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  1. Article ; Online: No more glowing in the dark: how deep learning improves exposure date estimation in thermoluminescence dosimetry.

    Mentzel, F / Derugin, E / Jansen, H / Kröninger, K / Nackenhorst, O / Walbersloh, J / Weingarten, J

    Journal of radiological protection : official journal of the Society for Radiological Protection

    2021  Volume 41, Issue 4

    Abstract: The time- or temperature-resolved detector signal from a thermoluminescence dosimeter can reveal additional information about circumstances of an exposure to ionising irradiation. We present studies using deep neural networks to estimate the date of a ... ...

    Abstract The time- or temperature-resolved detector signal from a thermoluminescence dosimeter can reveal additional information about circumstances of an exposure to ionising irradiation. We present studies using deep neural networks to estimate the date of a single irradiation with 12 mSv within a monitoring interval of 42 days from glow curves of novel TL-DOS personal dosimeters developed by the Materialprüfungsamt NRW in cooperation with TU Dortmund University. Using a deep convolutional network, the irradiation date can be predicted from raw time-resolved glow curve data with an uncertainty of roughly 1-2 days on a 68% confidence level without the need for a prior transformation into temperature space and a subsequent glow curve deconvolution (GCD). This corresponds to a significant improvement in prediction accuracy compared to a prior publication, which yielded a prediction uncertainty of 2-4 days using features obtained from a GCD as input to a neural network.
    MeSH term(s) Deep Learning ; Humans ; Thermoluminescent Dosimetry
    Language English
    Publishing date 2021-11-15
    Publishing country England
    Document type Journal Article
    ZDB-ID 639411-5
    ISSN 1361-6498 ; 0952-4746
    ISSN (online) 1361-6498
    ISSN 0952-4746
    DOI 10.1088/1361-6498/ac20ae
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    Zs.A 1886: Show issues Location:
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  2. Article ; Online: Small beams, fast predictions: a comparison of machine learning dose prediction models for proton minibeam therapy.

    Mentzel, F / Kröninger, K / Lerch, M / Nackenhorst, O / Rosenfeld, A / Tsoi, A C / Weingarten, J / Hagenbuchner, M / Guatelli, S

    Medical physics

    2022  

    Abstract: Background: Dose calculations for novel radiotherapy cancer treatments such as proton minibeam radiation therapy is often done using full Monte Carlo (MC) simulations. As MC simulations can be very time consuming for this kind of application, deep ... ...

    Abstract Background: Dose calculations for novel radiotherapy cancer treatments such as proton minibeam radiation therapy is often done using full Monte Carlo (MC) simulations. As MC simulations can be very time consuming for this kind of application, deep learning models have been considered to accelerate dose estimation in cancer patients.
    Purpose: This work systematically evaluates the dose prediction accuracy, speed and generalization performance of three selected state-of-the-art deep learning models for dose prediction applied to the proton minibeam therapy. The strengths and weaknesses of those models are thoroughly investigated, helping other researchers to decide on a viable algorithm for their own application.
    Methods: The following recently published models are compared: first, a 3D U-Net model trained as a regression network, second, a 3D U-Net trained as a generator of a generative adversarial network (GAN) and third, a dose transformer model which interprets the dose prediction as a sequence translation task. These models are trained to emulate the result of MC simulations. The dose depositions of a proton minibeam with a diameter of 800μm and an energy of 20-100 MeV inside a simple head phantom calculated by full Geant4 MC simulations are used as a case study for this comparison. The spatial resolution is 0.5 mm. Special attention is put on the evaluation of the generalization performance of the investigated models.
    Results: Dose predictions with all models are produced in the order of a second on a GPU, the 3D U-Net models being fastest with an average of 130 ms. An investigated 3D U-Net regression model is found to show the strongest performance with overall 61.0
    Conclusions: This paper reveals that (1) all studied deep learning models are significantly faster than non-machine learning approaches predicting the dose in the order of seconds compared to hours for MC, (2) all models provide reasonable accuracy, and (3) the regression-trained 3D U-Net provides the most accurate predictions.
    Language English
    Publishing date 2022-10-30
    Publishing country United States
    Document type Journal Article
    ZDB-ID 188780-4
    ISSN 2473-4209 ; 0094-2405
    ISSN (online) 2473-4209
    ISSN 0094-2405
    DOI 10.1002/mp.16066
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  3. Article ; Online: Deep TL: progress of a machine learning aided personal dose monitoring system.

    Derugin, Evelin / Kröninger, Kevin / Mentzel, Florian / Nackenhorst, Olaf / Walbersloh, Jörg / Weingarten, Jens

    Radiation protection dosimetry

    2023  Volume 199, Issue 8-9, Page(s) 767–774

    Abstract: Personal dosemeters using thermoluminescence detectors can provide information about the irradiation event beyond the pure dose estimation, which is valuable for improving radiation protection measures. In the presented study, the glow curves of the ... ...

    Abstract Personal dosemeters using thermoluminescence detectors can provide information about the irradiation event beyond the pure dose estimation, which is valuable for improving radiation protection measures. In the presented study, the glow curves of the novel TL-DOS dosemeters developed by the Materialprüfungsamt NRW in cooperation with the TU Dortmund University are analysed using deep learning approaches to predict the irradiation date of a single-dose irradiation of 10 mGy within a monitoring interval of 41 d. In contrast of previous work, the glow curves are measured using the current routine read-out process by pre-heating the detectors before the read-out. The irradiation dates are predicted with an accuracy of 2-5 d by the deep learning algorithm. Furthermore, the importance of the input features is evaluated using Shapley values to increase the interpretability of the neural network.
    MeSH term(s) Humans ; Algorithms ; Heating ; Machine Learning ; Neural Networks, Computer ; Radiation Protection
    Language English
    Publishing date 2023-05-16
    Publishing country England
    Document type Journal Article
    ZDB-ID 225912-6
    ISSN 1742-3406 ; 0144-8420
    ISSN (online) 1742-3406
    ISSN 0144-8420
    DOI 10.1093/rpd/ncad078
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 2009: Show issues Location:
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  4. Book ; Online: SR-GAN for SR-gamma

    Erdmann, Johannes / van der Graaf, Aaron / Mausolf, Florian / Nackenhorst, Olaf

    super resolution of photon calorimeter images at collider experiments

    2023  

    Abstract: We study single-image super-resolution algorithms for photons at collider experiments based on generative adversarial networks. We treat the energy depositions of simulated electromagnetic showers of photons and neutral-pion decays in a toy ... ...

    Abstract We study single-image super-resolution algorithms for photons at collider experiments based on generative adversarial networks. We treat the energy depositions of simulated electromagnetic showers of photons and neutral-pion decays in a toy electromagnetic calorimeter as 2D images and we train super-resolution networks to generate images with an artificially increased resolution by a factor of four in each dimension. The generated images are able to reproduce features of the electromagnetic showers that are not obvious from the images at nominal resolution. Using the artificially-enhanced images for the reconstruction of shower-shape variables and of the position of the shower center results in significant improvements. We additionally investigate the utilization of the generated images as a pre-processing step for deep-learning photon-identification algorithms and observe improvements in the case of training samples of small size.

    Comment: 26 pages, 13 figures
    Keywords High Energy Physics - Experiment ; Computer Science - Computer Vision and Pattern Recognition ; Physics - Instrumentation and Detectors
    Publishing date 2023-08-17
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Book ; Online: No more glowing in the dark

    Mentzel, Florian / Derugin, Evelin / Jansen, Hannah / Kröninger, Kevin / Nackenhorst, Olaf / Walbersloh, Jörg / Weingarten, Jens

    How deep learning improves exposure date estimation in thermoluminescence dosimetry

    2021  

    Abstract: The time- or temperature-resolved detector signal from a thermoluminescence dosimeter can reveal additional information about circumstances of an exposure to ionizing irradiation. We present studies using deep neural networks to estimate the date of a ... ...

