Characterization of the Hamamatsu R11265-103-M64 multi-anode photomultiplier tube

The aim of this paper is to fully characterize the new multi-anode photomultiplier tube R11265-103-M64, produced by Hamamatsu. Its high effective active area (77%), its pixel size, the low dark signal rate and the capability to detect single photon signals make this tube suitable for an application in high energy physics, such as for RICH detectors. Four tubes and two different bias voltage dividers have been tested. The results of a standard characterization of the gain and the anode uniformity, the dark signal rate, the cross-talk and the device behaviour as a function of temperature have been studied. The behaviour of the tube is studied in a longitudinal magnetic field up to 100 Gauss. Shields made of a high permeability material are also investigated. The deterioration of the device performance due to long time operation at intense light exposure is studied. A quantitative analysis of the variation of the gain and the dark signals rate due to the aging is described.Comment: 22 page

The running of the electromagnetic coupling alpha in small-angle Bhabha scattering

A method to determine the running of alpha from a measurement of small-angle Bhabha scattering is proposed and worked out. The method is suited to high statistics experiments at e+e- colliders, which are equipped with luminometers in the appropriate angular region. A new simulation code predicting small-angle Bhabha scattering is also presentedComment: 15 pages, 3 Postscript figure

Gastrointestinal neuroendocrine neoplasms (GI-NENs): hot topics in morphological, functional, and prognostic imaging

Neuroendocrine neoplasms (NENs) are heterogeneous tumours with a common phenotype descended from the diffuse endocrine system. NENs are found nearly anywhere in the body but the most frequent location is the gastrointestinal tract. Gastrointestinal neuroendocrine neoplasms (GI-NENs) are rather uncommon, representing around 2% of all gastrointestinal tumours and 20–30% of all primary neoplasms of the small bowel. GI-NENs have various clinical manifestations due to the different substances they can produce; some of these tumours appear to be associated with familial syndromes, such as multiple endocrine neoplasm and neurofibromatosis type 1. The current WHO classification (2019) divides NENs into three major categories: well-differentiated NENs, poorly differentiated NENs, and mixed neuroendocrine-non-neuroendocrine neoplasms. The diagnosis, localization, and staging of GI-NENs include morphology and functional imaging, above all contrast-enhanced computed tomography (CECT), and in the field of nuclear medicine imaging, a key role is played by (68)Ga-labelled-somatostatin analogues ((68)Ga-DOTA-peptides) positron emission tomography/computed tomography (PET/TC). In this review of recent literature, we described the objectives of morphological/functional imaging and potential future possibilities of prognostic imaging in the assessment of GI-NENs

Development of machine learning models to prognosticate chronic shunt-dependent hydrocephalus after aneurysmal subarachnoid hemorrhage

Background: Shunt-dependent hydrocephalus significantly complicates subarachnoid hemorrhage (SAH), and reliable prognosis methods have been sought in recent years to reduce morbidity and costs associated with delayed treatment or neglected onset. Machine learning (ML) defines modern data analysis techniques allowing accurate subject-based risk stratifications. We aimed at developing and testing different ML models to predict shunt-dependent hydrocephalus after aneurysmal SAH. Methods: We consulted electronic records of patients with aneurysmal SAH treated at our institution between January 2013 and March 2019. We selected variables for the models according to the results of the previous works on this topic. We trained and tested four ML algorithms on three datasets: one containing binary variables, one considering variables associated with shunt-dependency after an explorative analysis, and one including all variables. For each model, we calculated AUROC, specificity, sensitivity, accuracy, PPV, and also, on the validation set, the NPV and the Matthews correlation coefficient (ϕ). Results: Three hundred eighty-six patients were included. Fifty patients (12.9%) developed shunt-dependency after a mean follow-up of 19.7 (± 12.6) months. Complete information was retrieved for 32 variables, used to train the models. The best models were selected based on the performances on the validation set and were achieved with a distributed random forest model considering 21 variables, with a ϕ = 0.59, AUC = 0.88; sensitivity and specificity of 0.73 (C.I.: 0.39–0.94) and 0.92 (C.I.: 0.84–0.97), respectively; PPV = 0.59 (0.38–0.77); and NPV = 0.96 (0.90–0.98). Accuracy was 0.90 (0.82–0.95). Conclusions: Machine learning prognostic models allow accurate predictions with a large number of variables and a more subject-oriented prognosis. We identified a single best distributed random forest model, with an excellent prognostic capacity (ϕ = 0.58), which could be especially helpful in identifying low-risk patients for shunt-dependency

