Search for Dark Matter produced in association with a hypothetical Dark Higgs Boson decaying to W ± W ∓ or ZZ boson pairs in the fully hadronic final state at √s = 13 TeV using 139 fb−1 of pp collisions recorded with the ATLAS Detector

Searches for Dark Matter, one of the biggest unsolved problems of modern physics, at the LHC often comprise the most exotic signatures. At the same time, these pose experimental challenges, but also opportunities: new techniques are developed, which can find useful application also beyond the scope of their original conception. This thesis presents a search for Dark Matter produced in association with an hypothetical Dark Higgs Boson decaying to $VV$, $V=W,Z$, in the fully hadronic final state. The reconstruction of the boosted $V$ bosons is performed with a novel technique, the Track-Assisted Reclustered jets, used in a data analysis for the first time. This is shown to achieve a more robust performance and flexibility with respect to standard methods. The search was performed using 139 fb$^{-1}$ of proton-proton collision data at $\sqrt{s}$ = 13 TeV collected with the ATLAS detector during Run 2. No significant excess is found in the observed data over the Standard Model; the upper limits on the production cross-section of \met+$VV$ in the fully hadronic final state for the Dark Higgs scenario are set at 95\% confidence level

villas on islands cost effective energy refurbishment in mediterranean coastline houses

Abstract This paper aims to underline the variability of constructions in Mediterranean regions, where different climates, architectural techniques and kinds of building uses determine different optimal energy refurbishments of residential buildings placed on the coastline. More in detail, by considering two different construction technologies (i.e., a lightweight house in reinforced concrete and a massive tuff-made villa), two different climates (Greek coast, climate of Athens and Italian coast, climate of Naples), two cost-optimal energy retrofits are presented. The optimized energy retrofit, performed by coupling transient energy simulations and genetic algorithm for generating improved models, have taken into account all levers of energy efficiency, and thus optimization of building envelope (thermal insulation, reflectance, windows and solar screens), active energy systems (daylight control, HVAC systems for the regulation of indoor conditions) and renewable energy sources at the building scale (namely, solar photovoltaic)

The X-Ray Concentration-Virial Mass Relation

We present the concentration (c)-virial mass (M) relation of 39 galaxy systems ranging in mass from individual early-type galaxies up to the most massive galaxy clusters, (0.06-20) x 10^{14} M_sun. We selected for analysis the most relaxed systems possessing the highest quality data currently available in the Chandra and XMM public data archives. A power-law model fitted to the X-ray c-M relation requires at high significance (6.6 sigma) that c decreases with increasing M, which is a general feature of CDM models. The median and scatter of the c-M relation produced by the flat, concordance LCDM model (Omega_m=0.3, sigma_8=0.9) agrees with the X-ray data provided the sample is comprised of the most relaxed, early forming systems, which is consistent with our selection criteria. Holding the rest of the cosmological parameters fixed to those in the concordance model the c-M relation requires 0.76< sigma_8 <1.07 (99% conf.), assuming a 10% upward bias in the concentrations for early forming systems. The tilted, low-sigma_8 model suggested by a new WMAP analysis is rejected at 99.99% confidence, but a model with the same tilt and normalization can be reconciled with the X-ray data by increasing the dark energy equation of state parameter to w ~ -0.8. When imposing the additional constraint of the tight relation between sigma_8 and Omega_m from studies of cluster abundances, the X-ray c-M relation excludes (>99% conf.) both open CDM models and flat CDM models with Omega_m ~1. This result provides novel evidence for a flat, low-Omega_m universe with dark energy using observations only in the local (z << 1) universe. Possible systematic errors in the X-ray mass measurements of a magnitude ~10% suggested by CDM simulations do not change our conclusions.Comment: Accepted for Publication in ApJ; 13 pages, 4 figures; minor clarifications and updates; correlation coefficients corrected in Table 1 (correct values were used in the analysis in previous versions); conclusions unchange

LEMON:LEns MOdelling with Neural networks - I. Automated modelling of strong gravitational lenses with Bayesian Neural Networks