    Abstract The time- or temperature-resolved detector signal from a thermoluminescence dosimeter can reveal additional information about circumstances of an exposure to ionizing irradiation. We present studies using deep neural networks to estimate the date of a single irradiation with 12 mSv within a monitoring interval of 42 days from glow curves of novel TL-DOS personal dosimeters developed by the Materialpr\"ufungsamt NRW in cooperation with TU Dortmund University. Using a deep convolutional network, the irradiation date can be predicted from raw time-resolved glow curve data with an uncertainty of roughly 1-2 days on a 68% confidence level without the need for a prior transformation into temperature space and a subsequent glow curve deconvolution. This corresponds to a significant improvement in prediction accuracy compared to a prior publication, which yielded a prediction uncertainty of 2-4 days using features obtained from a glow curve deconvolution as input to a neural network.
    Keywords Physics - Medical Physics ; Computer Science - Machine Learning
    Subject code 006
    Publishing date 2021-06-14
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article: Accurate and Fast Deep Learning Dose Prediction for a Preclinical Microbeam Radiation Therapy Study Using Low-Statistics Monte Carlo Simulations.

    Mentzel, Florian / Paino, Jason / Barnes, Micah / Cameron, Matthew / Corde, Stéphanie / Engels, Elette / Kröninger, Kevin / Lerch, Michael / Nackenhorst, Olaf / Rosenfeld, Anatoly / Tehei, Moeava / Tsoi, Ah Chung / Vogel, Sarah / Weingarten, Jens / Hagenbuchner, Markus / Guatelli, Susanna

    Cancers

    2023  Volume 15, Issue 7

    Abstract: Microbeam radiation therapy (MRT) utilizes coplanar synchrotron radiation beamlets and is a proposed treatment approach for several tumor diagnoses that currently have poor clinical treatment outcomes, such as gliosarcomas. Monte Carlo (MC) simulations ... ...

    Abstract Microbeam radiation therapy (MRT) utilizes coplanar synchrotron radiation beamlets and is a proposed treatment approach for several tumor diagnoses that currently have poor clinical treatment outcomes, such as gliosarcomas. Monte Carlo (MC) simulations are one of the most used methods at the Imaging and Medical Beamline, Australian Synchrotron to calculate the dose in MRT preclinical studies. The steep dose gradients associated with the 50μm-wide coplanar beamlets present a significant challenge for precise MC simulation of the dose deposition of an MRT irradiation treatment field in a short time frame. The long computation times inhibit the ability to perform dose optimization in treatment planning or apply online image-adaptive radiotherapy techniques to MRT. Much research has been conducted on fast dose estimation methods for clinically available treatments. However, such methods, including GPU Monte Carlo implementations and machine learning (ML) models, are unavailable for novel and emerging cancer radiotherapy options such as MRT. In this work, the successful application of a fast and accurate ML dose prediction model for a preclinical MRT rodent study is presented for the first time. The ML model predicts the peak doses in the path of the microbeams and the valley doses between them, delivered to the tumor target in rat patients. A CT imaging dataset is used to generate digital phantoms for each patient. Augmented variations of the digital phantoms are used to simulate with Geant4 the energy depositions of an MRT beam inside the phantoms with 15% (high-noise) and 2% (low-noise) statistical uncertainty. The high-noise MC simulation data are used to train the ML model to predict the energy depositions in the digital phantoms. The low-noise MC simulations data are used to test the predictive power of the ML model. The predictions of the ML model show an agreement within 3% with low-noise MC simulations for at least 77.6% of all predicted voxels (at least 95.9% of voxels containing tumor) in the case of the valley dose prediction and for at least 93.9% of all predicted voxels (100.0% of voxels containing tumor) in the case of the peak dose prediction. The successful use of high-noise MC simulations for the training, which are much faster to produce, accelerates the production of the training data of the ML model and encourages transfer of the ML model to different treatment modalities for other future applications in novel radiation cancer therapies.
    Language English
    Publishing date 2023-04-04
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2527080-1
    ISSN 2072-6694
    ISSN 2072-6694
    DOI 10.3390/cancers15072137
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Fast and accurate dose predictions for novel radiotherapy treatments in heterogeneous phantoms using conditional 3D-UNet generative adversarial networks.

    Mentzel, Florian / Kröninger, Kevin / Lerch, Michael / Nackenhorst, Olaf / Paino, Jason / Rosenfeld, Anatoly / Saraswati, Ayu / Tsoi, Ah Chung / Weingarten, Jens / Hagenbuchner, Markus / Guatelli, Susanna

    Medical physics

    2022  Volume 49, Issue 5, Page(s) 3389–3404

    Abstract: Purpose: Novel radiotherapy techniques like synchrotron X-ray microbeam radiation therapy (MRT) require fast dose distribution predictions that are accurate at the sub-mm level, especially close to tissue/bone/air interfaces. Monte Carlo (MC) physics ... ...

    Abstract Purpose: Novel radiotherapy techniques like synchrotron X-ray microbeam radiation therapy (MRT) require fast dose distribution predictions that are accurate at the sub-mm level, especially close to tissue/bone/air interfaces. Monte Carlo (MC) physics simulations are recognized to be one of the most accurate tools to predict the dose delivered in a target tissue but can be very time consuming and therefore prohibitive for treatment planning. Faster dose prediction algorithms are usually developed for clinically deployed treatments only. In this work, we explore a new approach for fast and accurate dose estimations suitable for novel treatments using digital phantoms used in preclinical development and modern machine learning techniques. We develop a generative adversarial network (GAN) model, which is able to emulate the equivalent Geant4 MC simulation with adequate accuracy and use it to predict the radiation dose delivered by a broad synchrotron beam to various phantoms.
    Methods: The energy depositions used for the training of the GAN are obtained using full Geant4 MC simulations of a synchrotron radiation broad beam passing through the phantoms. The energy deposition is scored and predicted in voxel matrices of size 140 × 18 × 18 with a voxel edge length of 1 mm. The GAN model consists of two competing 3D convolutional neural networks, which are conditioned on the photon beam and phantom properties. The generator network has a U-Net structure and is designed to predict the energy depositions of the photon beam inside three phantoms of variable geometry with increasing complexity. The critic network is a relatively simple convolutional network, which is trained to distinguish energy depositions predicted by the generator from the ones obtained with the full MC simulation.
    Results: The energy deposition predictions inside all phantom geometries under investigation show deviations of less than 3% of the maximum deposited energy from the simulation for roughly 99% of the voxels in the field of the beam. Inside the most realistic phantom, a simple pediatric head, the model predictions deviate by less than 1% of the maximal energy deposition from the simulations in more than 96% of the in-field voxels. For all three phantoms, the model generalizes the energy deposition predictions well to phantom geometries, which have not been used for training the model but are interpolations of the training data in multiple dimensions. The computing time for a single prediction is reduced from several hundred hours using Geant4 simulation to less than a second using the GAN model.
    Conclusions: The proposed GAN model predicts dose distributions inside unknown phantoms with only small deviations from the full MC simulation with computations times of less than a second. It demonstrates good interpolation ability to unseen but similar phantom geometries and is flexible enough to be trained on data with different radiation scenarios without the need for optimization of the model parameter. This proof-of-concept encourages to apply and further develop the model for the use in MRT treatment planning, which requires fast and accurate predictions with sub-mm resolutions.
    MeSH term(s) Algorithms ; Child ; Humans ; Monte Carlo Method ; Phantoms, Imaging ; Radiotherapy Dosage ; Radiotherapy Planning, Computer-Assisted/methods
    Language English
    Publishing date 2022-03-03
    Publishing country United States
    Document type Journal Article
    ZDB-ID 188780-4
    ISSN 2473-4209 ; 0094-2405
    ISSN (online) 2473-4209
    ISSN 0094-2405
    DOI 10.1002/mp.15555
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Book ; Online ; Thesis: Search for the Standard Model Higgs boson produced in association with tt and decaying into bb at 8 TeV with the ATLAS detector using the Matrix Element Method

    Nackenhorst, Olaf / Frey, Ariane / Quadt, Arnulf

    2015  

    Abstract: A search for the Standard Model Higgs boson produced in association with a pair of top quarks (ttH) is presented. The analysis uses 20.3 fb−1 of pp collision data at √s = 8 TeV, collected with the ATLAS detector at the Large Hadron Collider during 2012. ... ...

