The Mu2e Crystal Calorimeter: An Overview

The Mu2e experiment at Fermilab will search for the standard model-forbidden, charged lepton flavour-violating conversion of a negative muon into an electron in the field of an aluminium nucleus. The distinctive signal signature is represented by a mono-energetic electron with an energy near the muon's rest mass. The experiment aims to improve the current single-event sensitivity by four orders of magnitude by means of a high-intensity pulsed muon beam and a high-precision tracking system. The electromagnetic calorimeter complements the tracker by providing high rejection power in muon to electron identification and a seed for track reconstruction while working in vacuum in presence of a 1 T axial magnetic field and in a harsh radiation environment. For 100 MeV electrons, the calorimeter should achieve: (a) a time resolution better than 0.5 ns, (b) an energy resolution <10%, and (c) a position resolution of 1 cm. The calorimeter design consists of two disks, each loaded with 674 undoped CsI crystals read out by two large-area arrays of UV-extended SiPMs and custom analogue and digital electronics. We describe here the status of construction for all calorimeter components and the performance measurements conducted on the large-sized prototype with electron beams and minimum ionizing particles at a cosmic ray test stand. A discussion of the calorimeter's engineering aspects and the on-going assembly is also reported

Mu2e Crystal Calorimeter Readout Electronics: Design and Characterisation

The Mu2e experiment at Fermi National Accelerator Laboratory will search for the charged-lepton flavour-violating neutrinoless conversion of negative muons into electrons in the Coulomb field of an Al nucleus. The conversion electron with a monoenergetic 104.967 MeV signature will be identified by a complementary measurement carried out by a high-resolution tracker and an electromagnetic calorimeter, improving by four orders of magnitude the current single-event sensitivity. The calorimeter—composed of 1348 pure CsI crystals arranged in two annular disks—has a high granularity, 10% energy resolution and 500 ps timing resolution for 100 MeV electrons. The readout, based on large-area UV-extended SiPMs, features a fully custom readout chain, from the analogue front-end electronics to the digitisation boards. The readout electronics design was validated for operation in vacuum and under magnetic fields. An extensive radiation hardness certification campaign certified the FEE design for doses up to 100 krad and 1012 n1MeVeq/cm2 and for single-event effects. A final vertical slice test on the final readout chain was carried out with cosmic rays on a large-scale calorimeter prototype

Colorectal cancer after bariatric surgery (Cric-Abs 2020): Sicob (Italian society of obesity surgery) endorsed national survey

Background The published colorectal cancer (CRC) outcomes after bariatric surgery (BS) are conflicting, with some anecdotal studies reporting increased risks. The present nationwide survey CRIC-ABS 2020 (Colo-Rectal Cancer Incidence-After Bariatric Surgery-2020), endorsed by the Italian Society of Obesity Surgery (SICOB), aims to report its incidence in Italy after BS, comparing the two commonest laparoscopic procedures-Sleeve Gastrectomy (SG) and Roux-en-Y gastric bypass (GBP). Methods Two online questionnaires-first having 11 questions on SG/GBP frequency with a follow-up of 5-10 years, and the second containing 15 questions on CRC incidence and management, were administered to 53 referral bariatric, high volume centers. A standardized incidence ratio (SIR-a ratio of the observed number of cases to the expected number) with 95% confidence intervals (CI) was calculated along with CRC incidence risk computation for baseline characteristics. Results Data for 20,571 patients from 34 (63%) centers between 2010 and 2015 were collected, of which 14,431 had SG (70%) and 6140 GBP (30%). 22 patients (0.10%, mean age = 53 +/- 12 years, 13 males), SG: 12 and GBP: 10, developed CRC after 4.3 +/- 2.3 years. Overall incidence was higher among males for both groups (SG: 0.15% vs 0.05%; GBP: 0.35% vs 0.09%) and the GBP cohort having slightly older patients. The right colon was most affected (n = 13) and SIR categorized/sex had fewer values &lt; 1, except for GBP males (SIR = 1.07). Conclusion Low CRC incidence after BS at 10 years (0.10%), and no difference between procedures was seen, suggesting that BS does not trigger the neoplasm development

Notulae to the Italian alien vascular flora: 11

In this contribution, new data concerning the distribution of vascular flora alien to Italy are presented. It includes new records, confirmations, exclusions, and status changes for Italy or for Italian administrative regions. Nomenclatural and distribution updates published elsewhere are provided as Suppl. material 1

The MEG detector for μ+→e+γ decay search

The MEG (Mu to Electron Gamma) experiment has been running at the Paul Scherrer Institut (PSI), Switzerland since 2008 to search for the decay mu(+) -> e(+)gamma by using one of the most intense continuous mu(+) beams in the world. This paper presents the MEG components: the positron spectrometer, including a thin target, a superconducting magnet, a set of drift chambers for measuring the muon decay vertex and the positron momentum, a timing counter for measuring the positron time, and a liquid xenon detector for measuring the photon energy, position and time. The trigger system, the read-out electronics and the data acquisition system are also presented in detail. The paper is completed with a description of the equipment and techniques developed for the calibration in time and energy and the simulation of the whole apparatus

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

CMS physics technical design report : Addendum on high density QCD with heavy ions

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