arXiv subjects

R. Pedro

Publications and source records attributed to R. Pedro.

9 recordsLinked to original sources

arXiv2024

Study of the Radiation Hardness of the ATLAS Tile Calorimeter Optical Instrumentation with Run 2 data

This paper presents a study of the radiation hardness of the hadronic Tile Calorimeter of the ATLAS experiment in the LHC Run 2. Both the plastic scintillators constituting the detector active media and the wavelength-shifting optical fibres collecting the scintillation light into the photodetector readout are elements susceptible to radiation damage. The dedicated calibration and monitoring systems of the detector (caesium radioactive sources, laser and minimum bias integrator) allow to assess the response of these optical components. Data collected with these systems between 2015 and 2018 are analysed to measure the degradation of the optical instrumentation across Run 2. Moreover, a simulation of the total ionising dose in the calorimeter is employed to study and model the degradation profile as a function of the exposure conditions, both integrated dose and dose rate. The measurement of the relative light output loss in Run 2 is presented and extrapolations to future scenarios are drawn based on current data. The impact of radiation damage on the cell response uniformity is also analysed.

physics.ins-det↗

arXiv2023

Production and optical characterisation of blended Polyethylene Terephthalate (PET)/Polyethylene Naphthalate (PEN) scintillator samples

In Particle and Nuclear Physics research and related applications, organic scintillators provide a cost-effective technology for the detection of ionising radiation. The next generation of experiments in this field is driving fundamental research and development on these materials, demanding improved light yield, radiation hardness, and fast response. Common materials such as PEN and PET have been found to offer scintillation properties competitive to commercial alternatives without the use of dopants. Motivated by their complementarity in terms of light yield, radiation hardness, and response time, there is an increasing interest in investigating PET:PEN mixtures to ascertain whether they exhibit synergistic blending. This paper presents results from the systematic development of samples of PET, PEN, and PET:PEN mixtures with varied mass proportions. The manufacturing technique, involving injection moulding of granule raw material, is detailed. The effects of doping the polymer base substrate with fluorescent dopants are explored. Finally, the emission spectra of the different material compositions and their relative light output are presented.

physics.ins-det↗

arXiv2023

Laser calibration of the ATLAS Tile Calorimeter during LHC Run 2

This article reports the laser calibration of the hadronic Tile Calorimeter of the ATLAS experiment in the LHC Run 2 data campaign. The upgraded Laser II calibration system is described. The system was commissioned during the first LHC Long Shutdown, exhibiting a stability better than 0.8% for the laser light monitoring. The methods employed to derive the detector calibration factors with data from the laser calibration runs are also detailed. These allowed to correct for the response fluctuations of the 9852 photomultiplier tubes of the Tile Calorimeter with a total uncertainty of 0.5% plus a luminosity-dependent sub-dominant term. Finally, we report the regular monitoring and performance studies using laser events in both standalone runs and during proton collisions. These studies include channel timing and quality inspection, and photomultiplier linearity and response dependence on anode current.

physics.ins-det↗

arXiv2021

Deep Learning for the Classification of Quenched Jets

An important aspect of the study of Quark-Gluon Plasma (QGP) in ultra-relativistic collisions of heavy ions is the ability to identify, in experimental data, a subset of the jets that were strongly modified by the interaction with the QGP. In this work, we propose studying deep learning techniques for this purpose. Samples of $Z+$jet events were simulated in vacuum and medium and used to train deep neural networks with the objective of discriminating between medium- and vacuum-like jets. Dedicated Convolutional Neural Networks, Dense Neural Networks and Recurrent Neural Networks were developed and trained, and their performance was studied. Our results show the potential of these techniques for the identification of jet quenching effects induced by the presence of the QGP.

hep-ph↗

arXiv2021

Use of a Generalized Energy Mover's Distance in the Search for Rare Phenomena at Colliders

In this paper, we expand on the previously proposed concept of Energy Mover's Distance. The resulting observables are shown to provide a way of identifying rare processes in proton-proton collider experiments. It is shown that different processes are grouped together differently and that this can contribute to the improvement of experimental analyses. The $t\bar{t}Z$ production at the Large Hadron Collider is used as a benchmark to illustrate the applicability of the method. Furthermore, we study the use of these observables as new features which can be used in the training of Deep Neural Networks.

