716 research outputs found
ATLAS New Heavy Quark Searches
No full textThis poster reviews the motivations for searching a fourth generation of fermions, and describes different strategies to detect new heavy quarks with the ATLAS experiment
Recherche de nouveaux quarks lourds avec l'expérience ATLAS a u LHC - Mise en oeuvre d'algorithmes d'identification de jets issus de quarks b
No full textThe hypothesis of a fourth generation of fermions - the matter particles described in the Standard Model (SM) of particle physics - is one of the simplest model of new physics still not excluded and accessible at the start of the Large Hadron Collider (LHC) - the world most powerful hadron collider since 2009. We search for the pair production of up-type t' quarks decaying to a W boson and a b-quark. The search is optimized for the high quark mass regime, for which the production can be distinguished from the top background by exploiting kinematic features of the decay products arising from the proton-proton collisions occurring at the center of the ATLAS detector. We present a novel search strategy reconstructing explicitly very high-pT W bosons from their collimated decay products. The analysis benefits from the commissioning of algorithms intended to identify jets stemming from the fragmentation of b-quarks. These algorithms are based on the precise reconstruction of the trajectory of charged particles, vertices of primary interaction and secondary vertices in jets. The b-tagging ability allows for ATLAS to improve the (re)discovery of the SM, and the sensibility to new physics. It will hence play an important role in the future of the LHC, the reason why we study the expected performance with an upgrade of the ATLAS pixel detector, called IBL and currently under construction. Our search of t' quark, using 4.7 fb^−1 of the 7 TeV data collected in 2011, has resulted in the world most stringent limit, excluding t' masses below 656 GeV, with also an interpretation in the framework of vector-like quarks
Commissioning of high-performance b-tagging algorithms in pp collisions at sqrt(s)=7 TeV with the ATLAS experiment
No full textThe ability to identify jets containing b-hadrons is important for the high-pT physics program of a general-purpose experiment at the LHC such as ATLAS. Two robust b-tagging algorithms taking advantage of the impact parameter of tracks or reconstructing secondary vertices have been swiftly commissioned and used for several analyses of the 2010 data: bottom and top quark production cross-section measurements, searches for supersymmetry and new physics, etc. Building on this success, several more advanced b-tagging algorithms are commissioned with the 2011 data. All these algorithms are based on a likelihood ratio formalism to compare the signal (b-jet) or background (light or in some cases charm jet) hypotheses, using Monte Carlo predicates. The accuracy with which the simulation reproduces the experimental data is therefore critical and is explained in details, as well as the expected improvement in performance brought in by these new algorithms and some first results about the measurement in data of their performance
Commissioning of Advanced b-Tagging Algorithms in pp collisions at √s =7 TeV with the Atlas Experiment
No full textInternational audienceThe ability to identify jets containing b-hadrons is important for the high-pT physics program of a general-purpose experiment at the LHC such as ATLAS. Two robust b-tagging algorithms, JetProb and SV0, taking advantage of the impact parameter of tracks or reconstructing secondary vertices have been swiftly commissioned and used for several analyses of the 2010 data: bottom and top quark production cross-section measurements, searches for supersymmetry etc. Building on this success, several more advanced b-tagging algorithms are commissioned using 330 pb−1 of the 2011 data. All these algorithms are based on a likelihood ratio formalism to separate the signal (b-jet) from the background (light or in some cases charm jet) using input distributions from simulated events. The accuracy with which the simulation reproduces the experimental data is detailed, as well as the expected improvement in performance achieved with these new tagging algorithm
Other Exotica Searches in ATLAS
No full textPresented at the 2011 Hadron Collider Physics symposium (HCP-2011), Paris, France, November 14-18 2011, 4 pages, 9 figuresThanks to the outstanding performance of the Large Hadron Collider (LHC) that delivered more than 2 fb1 of proton-proton collision data at center-of-mass energy of 7 TeV, the ATLAS experiment has been able to explore a wide range of exotic models trying to address the questions unanswered by the Standard Model of particle physics. Searches for leptoquarks, new heavy quarks, vector-like quarks, black holes, hidden valley and contact interactions are reviewed in these proceedings
ATLAS Detector Upgrade Plans
No full textThe ATLAS detector has had a very successful start with many results produced already in 2010. The LHC will continue increasing luminosity in a series of runs interspersed with long shut-downs for installation of injector, LHC and experiment upgrades. The higher integrated luminosity made available - the target is 3000 fb-1 - will open access to many new physics goals. This poster summarises these goals and ATLAS upgrade plans from now until the High-Luminosity LHC project around 2020
Commissioning of the ATLAS Advanced b-Tagging Algorithms
