‘Get out of Traian Square!’ : Roma Stigmatisation as a Mobilising Tool for the Far Right in Timişoara, Romania

Post-communist Central and Eastern Europe has seen far right movements and parties gain considerable ground by drawing on nativist and ethnic claims to call for a return to an imagined past. In Romania, far right groups have been able to capitalise on a sense of injustice while also playing on historically negative feelings towards the Roma community. These patterns have been seen in Timişoara where the group Noua Dreaptă (New Right) has established a foothold over the past decade by emphasising claims that blame Roma for loss of built heritage and corruption in the administration of property restitution. The aims of this paper are to 1) examine the emergence of Noua Dreaptă and its use of Roma stigmatisation, and 2) consider the ways extreme views are normalised by appealing to beliefs and perceptions. The findings of the paper show that pre-existing prejudices can be a powerful force to not just target marginalised communities, but also challenge administrative practices and build organisational support. Focusing at the level of the city, it is possible to identify the way these claims can be more precisely calibrated to draw on concerns that circulate within the community

Track reconstruction and matching between emulsion and silicon pixel detectors for the SHiP-charm experiment

In July 2018 an optimization run for the proposed charm cross section measurement for SHiP was performed at the CERN SPS. A heavy, moving target instrumented with nuclear emulsion films followed by a silicon pixel tracker was installed in front of the Goliath magnet at the H4 proton beam-line. Behind the magnet, scintillating-fibre, drift-tube and RPC detectors were placed. The purpose of this run was to validate the measurement's feasibility, to develop the required analysis tools and fine-tune the detector layout. In this paper, we present the track reconstruction in the pixel tracker and the track matching with the moving emulsion detector. The pixel detector performed as expected and it is shown that, after proper alignment, a vertex matching rate of 87% is achieved.Peer Reviewe

Observation of Collider Muon Neutrinos with the SND@LHC Experiment

We report the direct observation of muon neutrino interactions with the SND@LHC detector at the Large Hadron Collider. A dataset of proton-proton collisions at √ s = 13.6 TeV collected by SND@LHC in 2022 is used, corresponding to an integrated luminosity of 36.8 fb − 1 . The search is based on information from the active electronic components of the SND@LHC detector, which covers the pseudorapidity region of 7.2 < η < 8.4 , inaccessible to the other experiments at the collider. Muon neutrino candidates are identified through their charged-current interaction topology, with a track propagating through the entire length of the muon detector. After selection cuts, 8 ν μ interaction candidate events remain with an estimated background of 0.086 events, yielding a significance of about 7 standard deviations for the observed ν μ signal

SND@LHC: The Scattering and Neutrino Detector at the LHC

SND@LHC is a compact and stand-alone experiment designed to perform measurements with neutrinos produced at the LHC in the pseudo-rapidity region of ${7.2 < \eta < 8.4}$. The experiment is located 480 m downstream of the ATLAS interaction point, in the TI18 tunnel. The detector is composed of a hybrid system based on an 830 kg target made of tungsten plates, interleaved with emulsion and electronic trackers, also acting as an electromagnetic calorimeter, and followed by a hadronic calorimeter and a muon identification system. The detector is able to distinguish interactions of all three neutrino flavours, which allows probing the physics of heavy flavour production at the LHC in the very forward region. This region is of particular interest for future circular colliders and for very high energy astrophysical neutrino experiments. The detector is also able to search for the scattering of Feebly Interacting Particles. In its first phase, the detector will operate throughout LHC Run 3 and collect a total of 250 $\text{fb}^{-1}$

The SHiP experiment at the proposed CERN SPS Beam Dump Facility

The Search for Hidden Particles (SHiP) Collaboration has proposed a general-purpose experimental facility operating in beam-dump mode at the CERN SPS accelerator to search for light, feebly interacting particles. In the baseline configuration, the SHiP experiment incorporates two complementary detectors. The upstream detector is designed for recoil signatures of light dark matter (LDM) scattering and for neutrino physics, in particular with tau neutrinos. It consists of a spectrometer magnet housing a layered detector system with high-density LDM/neutrino target plates, emulsion-film technology and electronic high-precision tracking. The total detector target mass amounts to about eight tonnes. The downstream detector system aims at measuring visible decays of feebly interacting particles to both fully reconstructed final states and to partially reconstructed final states with neutrinos, in a nearly background-free environment. The detector consists of a 50 m long decay volume under vacuum followed by a spectrometer and particle identification system with a rectangular acceptance of 5 m in width and 10 m in height. Using the high-intensity beam of 400 GeV protons, the experiment aims at profiting from the 4 x 10(19) protons per year that are currently unexploited at the SPS, over a period of 5-10 years. This allows probing dark photons, dark scalars and pseudo-scalars, and heavy neutral leptons with GeV-scale masses in the direct searches at sensitivities that largely exceed those of existing and projected experiments. The sensitivity to light dark matter through scattering reaches well below the dark matter relic density limits in the range from a few MeV/c(2) up to 100 MeV-scale masses, and it will be possible to study tau neutrino interactions with unprecedented statistics. This paper describes the SHiP experiment baseline setup and the detector systems, together with performance results from prototypes in test beams, as it was prepared for the 2020 Update of the European Strategy for Particle Physics. The expected detector performance from simulation is summarised at the end

Track reconstruction and matching between emulsion and silicon pixel detectors for the SHiP-charm experiment

In July 2018 an optimization run for the proposed charm cross section measurement for SHiP was performed at the CERN SPS. A heavy, moving target instrumented with nuclear emulsion films followed by a silicon pixel tracker was installed in front of the Goliath magnet at the H4 proton beam-line. Behind the magnet, scintillating-fibre, drift-tube and RPC detectors were placed. The purpose of this run was to validate the measurement's feasibility, to develop the required analysis tools and fine-tune the detector layout. In this paper, we present the track reconstruction in the pixel tracker and the track matching with the moving emulsion detector. The pixel detector performed as expected and it is shown that, after proper alignment, a vertex matching rate of 87% is achieved

Stochastic S-system modeling of gene regulatory network

Microarray gene expression data can provide insights into biological processes at a system-wide level and is commonly used for reverse engineering gene regulatory networks (GRN). Due to the amalgamation of noise from different sources, microarray expression profiles become inherently noisy leading to significant impact on the GRN reconstruction process. Microarray replicates (both biological and technical), generated to increase the reliability of data obtained under noisy conditions, have limited influence in enhancing the accuracy of reconstruction. Therefore, instead of the conventional GRN modeling approaches which are deterministic, stochastic techniques are becoming increasingly necessary for inferring GRN from noisy microarray data. In this paper, we propose a new stochastic GRN model by investigating incorporation of various standard noise measurements in the deterministic S-system model. Experimental evaluations performed for varying sizes of synthetic network, representing different stochastic processes, demonstrate the effect of noise on the accuracy of genetic network modeling and the significance of stochastic modeling for GRN reconstruction. The proposed stochastic model is subsequently applied to infer the regulations among genes in two real life networks: (1) the well-studied IRMA network, a real-life in-vivo synthetic network constructed within the Saccharomycescerevisiae yeast, and (2) the SOS DNA repair network in Escherichiacoli. © 2015, Springer Science+Business Media Dordrecht