001, r = 0.541; caudate P = 0.001, r = 0.331; putamen P = 0.001, r = 0.423). We then observed a direct correlation between temporoparietal metabolism and caudate DA innervation, as well as a direct correlation between prefrontal metabolism and thalamus SE innervation. The effect of caudate [123 I]FP-CIT SBR values on temporoparietal metabolism was mediated by caudate metabolic values (percentage mediated: 89%, P-value = 0.008), and the effect of thalamus [123 I]FP-CIT SBR values on prefrontal metabolism was fully mediated by thalamus metabolic values (P < 0.001). Conclusions: These data suggest that the impact of deep gray matter monoaminergic deafferentation on cortical function is mediated by striatal and thalamic metabolism in drug-naive PD
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