The unprecedented number of gravitational lenses expected from new-generation facilities such as the ESA Euclid telescope and the Vera Rubin Observatory makes it crucial to rethink our classical approach to lens-modelling. In this paper, we present LEMON (Lens Modelling with Neural networks): a new machine-learning algorithm able to analyse hundreds of thousands of gravitational lenses in a reasonable amount of time. The algorithm is based on a Bayesian Neural Network: a new generation of neural networks able to associate a reliable confidence interval to each predicted parameter. We train the algorithm to predict the three main parameters of the singular isothermal ellipsoid model (the Einstein radius and the two components of the ellipticity) by employing two simulated data sets built to resemble the imaging capabilities of the Hubble Space Telescope and the forthcoming Euclid satellite. In this work, we assess the accuracy of the algorithm and the reliability of the estimated uncertainties by applying the network to several simulated data sets of 104 images each. We obtain accuracies comparable to previous studies present in the current literature and an average modelling time of just ∼0.5 s per lens. Finally, we apply the LEMON algorithm to a pilot data set of real lenses observed with HST during the SLACS program, obtaining unbiased estimates of their SIE parameters. The code is publicly available on GitHub (https://github.com/fab-gentile/LEMON).</p

CASCO: Cosmological and AStrophysical parameters from Cosmological simulations and Observations -- I. Constraining physical processes in local star-forming galaxies

We compare the structural properties and dark matter content of star-forming galaxies taken from the CAMELS cosmological simulations to the observed trends derived from the SPARC sample in the stellar mass range $[10^{9}, 10^{11}]\,\textrm{M}_{\odot}$, to provide constraints on the value of cosmological and astrophysical (SN- and AGN-related) parameters. We consider the size-, internal DM fraction-, internal DM mass- and total-stellar mass relations for all the 1065 simulations from the IllustrisTNG, SIMBA and ASTRID suites of CAMELS, and search for the parameters that minimize the $\chi^{2}$ with respect to the observations. For the IllustrisTNG suite, we find the following constraints for the cosmological parameters: $\Omega_{\textrm{m}} = 0.27_{-0.05}^{+0.01}$, $\sigma_{8} = 0.83_{-0.11}^{+0.08}$ and $S_{8} = 0.78_{-0.09}^{+0.03}$, which are consistent within $1\sigma$ with the results from the nine-year WMAP observations. SN feedback-related astrophysical parameters, which describe the departure of outflow wind energy per unit star formation rate and wind velocity from the reference IllustrisTNG simulations, assume the following values: $A_{\textrm{SN1}} = 0.48_{-0.16}^{+0.25}$ and $A_{\textrm{SN2}} = 1.21_{-0.34}^{+0.03}$, respectively. Therefore, simulations with a lower value of outflow wind energy per unit star formation rate with respect to the reference illustrisTNG simulation better reproduce the observations. Simulations based on SIMBA and ASTRID suites predict central dark matter masses substantially larger than those observed in real galaxies, which can be reconciled with observations only by requiring values of $\Omega_{\textrm{m}}$ inconsistent with cosmological constraints for SIMBA, or simulations characterized by unrealistic galaxy mass distributions for ASTRID.Comment: 24 pages, 10 figures, 9 tables. Accepted by MNRAS for publication; Added a reference to sec. 4.

Twenty-Year Follow-Up of Excimer Laser Photorefractive Keratectomy: A Retrospective Observational Study

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Gestational Diabetes and Thyroid Autoimmunity

Background. About 10% of pregnancies are complicated by previously unknown impairment of glucose metabolism, which is defined as gestational diabetes. There are little data available on prevalence of thyroid disorders in patients affected by gestational diabetes, and about their postgestational thyroid function and autoimmunity. We therefore investigated pancreatic and thyroid autoimmunity in gestational diabetic patients and in women who had had a previous gestational diabetic pregnancy. Methods. We investigated 126 pregnant women at the time of a 100-g oral glucose tolerance test: 91 were classified as gestational diabetics, and 35 were negative (controls). We also studied 69 women who had delivered a baby 18–120 months prior to this investigation and who were classified at that time gestational diabetics (38 women) or normally pregnant (31 women; controls). Results. Our data show no differences for both thyroid function and prevalence of autoimmune disorders during pregnancy; however, a significant increase in thyroid autoimmunity was seen in women previously affected by gestational diabetes. This increased prevalence of thyroid autoimmunity was not associated with the development of impaired glucose metabolism after pregnancy. Conclusions. Our data suggest that maternal hyperglycemia is a risk factor for the development of thyroid autoimmunity, a conclusion that should now be confirmed in a larger cohort of patients