    Author's details vorgelegt von Olaf Nackenhorst
    Abstract A search for the Standard Model Higgs boson produced in association with a pair of top quarks (ttH) is presented. The analysis uses 20.3 fb−1 of pp collision data at √s = 8 TeV, collected with the ATLAS detector at the Large Hadron Collider during 2012. The search is designed for the H → bb decay mode and is performed in the single lepton (electrons or muons) decay channel of the top quark pair. In order to improve the sensitivity of the search, events are categorised according to their jet and b-tagged jet multiplicities into nine different analysis regions. A matrix element method is deve...
    Language English
    Size Online-Ressource
    Document type Book ; Online ; Thesis
    Thesis / German Habilitation thesis --Göttingen, Univ., Diss., 2015
    Database Library catalogue of the German National Library of Science and Technology (TIB), Hannover

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  9. Book ; Online: Accurate and fast deep learning dose prediction for a preclinical microbeam radiation therapy study using low-statistics Monte Carlo simulations

    Mentzel, Florian / Paino, Jason / Barnes, Micah / Cameron, Matthew / Corde, Stéphanie / Engels, Elette / Kröninger, Kevin / Lerch, Michael / Nackenhorst, Olaf / Rosenfeld, Anatoly / Tehei, Moeva / Tsoi, Ah Chung / Vogel, Sarah / Weingarten, Jens / Hagenbuchner, Markus / Guatelli, Susanna

    2022  

    Abstract: Microbeam radiation therapy (MRT) utilizes coplanar synchrotron radiation beamlets and is a proposed treatment approach for several tumour diagnoses that currently have poor clinical treatment outcomes, such as gliosarcomas. Prescription dose estimations ...

    Abstract Microbeam radiation therapy (MRT) utilizes coplanar synchrotron radiation beamlets and is a proposed treatment approach for several tumour diagnoses that currently have poor clinical treatment outcomes, such as gliosarcomas. Prescription dose estimations for treating preclinical gliosarcoma models in MRT studies at the Imaging and Medical Beamline at the Australian Synchrotron currently rely on Monte Carlo (MC) simulations. The steep dose gradients associated with the 50$\,\mu$m wide coplanar beamlets present a significant challenge for precise MC simulation of the MRT irradiation treatment field in a short time frame. Much research has been conducted on fast dose estimation methods for clinically available treatments. However, such methods, including GPU Monte Carlo implementations and machine learning (ML) models, are unavailable for novel and emerging cancer radiation treatment options like MRT. In this work, the successful application of a fast and accurate machine learning dose prediction model in a retrospective preclinical MRT rodent study is presented for the first time. The ML model predicts the peak doses in the path of the microbeams and the valley doses between them, delivered to the gliosarcoma in rodent patients. The predictions of the ML model show excellent agreement with low-noise MC simulations, especially within the investigated tumour volume. This agreement is despite the ML model being deliberately trained with MC-calculated samples exhibiting significantly higher statistical uncertainties. The successful use of high-noise training set data samples, which are much faster to generate, encourages and accelerates the transfer of the ML model to different treatment modalities for other future applications in novel radiation cancer therapies.
    Keywords Physics - Medical Physics
    Subject code 616
    Publishing date 2022-12-11
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Article ; Online: Observation of WZγ Production in pp Collisions at sqrt[s]=13  TeV with the ATLAS Detector.