Special Article -Tools for Experiment and Theory Refractive index dispersion law of silica aerogel

Abstract. This paper presents measurements of the refractive index of a hygroscopic silica aerogel block at several wavelengths. The measurements, performed with a monochromator, have been compared with different parameterisations for n(λ), in order to determine the best chromaticity law for the aerogel. This is an important input for design and operation of RICH detectors with silica aerogel radiator

K2 results for "young" α\alpha-rich stars in the Galaxy

The origin of apparently young $\alpha$-rich stars in the Galaxy is still a matter of debate in Galactic archaeology, whether they are genuinely young or might be products of binary evolution and merger/mass accretion. We aim to shed light on the nature of young $\alpha$-rich stars in the Milky Way by studying their distribution in the Galaxy thanks to an unprecedented sample of giant stars that cover different Galactic regions and have precise asteroseismic ages, chemical, and kinematic measurements. We analyze a new sample of $\sim$ 6000 stars with precise ages coming from asteroseismology. Our sample combines the global asteroseismic parameters measured from light curves obtained by the K2 mission with stellar parameters and chemical abundances obtained from APOGEE DR17 and GALAH DR3, then cross-matched with Gaia DR3. We define our sample of young $\alpha$-rich stars and study their chemical, kinematic, and age properties. We investigate young $\alpha$-rich stars in different parts of the Galaxy and we find that the fraction of young $\alpha$-rich stars remains constant with respect to the number of high-$\alpha$ stars at $\sim$ 10%. Furthermore, young $\alpha$-rich stars have kinematic and chemical properties similar to high-$\alpha$ stars, except for [C/N] ratios. This suggests that these stars are not genuinely young, but products of binary evolution and merger/mass accretion. Under that assumption, we find the fraction of these stars in the field to be similar to that found recently in clusters. This fact suggests that $\sim$ 10% of the low-$\alpha$ field stars could also have their ages underestimated by asteroseismology. This should be kept in mind when using asteroseismic ages to interpret results in Galactic archaeology.Comment: 13 pages, 7 figures. Accepted by A&

K2 results for “young” α-rich stars in the Galaxy

Context. The origin of apparently young α-rich stars in the Galaxy is still a matter of debate in Galactic archaeology, whether they are genuinely young or might be products of binary evolution, and mergers or mass accretion.Aims: Our aim is to shed light on the nature of young α-rich stars in the Milky Way by studying their distribution in the Galaxy thanks to an unprecedented sample of giant stars that cover different Galactic regions and have precise asteroseismic ages, and chemical and kinematic measurements.Methods: We analyzed a new sample of ∼6000 stars with precise ages coming from asteroseismology. Our sample combines the global asteroseismic parameters measured from light curves obtained by the K2 mission with stellar parameters and chemical abundances obtained from APOGEE DR17 and GALAH DR3, then cross-matched with Gaia DR3. We define our sample of young α-rich stars and study their chemical, kinematic, and age properties.Results: We investigated young α-rich stars in different parts of the Galaxy and we find that the fraction of young α-rich stars remains constant with respect to the number of high-α stars at ∼10%. Furthermore, young α-rich stars have kinematic and chemical properties similar to high-α stars, except for [C/N] ratios.Conclusions: Thanks to our new K2 sample, we conclude that young α-rich stars have similar occurrence rates in different parts of the Galaxy, and that they share properties similar to the normal high-α population, except for [C/N] ratios. This suggests that these stars are not genuinely young, but are products of binary evolution, and mergers or mass accretion. Under that assumption, we find the fraction of these stars in the field to be similar to that found recently in clusters. This suggests that ∼10% of the low-α field stars could also have their ages underestimated by asteroseismology. This should be kept in mind when using asteroseismic ages to interpret results in Galactic archaeology