No full textThe ability to identify jets containing b-hadrons is important for the high-pT physics program of a general-purpose experiment at the LHC such as ATLAS. Two robust b-tagging algorithms taking advantage of the impact parameter of tracks or reconstructing secondary vertices have been swiftly commissioned and used for several analyses of the 2010 data: bottom and top quark production cross-section measurements, searches for supersymmetry and new physics, etc. Building on this success, several more advanced b-tagging algorithms are commissioned with the 2011 data. All these algorithms are based on a likelihood ratio formalism to compare the signal (b-jet) or background (light or in some cases charm jet) hypotheses, using Monte Carlo predicates. The accuracy with which the simulation reproduces the experimental data is therefore critical and is explained in details, as well as the expected improvement in performance brought in by these new algorithms and some first results about the measurement in data of their performance
Recherche de Nouvelle Physique avec ATLAS au LHC grace a l'identification des jets de saveur b
No full text
Feedback Neural Network Based Orbital Trajectory Prediction
No full textIn recent years, the number of satellites and debris in space has dangerously increased. For
this reason, it is indispensable that tracking and orbit prediction of these objects is performed
with the highest level of accuracy. Currently, orbit prediction depends on mathematical models that describe the physics behind the movement of a certain object in space. However, at
times, these models can limit the accuracy of the orbit prediction for being characterized by
a high degree of complexity and nonlinearity. On another note, the application of Machine
Learning to the space sector has been increasing rapidly, being of interest to investigate its
applicability in the field of orbit prediction. In the present dissertation, a Long ShortTerm
Memory (LSTM) neural network is designed and investigated. The obtained results are subsequently compared with the results obtained from an Extended Kalman Filter (EKF). With
data from a twoline element (TLE) file belonging to the satellite STARLINK1028, its orbit was propagated for 48h, producing 17281 state vectors that are utilized for training the
neural network. A second data set was generated, where Gaussian noise with a distribution
N(0, 100) was added. The purpose of this noisy data set is to represent the presence of errors caused by measurements and assess the robustness of the models. A neural network
was developed using the Python language and the Tensorflow and Keras libraries, following
a Multiple Inputs Single Output (MISO) approach. To test if the performance of the neural
network increases the more data is available for training, three case studies were developed,
where case studies A, B and C use 41.7%, 83.3% and 100% of the data set, respectively. The
models were validated using a pragmatic validation and the more common validation, where
it is shown that there are no signs of overfitting or underfitting. Results demonstrate that
the models are robust when faced with noisy data and their performance increases with the
size of the training set. However, despite the the neural network having been validated and
exhibits low prediction errors, the Kalman filter achieved a better performance.Nos últimos anos, o número de satélites e lixo espacial tem aumentado perigosamente. Com
isto, é indispensável que a localização e previsão de órbita destes objetos seja feita com o
maior nível de precisão. Atualmente, a previsão de órbitas depende de modelos matemáticos que descrevem a física por detrás do movimento de certo objecto no espaço. Contudo,
por vezes, estes modelos podem limitar a precisão da previsão de órbita por serem caracterizados por um alto grau de complexidade e não linearidade. Por outro lado, a aplicação de
Machine Learning no setor espacial tem vindo a aumentar rapidamente, sendo de interesse
investigar a sua aplicabilidade na área de previsão de órbitas. Na presente dissertação, uma
rede neuronal Long ShortTerm Memory (LSTM) é projetada e investigada. Os resultados
obtidos são posteriormente comparados com os resultados obtidos por um filtro de Kalman
Extendido (EKF). Com recurso a dados provenientes de um ficheiro twoline element (TLE)
referente ao satélite STARLINK1028, a órbita deste foi propagada durante 48h, produzindo
17281 vetores de estado que são utilizados para treinar a rede neuronal. Um segundo data set
foi gerado, onde ruído gaussiano com uma distribuição N(0, 100)foi adicionado. O propósito
deste data set ruidoso é de retratar a presença de erros causados pelas medições e avaliar a
robustez dos modelos. A rede neuronal foi desenvolvida com recurso à linguagem Python e
às bibliotecas Tensorflow e Keras, tendo sido tomada uma abordagem Multiple Inputs Single Output (MISO). De forma a testar se a performance da rede neuronal aumenta consoante
o aumento de dados disponíveis para treino, 3 casos de estudo foram criados, onde os casos de estudo A, B e C usam 41.7%, 83.3% e 100% do data set, respetivamente. Os modelos
foram validados utilizando uma validação pragmática e a validação mais comum, onde se
demonstra que não há sinais de overfitting ou underfitting. Resultados demonstram que os
modelos são robustos face a dados ruidosos e a performance destes aumenta com o tamanho
do training set. Contudo, apesar da rede neuronal ter sido validada e possuir baixos erros de
previsão, o filtro de Kalman atingiu uma melhor performance
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