hep-ph↗

arXiv2020

Finding New Physics without learning about it: Anomaly Detection as a tool for Searches at Colliders

In this paper we propose a new strategy, based on anomaly detection methods, to search for new physics phenomena at colliders independently of the details of such new events. For this purpose, machine learning techniques are trained using Standard Model events, with the corresponding outputs being sensitive to physics beyond it. We explore three novel AD methods in HEP: Isolation Forest, Histogram Based Outlier Detection, and Deep Support Vector Data Description; alongside the most customary Autoencoder. In order to evaluate the sensitivity of the proposed approach, predictions from specific new physics models are considered and compared to those achieved when using fully supervised deep neural networks. A comparison between shallow and deep anomaly detection techniques is also presented. Our results demonstrate the potential of semi-supervised anomaly detection techniques to extensively explore the present and future hadron colliders' data.

hep-ph↗

arXiv2020

Transferability of Deep Learning Models in Searches for New Physics at Colliders

In this work we assess the transferability of deep learning models to detect beyond the standard model signals. For this we trained Deep Neural Networks on three different signal models: $tZ$ production via a flavour changing neutral current, pair-production of vector-like $T$-quarks via standard model gluon fusion and via a heavy gluon decay in a grid of 3 mass points: 1, 1.2 and 1.4 TeV. These networks were trained with $t\bar{t}$, $Z$+jets and dibosons as the main backgrounds. Limits were derived for each signal benchmark using the inference of networks trained on each signal independently, so that we can quantify the degradation of their discriminative power across different signal processes. We determine that the limits are compatible within uncertainties for all networks trained on signals with vector-like $T$-quarks, whether they are produced via heavy gluon decay or standard model gluon fusion. The network trained on flavour changing neutral current signal, while struggling the most on the other signals, still produce reasonable limits. These results indicate that deep learning models are capable of providing sensitivity in the search for new physics even if it manifests itself in models not assumed during training.

hep-ph↗

arXiv2020

Report on the ECFA Early-Career Researchers Debate on the 2020 European Strategy Update for Particle Physics

A group of Early-Career Researchers (ECRs) has been given a mandate from the European Committee for Future Accelerators (ECFA) to debate the topics of the current European Strategy Update (ESU) for Particle Physics and to summarise the outcome in a brief document [1]. A full-day debate with 180 delegates was held at CERN, followed by a survey collecting quantitative input. During the debate, the ECRs discussed future colliders in terms of the physics prospects, their implications for accelerator and detector technology as well as computing and software. The discussion was organised into several topic areas. From these areas two common themes were particularly highlighted by the ECRs: sociological and human aspects; and issues of the environmental impact and sustainability of our research.

hep-ex↗

arXiv2019

Optics robustness of the ATLAS Tile Calorimeter

TileCal, the central hadronic calorimeter of the ATLAS detector is composed of plastic scintillators interleaved by steel plates, and wavelength shifting optical fibres. The optical properties of these components are known to suffer from natural ageing and degrade due to exposure to radiation. The calorimeter was designed for 10 years of LHC operating at the design luminosity of $10^{34}$cm$^{-2}$s$^{-1}$. Irradiation tests of scintillators and fibres have shown that their light yield decrease by about 10% for the maximum dose expected after 10 years of LHC operation. The robustness of the TileCal optics components is evaluated using the calibration systems of the calorimeter: Cs-137 gamma source, laser light, and integrated photomultiplier signals of particles from proton-proton collisions. It is observed that the loss of light yield increases with exposure to radiation as expected. The decrease in the light yield during the years 2015-2017 corresponding to the LHC Run 2 will be reported. The current LHC operation plan foresees a second high luminosity LHC (HL-LHC) phase extending the experiment lifetime for 10 years more. The results obtained in Run 2 indicate that following the light yield response of TileCal is an essential step for predicting the calorimeter performance in future runs. Preliminary studies attempt to extrapolate these measurements to the HL-LHC running conditions.

physics.ins-det↗