    Aad, G / Abbott, B / Abeling, K / Abicht, N J / Abidi, S H / Aboulhorma, A / Abramowicz, H / Abreu, H / Abulaiti, Y / Abusleme Hoffman, A C / Acharya, B S / Adam Bourdarios, C / Adamczyk, L / Adamek, L / Addepalli, S V / Addison, M J / Adelman, J / Adiguzel, A / Adye, T /
    Affolder, A A / Afik, Y / Agaras, M N / Agarwala, J / Aggarwal, A / Agheorghiesei, C / Ahmad, A / Ahmadov, F / Ahmed, W S / Ahuja, S / Ai, X / Aielli, G / Ait Tamlihat, M / Aitbenchikh, B / Aizenberg, I / Akbiyik, M / Åkesson, T P A / Akimov, A V / Akiyama, D / Akolkar, N N / Al Khoury, K / Alberghi, G L / Albert, J / Albicocco, P / Albouy, G L / Alderweireldt, S / Aleksa, M / Aleksandrov, I N / Alexa, C / Alexopoulos, T / Alfonsi, A / Alfonsi, F / Algren, M / Alhroob, M / Ali, B / Ali, H M J / Ali, S / Alibocus, S W / Aliev, M / Alimonti, G / Alkakhi, W / Allaire, C / Allbrooke, B M M / Allen, J F / Allendes Flores, C A / Allport, P P / Aloisio, A / Alonso, F / Alpigiani, C / Alvarez Estevez, M / Alvarez Fernandez, A / Alves Cardoso, M / Alviggi, M G / Aly, M / Amaral Coutinho, Y / Ambler, A / Amelung, C / Amerl, M / Ames, C G / Amidei, D / Amor Dos Santos, S P / Amos, K R / Ananiev, V / Anastopoulos, C / Andeen, T / Anders, J K / Andrean, S Y / Andreazza, A / Angelidakis, S / Angerami, A / Anisenkov, A V / Annovi, A / Antel, C / Anthony, M T / Antipov, E / Antonelli, M / Antrim, D J A / Anulli, F / Aoki, M / Aoki, T / Aparisi Pozo, J A / Aparo, M A / Aperio Bella, L / Appelt, C / Apyan, A / Aranzabal, N / Arcangeletti, C / Arce, A T H / Arena, E / Arguin, J-F / Argyropoulos, S / Arling, J-H / Arnaez, O / Arnold, H / Arrubarrena Tame, Z P / Artoni, G / Asada, H / Asai, K / Asai, S / Asbah, N A / Assahsah, J / Assamagan, K / Astalos, R / Atashi, S / Atkin, R J / Atkinson, M / Atlay, N B / Atmani, H / Atmasiddha, P A / Augsten, K / Auricchio, S / Auriol, A D / Austrup, V A / Avolio, G / Axiotis, K / Azuelos, G / Babal, D / Bachacou, H / Bachas, K / Bachiu, A / Backman, F / Badea, A / Bagnaia, P / Bahmani, M / Bailey, A J / Bailey, V R / Baines, J T / Baines, L / Bakalis, C / Baker, O K / Bakos, E / Bakshi Gupta, D / Balasubramanian, R / Baldin, E M / Balek, P / Ballabene, E / Balli, F / Baltes, L M / Balunas, W K / Balz, J / Banas, E / Bandieramonte, M / Bandyopadhyay, A / Bansal, S / Barak, L / Barakat, M / Barberio, E L / Barberis, D / Barbero, M / Barbour, G / Barends, K N / Barillari, T / Barisits, M-S / Barklow, T / Baron, P / Baron Moreno, D A / Baroncelli, A / Barone, G / Barr, A J / Barr, J D / Barranco Navarro, L / Barreiro, F / Barreiro Guimarães da Costa, J / Barron, U / Barros Teixeira, M G / Barsov, S / Bartels, F / Bartoldus, R / Barton, A E / Bartos, P / Basan, A / Baselga, M / Bassalat, A / Basso, M J / Basson, C R / Bates, R L / Batlamous, S / Batley, J R / Batool, B / Battaglia, M / Battulga, D / Bauce, M / Bauer, M / Bauer, P / Bazzano Hurrell, L T / Beacham, J B / Beau, T / Beauchemin, P H / Becherer, F / Bechtle, P / Beck, H P / Becker, K / Beddall, A J / Bednyakov, V A / Bee, C P / Beemster, L J / Beermann, T A / Begalli, M / Begel, M / Behera, A / Behr, J K / Beirer, J F / Beisiegel, F / Belfkir, M / Bella, G / Bellagamba, L / Bellerive, A / Bellos, P / Beloborodov, K / Belyaev, N L / Benchekroun, D / Bendebba, F / Benhammou, Y / Benoit, M / Bensinger, J R / Bentvelsen, S / Beresford, L / Beretta, M / Bergeaas Kuutmann, E / Berger, N / Bergmann, B / Beringer, J / Bernardi, G / Bernius, C / Bernlochner, F U / Bernon, F / Berry, T / Berta, P / Berthold, A / Bertram, I A / Bethke, S / Betti, A / Bevan, A J / Bhamjee, M / Bhatta, S / Bhattacharya, D S / Bhattarai, P / Bhopatkar, V S / Bi, R / Bianchi, R M / Bianco, G / Biebel, O / Bielski, R / Biglietti, M / Billoud, T R V / Bindi, M / Bingul, A / Bini, C / Biondini, A / Birch-Sykes, C J / Bird, G A / Birman, M / Biros, M / Bisanz, T / Bisceglie, E / Biswas, D / Bitadze, A / Bjørke, K / Bloch, I / Blocker, C / Blue, A / Blumenschein, U / Blumenthal, J / Bobbink, G J / Bobrovnikov, V S / Boehler, M / Boehm, B / Bogavac, D / Bogdanchikov, A G / Bohm, C / Boisvert, V / Bokan, P / Bold, T / Bomben, M / Bona, M / Boonekamp, M / Booth, C D / Borbély, A G / Bordulev, I S / Borecka-Bielska, H M / Borgna, L S / Borissov, G / Bortoletto, D / Boscherini, D / Bosman, M / Bossio Sola, J D / Bouaouda, K / Bouchhar, N / Boudreau, J / Bouhova-Thacker, E V / Boumediene, D / Bouquet, R / Boveia, A / Boyd, J / Boye, D / Boyko, I R / Bracinik, J / Brahimi, N / Brandt, G / Brandt, O / Braren, F / Brau, B / Brau, J E / Brener, R / Brenner, L / Brenner, R / Bressler, S / Britton, D / Britzger, D / Brock, I / Brooijmans, G / Brooks, W K / Brost, E / Brown, L M / Bruce, L E / Bruckler, T L / Bruckman de Renstrom, P A / Brüers, B / Bruncko, D / Bruni, A / Bruni, G / Bruschi, M / Bruscino, N / Buanes, T / Buat, Q / Buchin, D / Buckley, A G / Bugge, M K / Bulekov, O / Bullard, B A / Burdin, S / Burgard, C D / Burger, A M / Burghgrave, B / Burlayenko, O / Burr, J T P / Burton, C D / Burzynski, J C / Busch, E L / Büscher, V / Bussey, P J / Butler, J M / Buttar, C M / Butterworth, J M / Buttinger, W / Buxo Vazquez, C J / Buzykaev, A R / Cabras, G / Cabrera Urbán, S / Cadamuro, L / Caforio, D / Cai, H / Cai, Y / Cairo, V M M / Cakir, O / Calace, N / Calafiura, P / Calderini, G / Calfayan, P / Callea, G / Caloba, L P / Calvet, D / Calvet, S / Calvet, T P / Calvetti, M / Camacho Toro, R / Camarda, S / Camarero Munoz, D / Camarri, P / Camerlingo, M T / Cameron, D / Camincher, C / Campanelli, M / Camplani, A / Canale, V / Canesse, A / Cano Bret, M / Cantero, J / Cao, Y / Capocasa, F / Capua, M / Carbone, A / Cardarelli, R / Cardenas, J C J / Cardillo, F / Carli, T / Carlino, G / Carlotto, J I / Carlson, B T / Carlson, E M / Carminati, L / Carnelli, A / Carnesale, M / Caron, S / Carquin, E / Carrá, S / Carratta, G / Carrio Argos, F / Carter, J W S / Carter, T M / Casado, M P / Caspar, M / Castiglia, E G / Castillo, F L / Castillo Garcia, L / Castillo Gimenez, V / Castro, N F / Catinaccio, A / Catmore, J R / Cavaliere, V / Cavalli, N / Cavasinni, V / Cekmecelioglu, Y C / Celebi, E / Celli, F / Centonze, M S / Cerny, K / Cerqueira, A S / Cerri, A / Cerrito, L / Cerutti, F / Cervato, B / Cervelli, A / Cesarini, G / Cetin, S A / Chadi, Z / Chakraborty, D / Chala, M / Chan, J / Chan, W Y / Chapman, J D / Chapon, E / Chargeishvili, B / Charlton, D G / Charman, T P / Chatterjee, M / Chauhan, C / Chekanov, S / Chekulaev, S V / Chelkov, G A / Chen, A / Chen, B / Chen, H / Chen, J / Chen, M / Chen, S / Chen, S J / Chen, X / Chen, Y / Cheng, C L / Cheng, H C / Cheong, S / Cheplakov, A / Cheremushkina, E / Cherepanova, E / Cherkaoui El Moursli, R / Cheu, E / Cheung, K / Chevalier, L / Chiarella, V / Chiarelli, G / Chiedde, N / Chiodini, G / Chisholm, A S / Chitan, A / Chitishvili, M / Chizhov, M V / Choi, K / Chomont, A R / Chou, Y / Chow, E Y S / Chowdhury, T / Chu, K L / Chu, M C / Chu, X / Chudoba, J / Chwastowski, J J / Cieri, D / Ciesla, K M / Cindro, V / Ciocio, A / Cirotto, F / Citron, Z H / Citterio, M / Ciubotaru, D A / Ciungu, B M / Clark, A / Clark, P J / Clavijo Columbie, J M / Clawson, S E / Clement, C / Clercx, J / Clissa, L / Coadou, Y / Cobal, M / Coccaro, A / Barrue, R F Coelho / Coelho Lopes De Sa, R / Coelli, S / Cohen, H / Coimbra, A E C / Cole, B / Collot, J / Conde Muiño, P / Connell, M P / Connell, S H / Connelly, I A / Conroy, E I / Conventi, F / Cooke, H G / Cooper-Sarkar, A M / Cordeiro Oudot Choi, A / Cormier, F / Corpe, L D / Corradi, M / Corriveau, F / Cortes-Gonzalez, A / Costa, M J / Costanza, F / Costanzo, D / Cote, B M / Cowan, G / Cranmer, K / Cremonini, D / Crépé-Renaudin, S / Crescioli, F / Cristinziani, M / Cristoforetti, M / Croft, V / Crosby, J E / Crosetti, G / Cueto, A / Cuhadar Donszelmann, T / Cui, H / Cui, Z / Cunningham, W R / Curcio, F / Czodrowski, P / Czurylo, M M / De