Energy Analysis of a Real Industrial Building: Model Development, Calibration via Genetic Algorithm and Monitored Data, Optimization of Photovoltaic Integration

This study performs the energy analysis of a real industrial building, located near Naples (South Italy). The used approach includes three phases: development of the energy model, model calibration based on monitored data and optimization of photovoltaic (PV) integration. Monitored data provide the monthly overall electricity demands of the facility for different years, while the load factors of industrial devices are not available. Thus, the assessment of hourly and daily trends of electricity demands and internal heat loads is not possible from monitored data. In order to solve such issue, the energy model of the building is developed under EnergyPlus environment, taking account of the existing PV system too. A genetic algorithm is run by coupling EnergyPlus and MATLAB® to properly calibrate the hourly load factors of the devices in order to achieve a good agreement between simulated and monitored values of monthly electricity demands. Finally, the installation of further PV panels is investigated to optimize the photovoltaic integration with a view to cost-effectiveness. The robustness of the optimization process is ensured using the calibrated energy model, which provides reliable hourly values of building electricity demand. Results show that the electricity produced by the additional PV panels is around 160 MWh per year, while the payback period is around 10 years demonstrating the financial viability of PV integration

Multi-centre and multi-vendor reproducibility of a standardized protocol for quantitative susceptibility Mapping of the human brain at 3T

: Quantitative Susceptibility Mapping (QSM) is an MRI-based technique allowing the non-invasive quantification of iron content and myelination in the brain. The RIN - Neuroimaging Network established an optimized and harmonized protocol for QSM across ten sites with 3T MRI systems from three different vendors to enable multicentric studies. The assessment of the reproducibility of this protocol is crucial to establish susceptibility as a quantitative biomarker. In this work, we evaluated cross-vendor reproducibility in a group of six traveling brains. Then, we recruited fifty-one volunteers and measured the variability of QSM values in a cohort of healthy subjects scanned at different sites, simulating a multicentric study. Both voxelwise and Region of Interest (ROI)-based analysis on cortical and subcortical gray matter were performed. The traveling brain study yielded high structural similarity (∼0.8) and excellent reproducibility comparing maps acquired on scanners from two different vendors. Depending on the ROI, we reported a quantification error ranging from 0.001 to 0.017&nbsp;ppm for the traveling brains. In the cohort of fifty-one healthy subjects scanned at nine different sites, the ROI-dependent variability of susceptibility values, of the order of 0.005-0.025&nbsp;ppm, was comparable to the result of the traveling brain experiment. The harmonized QSM protocol of the RIN - Neuroimaging Network provides a reliable quantification of susceptibility in both cortical and subcortical gray matter regions and it is ready for multicentric and longitudinal clinical studies in neurological and pychiatric diseases

Strongest atomic physics bounds on Non-Commutative Quantum Gravity Models

Investigations of possible violations of the Pauli Exclusion Principle represent critical tests of the microscopic space-time structure and properties. Space-time non-commutativity provides a class of universality for several Quantum Gravity models. In this context the VIP-2 Lead experiment sets the strongest bounds, searching for Pauli Exclusion Principle violating atomic-transitions in lead, excluding the $\theta$-Poincar\'e Non Commutative Quantum Gravity models far above the Planck scale for non-vanishing $\theta_{\mu \nu}$ ``electric-like'' components, and up to $6.9 \cdot 10^{-2}$ Planck scales if $\theta_{0i} = 0$.Comment: 7 pages, 2 figure