Sousa, M J Da Cunha Sargedas / Da Fonseca Pinto, J V / Da Via, C / Dabrowski, W / Dado, T / Dahbi, S / Dai, T / Dallapiccola, C / Dam, M / D'amen, G / D'Amico, V / Damp, J / Dandoy, J R / Daneri, M F / Danninger, M / Dao, V / Darbo, G / Darmora, S / Das, S J / D'Auria, S / David, C / Davidek, T / Davis-Purcell, B / Dawson, I / Day-Hall, H A / De, K / De Asmundis, R / De Biase, N / De Castro, S / De Groot, N / de Jong, P / De la Torre, H / De Maria, A / De Salvo, A / De Sanctis, U / De Santo, A / De Vivie De Regie, J B / Dedovich, D V / Degens, J / Deiana, A M / Del Corso, F / Del Peso, J / Del Rio, F / Deliot, F / Delitzsch, C M / Della Pietra, M / Della Volpe, D / Dell'Acqua, A / Dell'Asta, L / Delmastro, M / Delsart, P A / Demers, S / Demichev, M / Denisov, S P / D'Eramo, L / Derendarz, D / Derue, F / Dervan, P / Desch, K / Deutsch, C / Di Bello, F A / Di Ciaccio, A / Di Ciaccio, L / Di Domenico, A / Di Donato, C / Di Girolamo, A / Di Gregorio, G / Di Luca, A / Di Micco, B / Di Nardo, R / Diaconu, C / Diamantopoulou, M / Dias, F A / Vale, T Dias Do / Diaz, M A / Diaz Capriles, F G / Didenko, M / Diehl, E B / Diehl, L / Díez Cornell, S / Diez Pardos, C / Dimitriadi, C / Dimitrievska, A / Dingfelder, J / Dinu, I-M / Dittmeier, S J / Dittus, F / Djama, F / Djobava, T / Djuvsland, J I / Doglioni, C / Dolejsi, J / Dolezal, Z / Donadelli, M / Dong, B / Donini, J / D'Onofrio, A / D'Onofrio, M / Dopke, J / Doria, A / Dos Santos Fernandes, N / Dova, M T / Doyle, A T / Draguet, M A / Dreyer, E / Drivas-Koulouris, I / Drobac, A S / Drozdova, M / Du, D / du Pree, T A / Dubinin, F / Dubovsky, M / Duchovni, E / Duckeck, G / Ducu, O A / Duda, D / Dudarev, A / Duden, E R / D'uffizi, M / Duflot, L / Dührssen, M / Dülsen, C / Dumitriu, A E / Dunford, M / Dungs, S / Dunne, K / Duperrin, A / Yildiz, H Duran / Düren, M / Durglishvili, A / Dwyer, B L / Dyckes, G I / Dyndal, M / Dysch, S / Dziedzic, B S / Earnshaw, Z O / Eberwein, G H / Eckerova, B / Eggebrecht, S / Eggleston, M G / Purcino De Souza, E Egidio / Ehrke, L F / Eigen, G / Einsweiler, K / Ekelof, T / Ekman, P A / El Farkh, S / El Ghazali, Y / El Jarrari, H / El Moussaouy, A / Ellajosyula, V / Ellert, M / Ellinghaus, F / Elliot, A A / Ellis, N / Elmsheuser, J / Elsing, M / Emeliyanov, D / Enari, Y / Ene, I / Epari, S / Erdmann, J / Erland, P A / Errenst, M / Escalier, M / Escobar, C / Etzion, E / Evans, G / Evans, H / Evans, L S / Evans, M O / Ezhilov, A / Ezzarqtouni, S / Fabbri, F / Fabbri, L / Facini, G / Fadeyev, V / Fakhrutdinov, R M / Falciano, S / Falda Ulhoa Coelho, L F / Falke, P J / Faltova, J / Fan, C / Fan, Y / Fang, Y / Fanti, M / Faraj, M / Farazpay, Z / Farbin, A / Farilla, A / Farooque, T / Farrington, S M / Fassi, F / Fassouliotis, D / Faucci Giannelli, M / Fawcett, W J / Fayard, L / Federic, P / Federicova, P / Fedin, O L / Fedotov, G / Feickert, M / Feligioni, L / Fellers, D E / Feng, C / Feng, M / Feng, Z / Fenton, M J / Fenyuk, A B / Ferencz, L / Ferguson, R A M / Fernandez Luengo, S I / Fernoux, M J V / Ferrando, J / Ferrari, A / Ferrari, P / Ferrari, R / Ferrere, D / Ferretti, C / Fiedler, F / Filipčič, A / Filmer, E K / Filthaut, F / Fiolhais, M C N / Fiorini, L / Fisher, W C / Fitschen, T / Fitzhugh, P M / Fleck, I / Fleischmann, P / Flick, T / Flores, L / Flores, M / Flores Castillo, L R / Flores Sanz De Acedo, L / Follega, F M / Fomin, N / Foo, J H / Forland, B C / Formica, A / Forti, A C / Fortin, E / Fortman, A W / Foti, M G / Fountas, L / Fournier, D / Fox, H / Francavilla, P / Francescato, S / Franchellucci, S / Franchini, M / Franchino, S / Francis, D / Franco, L / Franconi, L / Franklin, M / Frattari, G / Freegard, A C / Freund, W S / Frid, Y Y / Fritzsche, N / Froch, A / Froidevaux, D / Frost, J A / Fu, Y / Fujimoto, M / Fullana Torregrosa, E / Fung, K Y / De Simas Filho, E Furtado / Furukawa, M / Fuster, J / Gabrielli, A / Gadow, P / Gagliardi, G / Gagnon, L G / Gallas, E J / Gallop, B J / Gan, K K / Ganguly, S / Gao, J / Gao, Y / Garay Walls, F M / Garcia, B / García, C / Garcia Alonso, A / Garcia Caffaro, A G / García Navarro, J E / Garcia-Sciveres, M / Gardner, G L / Gardner, R W / Garelli, N / Garg, D / Garg, R B / Gargan, J M / Garner, C A / Gasiorowski, S J / Gaspar, P / Gaudio, G / Gautam, V / Gauzzi, P / Gavrilenko, I L / Gavrilyuk, A / Gay, C / Gaycken, G / Gazis, E N / Geanta, A A / Gee, C M / Gemme, C / Genest, M H / Gentile, S / George, S / George, W F / Geralis, T / Gessinger-Befurt, P / Geyik, M E / Ghneimat, M / Ghorbanian, K / Ghosal, A / Ghosh, A / Giacobbe, B / Giagu, S / Giannetti, P / Giannini, A / Gibson, S M / Gignac, M / Gil, D T / Gilbert, A K / Gilbert, B J / Gillberg, D / Gilles, G / Gillwald, N E K / Ginabat, L / Gingrich, D M / Giordani, M P / Giraud, P F / Giugliarelli, G / Giugni, D / Giuli, F / Gkialas, I / Gladilin, L K / Glasman, C / Gledhill, G R / Glemža, G / Glisic, M / Gnesi, I / Go, Y / Goblirsch-Kolb, M / Gocke, B / Godin, D / Gokturk, B / Goldfarb, S / Golling, T / Gololo, M G D / Golubkov, D / Gombas, J P / Gomes, A / Gomes Da Silva, G / Gomez Delegido, A J / Gonçalo, R / Gonella, G / Gonella, L / Gongadze, A / Gonnella, F / Gonski, J L / González Andana, R Y / González de la Hoz, S / Gonzalez Fernandez, S / Gonzalez Lopez, R / Gonzalez Renteria, C / Gonzalez Suarez, R / Gonzalez-Sevilla, S / Gonzalvo Rodriguez, G R / Goossens, L / Gorbounov, P A / Gorini, B / Gorini, E / Gorišek, A / Gosart, T C / Goshaw, A T / Gostkin, M I / Goswami, S / Gottardo, C A / Gouighri, M / Goumarre, V / Goussiou, A G / Govender, N / Grabowska-Bold, I / Graham, K / Gramstad, E / Grancagnolo, S / Grandi, M / Gravila, P M / Gravili, F G / Gray, H M / Greco, M / Grefe, C / Gregor, I M / Grenier, P / Grieco, C / Grillo, A A / Grimm, K / Grinstein, S / Grivaz, J-F / Gross, E / Grosse-Knetter, J / Grud, C / Grundy, J C / Guan, L / Guan, W / Gubbels, C / Guerrero Rojas, J G R / Guerrieri, G / Guescini, F / Gugel, R / Guhit, J A M / Guida, A / Guillemin, T / Guilloton, E / Guindon, S / Guo, F / Guo, J / Guo, L / Guo, Y / Gupta, R / Gurbuz, S / Gurdasani, S S / Gustavino, G / Guth, M / Gutierrez, P / Gutierrez Zagazeta, L F / Gutschow, C / Gwenlan, C / Gwilliam, C B / Haaland, E S / Haas, A / Habedank, M / Haber, C / Hadavand, H K / Hadef, A / Hadzic, S / Hahn, J J / Haines, E H / Haleem, M / Haley, J / Hall, J J / Hallewell, G D / Halser, L / Hamano, K / Hamdaoui, H / Hamer, M / Hamity, G N / Hampshire, E J / Han, J / Han, K / Han, L / Han, S / Han, Y F / Hanagaki, K / Hance, M / Hangal, D A / Hanif, H / Hank, M D / Hankache, R / Hansen, J B / Hansen, J D / Hansen, P H / Hara, K / Harada, D / Harenberg, T / Harkusha, S / Harris, M L / Harris, Y T / Harrison, J / Harrison, N M / Harrison, P F / Hartman, N M / Hartmann, N M / Hasegawa, Y / Hasib, A / Haug, S / Hauser, R / Hawkes, C M / Hawkings, R J / Hayashi, Y / Hayashida, S / Hayden, D / Hayes, C / Hayes, R L / Hays, C P / Hays, J M / Hayward, H S / He, F / He, M / He, Y / Heatley, N B / Hedberg, V / Heggelund, A L / Hehir, N D / Heidegger, C / Heidegger, K K / Heidorn, W D / Heilman, J / Heim, S / Heim, T / Heinemann, B / Heinlein, J G / Heinrich, J J / Heinrich, L / Hejbal, J / Helary, L / Held, A / Hellesund, S / Helling, C M / Hellman, S / Henderson, R C W / Henkelmann, L / Henriques Correia, A M / Herde, H / Hernández Jiménez, Y / Herrmann, L M / Herrmann, T / Herten, G / Hertenberger, R / Hervas, L / 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J M / Iuppa, R / Ivina, A / Izen, J M / Izzo, V / Jacka, P / Jackson, P / Jacobs, R M / Jaeger, B P / Jagfeld, C S / Jain, P / Jäkel, G / Jakobs, K / Jakoubek, T / Jamieson, J / Janas, K W / Jaspan, A E / Javurkova, M / Jeanneau, F / Jeanty, L / Jejelava, J / Jenni, P / Jessiman, C E / Jézéquel, S / Jia, C / Jia, J / Jia, X / Jia, Z / Jiang, Y / Jiggins, S / Jimenez Pena, J / Jin, S / Jinaru, A / Jinnouchi, O / Johansson, P / Johns, K A / Johnson, J W / Jones, D M / Jones, E / Jones, P / Jones, R W L / Jones, T J / Joshi, R / Jovicevic, J / Ju, X / Junggeburth, J J / Junkermann, T / Juste Rozas, A / Juzek, M K / Kabana, S / Kaczmarska, A / Kado, M / Kagan, H / Kagan, M / Kahn, A / Kahra, C / Kaji, T / Kajomovitz, E / Kakati, N / Kalaitzidou, I / Kalderon, C W / Kamenshchikov, A / Kanayama, S / Kang, N J / Kar, D / Karava, K / Kareem, M J / Karentzos, E / Karkanias, I / Karkout, O / Karpov, S N / Karpova, Z M / Kartvelishvili, V / Karyukhin, A N / Kasimi, E / Katzy, J / Kaur, S / Kawade, K / Kawale, M P / Kawamoto, T / Kay, E F / Kaya, F I / Kazakos, S / Kazanin, V F / Ke, Y / Keaveney, J M / Keeler, R / Kehris, G V / Keller, J S / Kelly, A S / Kempster, J J / Kennedy, K E / Kennedy, P D / Kepka, O / Kerridge, B P / Kersten, S / Kerševan, B P / Keshri, S / Keszeghova, L / Ketabchi Haghighat, S / Khandoga, M / Khanov, A / Kharlamov, A G / Kharlamova, T / Khoda, E E / Khoo, T J / Khoriauli, G / Khubua, J / Khwaira, Y A R / Kilgallon, A / Kim, D W / Kim, Y K / Kimura, N / Kirchhoff, A / Kirfel, C / Kirfel, F / Kirk, J / Kiryunin, A E / Kitsaki, C / Kivernyk, O / Klassen, M / Klein, C / Klein, L / Klein, M H / Klein, M / Klein, S B / Klein, U / Klimek, P / Klimentov, A / Klioutchnikova, T / Kluit, P / Kluth, S / Kneringer, E / Knight, T M / Knue, A / Kobayashi, R / Koch, S F / Kocian, M / Kodyš, P / Koeck, D M / Koenig, P T / Koffas, T / Kolb, M / Koletsou, I / Komarek, T / Köneke, K / Kong, A X Y / Kono, T / Konstantinidis, N / Konya, B / Kopeliansky, R / Koperny, S / Korcyl, K / Kordas, K / Koren, G / Korn, A / Korn, S / Korolkov, I / Korotkova, N / Kortman, B / Kortner, O / Kortner, S / Kostecka, W H / Kostyukhin, V V / Kotsokechagia, A / Kotwal, A / Koulouris, A / Kourkoumeli-Charalampidi, A / Kourkoumelis, C / Kourlitis, E / Kovanda, O / Kowalewski, R / Kozanecki, W / Kozhin, A S / Kramarenko, V A / Kramberger, G / Kramer, P / Krasny, M W / Krasznahorkay, A / Kraus, J W / Kremer, J A / Kresse, T / Kretzschmar, J / Kreul, K / Krieger, P / Krishnamurthy, S / Krivos, M / Krizka, K / Kroeninger, K / Kroha, H / Kroll, J / Krowpman, K S / Kruchonak, U / Krüger, H / Krumnack, N / Kruse, M C / Krzysiak, J A / Kuchinskaia, O / Kuday, S / Kuehn, S / Kuesters, R / Kuhl, T / Kukhtin, V / Kulchitsky, Y / Kuleshov, S / Kumar, M / Kumari, N / Kupco, A / Kupfer, T / Kupich, A / Kuprash, O / Kurashige, H / Kurchaninov, L L / Kurdysh, O / Kurochkin, Y A / Kurova, A / Kuze, M / Kvam, A K / Kvita, J / Kwan, T / Kyriacou, N G / Laatu, L A O / Lacasta, C / Lacava, F / Lacker, H / Lacour, D / Lad, N N / Ladygin, E / Laforge, B / Lagouri, T / Lai, S / Lakomiec, I K / Lalloue, N / Lambert, J E / Lammers, S / Lampl, W / Lampoudis, C / Lancaster, A N / Lançon, E / Landgraf, U / Landon, M P J / Lang, V S / Langenberg, R J / Langrekken, O K B / Lankford, A J / Lanni, F / Lantzsch, K / Lanza, A / Lapertosa, A / Laporte, J F / Lari, T / Lasagni Manghi, F / Lassnig, M / Latonova, V / Laudrain, A / Laurier, A / Lawlor, S D / Lawrence, Z / Lazzaroni, M / Le, B / Le Boulicaut, E M / Leban, B / Lebedev, A / LeBlanc, M / Ledroit-Guillon, F / Lee, A C A / Lee, S C / Lee, S / Lee, T F / Leeuw, L L / Lefebvre, H P / Lefebvre, M / Leggett, C / Lehmann Miotto, G / Leigh, M / Leight, W A / Leinonen, W / Leisos, A / Leite, M A L / Leitgeb, C E / Leitner, R / Leney, K J C / Lenz, T / Leone, S / Leonidopoulos, C / Leopold, A / Leroy, C / Les, R / Lester, C G / Levchenko, M / Levêque, J / Levin, D / Levinson, L J / Lewicki, M P / Lewis, D J / Li, A / Li, B / Li, C / Li, C-Q / Li, H / Li, K / Li, L / Li, M / Li, Q Y / Li, S / Li, T / Li, X / Li, Z / Liang, Z / Liberatore, M / Liberti, B / Lie, K / Lieber Marin, J / Lien, H / Lin, K / Lindley, R E / Lindon, J H / Linss, A / Lipeles, E / Lipniacka, A / Lister, A / Little, J D / Liu, B / Liu, B X / Liu, D / Liu, J B / Liu, J K K / Liu, K / Liu, M / Liu, M Y / Liu, P / Liu, Q / Liu, X / Liu, Y / Liu, Y L / Liu, Y W / Llorente Merino, J / Lloyd, S L / Lobodzinska, E M / Loch, P / Loffredo, S / Lohse, T / Lohwasser, K / Loiacono, E / Lokajicek, M / Lomas, J D / Long, J D / Longarini, I / Longo, L / Longo, R / Lopez Paz, I / Lopez Solis, A / Lorenz, J / Lorenzo Martinez, N / Lory, A M / Löschcke Centeno, G / Loseva, O / Lou, X / Lounis, A / Love, J / Love, P A / Lu, G / Lu, M / Lu, S / Lu, Y J / Lubatti, H J / Luci, C / Lucio Alves, F L / Lucotte, A / Luehring, F / Luise, I / Lukianchuk, O / Lundberg, O / Lund-Jensen, B / Luongo, N A / Lutz, M S / Lynn, D / Lyons, H / Lysak, R / Lytken, E / Lyubushkin, V / Lyubushkina, T / Lyukova, M M / Ma, H / Ma, K / Ma, L L / Ma, Y / Mac Donell, D M / Maccarrone, G / MacDonald, J C / Madar, R / Mader, W F / Maeda, J / Maeno, T / Maerker, M / Maguire, H / Maiboroda, V / Maio, A / Maj, K / Majersky, O / Majewski, S / Makovec, N / Maksimovic, V / Malaescu, B / Malecki, Pa / Maleev, V P / Malek, F / Mali, M / Malito, D / Mallik, U / Maltezos, S / Malyukov, S / Mamuzic, J / Mancini, G / Manco, G / Mandalia, J P / Mandić, I / Manhaes de Andrade Filho, L / Maniatis, I M / Manjarres Ramos, J / Mankad, D C / Mann, A / Mansoulie, B / Manzoni, S / Marantis, A / Marchiori, G / Marcisovsky, M / Marcon, C / Marinescu, M / Marjanovic, M / Marshall, E J / Marshall, Z / Marti-Garcia, S / Martin, T A / Martin, V J / Martin Dit Latour, B / Martinelli, L / Martinez, M / Martinez Agullo, P / Martinez Outschoorn, V I / Martinez Suarez, P / Martin-Haugh, S / Martoiu, V S / Martyniuk, A C / Marzin, A / Mascione, D / Masetti, L / Mashimo, T / Masik, J / Maslennikov, A L / Massa, L / Massarotti, P / Mastrandrea, P / Mastroberardino, A / Masubuchi, T / Mathisen, T / Matousek, J / Matsuzawa, N / Maurer, J / Maček, B / Maximov, D A / Mazini, R / Maznas, I / Mazza, M / Mazza, S M / Mazzeo, E / Mc Ginn, C / Mc Gowan, J P / Mc Kee, S P / McDonald, E F / McDougall, A E / Mcfayden, J A / McGovern, R P / Mchedlidze, G / Mckenzie, R P / Mclachlan, T C / Mclaughlin, D J / McLean, K D / McMahon, S J / McNamara, P C / Mcpartland, C M / McPherson, R A / Mehlhase, S / Mehta, A / Melini, D / Mellado Garcia, B R / Melo, A H / Meloni, F / Mendes Jacques Da Costa, A M / Meng, H Y / Meng, L / Menke, S / Mentink, M / Meoni, E / Merlassino, C / Merola, L / Meroni, C / Merz, G / Meshkov, O / Metcalfe, J / Mete, A S / Meyer, C / Meyer, J-P / Middleton, R P / Mijović, L / Mikenberg, G / Mikestikova, M / Mikuž, M / Mildner, H / Milic, A / Milke, C D / Miller, D W / Miller, L S / Milov, A / Milstead, D A / Min, T / Minaenko, A A / Minashvili, I A / Mince, L / Mincer, A I / Mindur, B / Mineev, M / Mino, Y / Mir, L M / Miralles Lopez, M / Mironova, M / Mishima, A / Missio, M C / Mitani, T / Mitra, A / Mitsou, V A / Miu, O / Miyagawa, P S / Miyazaki, Y / Mizukami, A / Mkrtchyan, T / Mlinarevic, M / Mlinarevic, T / Mlynarikova, M / Mobius, S / Mochizuki, K / Moder, P / Mogg, P / Mohammed, A F / Mohapatra, S / Mokgatitswane, G / Moleri, L / Mondal, B / Mondal, S / Mönig, K / Monnier, E / Monsonis Romero, L / Montejo Berlingen, J / Montella, M / Montereali, F / Monticelli, F / Monzani, S / Morange, N / De Carvalho, A L Moreira / Moreno Llácer, M / Moreno Martinez, C / Morettini, P / Morgenstern, S / Morii, M / Morinaga, M / Morley, A K / Morodei, F / Morvaj, L / Moschovakos, P / Moser, B / Mosidze, M / Moskalets, T / Moskvitina, P / Moss, J / Moyse, E J W / Mtintsilana, O / Muanza, S / Mueller, J / Muenstermann, D / Müller, R / Mullier, G A / Mullin, A J / Mullin, J J / Mungo, D P / Munoz Perez, D / Munoz Sanchez, F J / Murin, M / Murray, W J / 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Norisam, R R B / Norman, B J / Novak, J / Novak, T / Novotny, L / Novotny, R / Nozka, L / Ntekas, K / Nunes De Moura Junior, N M J / Nurse, E / Ocariz, J / Ochi, A / Ochoa, I / Oerdek, S / Offermann, J T / Ogrodnik, A / Oh, A / Ohm, C C / Oide, H / Oishi, R / Ojeda, M L / Okazaki, Y / O'Keefe, M W / Okumura, Y / Seabra, L F Oleiro / Olivares Pino, S A / Oliveira Damazio, D / Oliveira Goncalves, D / Oliver, J L / Olszewski, A / Öncel, Ö O / O'Neil, D C / O'Neill, A P / Onofre, A / Onyisi, P U E / Oreglia, M J / Orellana, G E / Orestano, D / Orlando, N / Orr, R S / O'Shea, V / Osojnak, L M / Ospanov, R / Otero Y Garzon, G / Otono, H / Ott, P S / Ottino, G J / Ouchrif, M / Ouellette, J / Ould-Saada, F / Owen, M / Owen, R E / Oyulmaz, K Y / Ozcan, V E / Ozturk, N / Ozturk, S / Pacey, H A / Pacheco Pages, A / Padilla Aranda, C / Padovano, G / Pagan Griso, S / Palacino, G / Palazzo, A / Palestini, S / Pan, J / Pan, T / Panchal, D K / Pandini, C E / Panduro Vazquez, J G / Pang, H / Pani, P / Panizzo, G / Paolozzi, L / Papadatos, C / Parajuli, S / Paramonov, A / Paraskevopoulos, C / Paredes Hernandez, D / Park, T H / Parker, M A / Parodi, F / Parrish, E W / Parrish, V A / Parsons, J A / Parzefall, U / Pascual Dias, B / Pascual Dominguez, L / Pasquali, F / Pasqualucci, E / Passaggio, S / Pastore, F / Pasuwan, P / Patel, P / Patel, U M / Pater, J R / Pauly, T / Pearkes, J / Pedersen, M / Pedro, R / Peleganchuk, S V / Penc, O / Pender, E A / Peng, H / Penski, K E / Penzin, M / Peralva, B S / Peixoto, A P Pereira / Pereira Sanchez, L / Perepelitsa, D V / Perez Codina, E / Perganti, M / Perini, L / Pernegger, H / Perrin, O / Peters, K / Peters, R F Y / Petersen, B A / Petersen, T C / Petit, E / Petousis, V / Petridou, C / Petrukhin, A / Pettee, M / Pettersson, N E / Petukhov, A / Petukhova, K / Peyaud, A / Pezoa, R / Pezzotti, L / Pezzullo, G / Pham, T M / Pham, T / Phillips, P W / Piacquadio, G / Pianori, E / Piazza, F / Piegaia, R / Pietreanu, D / Pilkington, A D / Pinamonti, M / Pinfold, J L / Pereira, B C Pinheiro / Pinto Pinoargote, A E / Piper, K M / Pirttikoski, A / Pitman Donaldson, C / Pizzi, D A / Pizzimento, L / Pizzini, A / Pleier, M-A / Plesanovs, V / Pleskot, V / Plotnikova, E / Poddar, G / Poettgen, R / Poggioli, L / Pokharel, I / Polacek, S / Polesello, G / Poley, A / Polifka, R / Polini, A / Pollard, C S / Pollock, Z B / Polychronakos, V / Pompa Pacchi, E / Ponomarenko, D / Pontecorvo, L / Popa, S / Popeneciu, G A / Poreba, A / Portillo Quintero, D M / Pospisil, S / Postill, M A / Postolache, P / Potamianos, K / Potepa, P A / Potrap, I N / Potter, C J / Potti, H / Poulsen, T / Poveda, J / Pozo Astigarraga, M E / Prades Ibanez, A / Pretel, J / Price, D / Primavera, M / Principe Martin, M A / Privara, R / Procter, T / Proffitt, M L / Proklova, N / Prokofiev, K / Proto, G / Protopopescu, S / Proudfoot, J / Przybycien, M / Przygoda, W W / Puddefoot, J E / Pudzha, D / Pyatiizbyantseva, D / Qian, J / Qichen, D / Qin, Y / Qiu, T / Quadt, A / Queitsch-Maitland, M / Quetant, G / Rabanal Bolanos, G / Rafanoharana, D / Ragusa, F / Rainbolt, J L / Raine, J A / Rajagopalan, S / Ramakoti, E / Ran, K / Rapheeha, N P / Rasheed, H / Raskina, V / Rassloff, D F / Rave, S / Ravina, B / Ravinovich, I / Raymond, M / Read, A L / Readioff, N P / Rebuzzi, D M / Redlinger, G / Reed, A S / Reeves, K / Reidelsturz, J A / Reikher, D / Rej, A / Rembser, C / Renardi, A / Renda, M / Rendel, M B / Renner, F / Rennie, A G / Resconi, S / Ressegotti, M / Rettie, S / Reyes Rivera, J G / Reynolds, B / Reynolds, E / Rezanova, O L / Reznicek, P / Ribaric, N / Ricci, E / Richter, R / Richter, S / Richter-Was, E / Ridel, M / Ridouani, S / Rieck, P / Riedler, P / Rijssenbeek, M / Rimoldi, A / Rimoldi, M / Rinaldi, L / Rinn, T T / Rinnagel, M P / Ripellino, G / Riu, I / Rivadeneira, P / Rivera Vergara, J C / Rizatdinova, F / Rizvi, E / Roberts, B A / Roberts, B R / Robertson, S H / Robin, M / Robinson, D / Robles Gajardo, C M / Robles Manzano, M / Robson, A / Rocchi, A / Roda, C / Rodriguez Bosca, S / Rodriguez Garcia, Y / Rodriguez Rodriguez, A / Rodríguez Vera, A M / Roe, S / Roemer, J T / Roepe-Gier, A R / Roggel, J / Røhne, O / Rojas, R A / Roland, C P A / Roloff, J / Romaniouk, A / Romano, E / Romano, M / Romero Hernandez, A C / Rompotis, N / Roos, L / Rosati, S / Rosser, B J / Rossi, E / Rossi, L P / Rossini, L / Rosten, R / Rotaru, M / Rottler, B / Rougier, C / Rousseau, D / Rousso, D / Roy, A / Roy-Garand, S / Rozanov, A / Rozen, Y / Ruan, X / Rubio Jimenez, A / Ruby, A J / Ruelas Rivera, V H / Ruggeri, T A / Ruggiero, A / Ruiz-Martinez, A / Rummler, A / Rurikova, Z / Rusakovich, N A / Russell, H L / Russo, G / Rutherfoord, J P / Rutherford Colmenares, S / Rybacki, K / Rybar, M / Rye, E B / Ryzhov, A / Sabater Iglesias, J A / Sabatini, P / Sabetta, L / Sadrozinski, H F-W / Safai Tehrani, F / Safarzadeh Samani, B / Safdari, M / Saha, S / Sahinsoy, M / Saimpert, M / Saito, M / Saito, T / Salamani, D / Salnikov, A / Salt, J / Salvador Salas, A / Salvatore, D / Salvatore, F / Salzburger, A / Sammel, D / Sampsonidis, D / Sampsonidou, D / Sánchez, J / Sanchez Pineda, A / Sanchez Sebastian, V / Sandaker, H / Sander, C O / Sandesara, J A / Sandhoff, M / Sandoval, C / Sankey, D P C / Sano, T / Sansoni, A / Santi, L / Santoni, C / Santos, H / Santpur, S N / Santra, A / Saoucha, K A / Saraiva, J G / Sardain, J / Sasaki, O / Sato, K / Sauer, C / Sauerburger, F / Sauvan, E / Savard, P / Sawada, R / Sawyer, C / Sawyer, L / Sayago Galvan, I / Sbarra, C / Sbrizzi, A / Scanlon, T / Schaarschmidt, J / Schacht, P / Schaefer, D / Schäfer, U / Schaffer, A C / Schaile, D / Schamberger, R D / Scharf, C / Schefer, M M / Schegelsky, V A / Scheirich, D / Schenck, F / Schernau, M / Scheulen, C / Schiavi, C / Schioppa, E J / Schioppa, M / Schlag, B / Schleicher, K E / Schlenker, S / Schmeing, J / Schmidt, M A / Schmieden, K / Schmitt, C / Schmitt, S / Schoeffel, L / Schoening, A / Scholer, P G / Schopf, E / Schott, M / Schovancova, J / Schramm, S / Schroeder, F / Schroer, T / Schultz-Coulon, H-C / Schumacher, M / Schumm, B A / Schune, Ph / Schuy, A J / Schwartz, H R / Schwartzman, A / Schwarz, T A / Schwemling, Ph / Schwienhorst, R / Sciandra, A / Sciolla, G / Scuri, F / Sebastiani, C D / Sedlaczek, K / Seema, P / Seidel, S C / Seiden, A / Seidlitz, B D / Seitz, C / Seixas, J M / Sekhniaidze, G / Sekula, S J / Selem, L / Semprini-Cesari, N / Sengupta, D / Senthilkumar, V / Serin, L / Serkin, L / Sessa, M / Severini, H / Sforza, F / Sfyrla, A / Shabalina, E / Shaheen, R / Shahinian, J D / Shaked Renous, D / Shan, L Y / Shapiro, M / Sharma, A / Sharma, A S / Sharma, P / Sharma, S / Shatalov, P B / Shaw, K / Shaw, S M / Shcherbakova, A / Shen, Q / Sherwood, P / Shi, L / Shi, X / Shimmin, C O / Shimogama, Y / Shinner, J D / Shipsey, I P J / Shirabe, S / Shiyakova, M / Shlomi, J / Shochet, M J / Shojaii, J / Shope, D R / Shrestha, B / Shrestha, S / Shrif, E M / Shroff, M J / Sicho, P / Sickles, A M / Sideras Haddad, E / 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G / Spina, M / Spinali, S / Spiteri, D P / Spousta, M / Staats, E J / Stabile, A / Stamen, R / Stamenkovic, M / Stampekis, A / Standke, M / Stanecka, E / Stange, M V / Stanislaus, B / Stanitzki, M M / Stapf, B / Starchenko, E A / Stark, G H / Stark, J / Starko, D M / Staroba, P / Starovoitov, P / Stärz, S / Staszewski, R / Stavropoulos, G / Steentoft, J / Steinberg, P / Stelzer, B / Stelzer, H J / Stelzer-Chilton, O / Stenzel, H / Stevenson, T J / Stewart, G A / Stewart, J R / Stockton, M C / Stoicea, G / Stolarski, M / Stonjek, S / Straessner, A / Strandberg, J / Strandberg, S / Strauss, M / Strebler, T / Strizenec, P / Ströhmer, R / Strom, D M / Strom, L R / Stroynowski, R / Strubig, A / Stucci, S A / Stugu, B / Stupak, J / Styles, N A / Su, D / Su, S / Su, W / Su, X / Sugizaki, K / Sulin, V V / Sullivan, M J / Sultan, D M S / Sultanaliyeva, L / Sultansoy, S / Sumida, T / Sun, S / Gudnadottir, O Sunneborn / Sur, N / Sutton, M R / Suzuki, H / Svatos, M / Swiatlowski, M / Swirski, T / 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    Physical review letters

    2024  Volume 132, Issue 2, Page(s) 21802

    Abstract: This Letter reports the observation of WZγ production and a measurement of its cross section using 140.1±1.2  fb^{-1} of proton-proton collision data recorded at a center-of-mass energy of 13 TeV by the ATLAS detector at the Large Hadron Collider. The ... ...

    Abstract This Letter reports the observation of WZγ production and a measurement of its cross section using 140.1±1.2  fb^{-1} of proton-proton collision data recorded at a center-of-mass energy of 13 TeV by the ATLAS detector at the Large Hadron Collider. The WZγ production cross section, with both the W and Z bosons decaying leptonically, pp→WZγ→ℓ^{'}^{±}νℓ^{+}ℓ^{-}γ (ℓ^{(^{'})}=e, μ), is measured in a fiducial phase-space region defined such that the leptons and the photon have high transverse momentum and the photon is isolated. The cross section is found to be 2.01±0.30(stat)±0.16(syst)  fb. The corresponding standard model predicted cross section calculated at next-to-leading order in perturbative quantum chromodynamics and at leading order in the electroweak coupling constant is 1.50±0.06  fb. The observed significance of the WZγ signal is 6.3σ, compared with an expected significance of 5.0σ.
    Language English
    Publishing date 2024-01-26
    Publishing country United States
    Document type Journal Article
    ZDB-ID 208853-8
    ISSN 1079-7114 ; 0031-9007
    ISSN (online) 1079-7114
    ISSN 0031-9007
    DOI 10.1103/PhysRevLett.132.021802
    Database MEDical Literature Analysis and Retrieval System OnLINE

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