Documentos de Cristina Mantilla Suarez | REDI

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Mostrando 4 resultados de: 4

Subtipo de publicación

Conference Object(1)

Publisher

Journal of High Energy Physics(1)

Journal of Instrumentation(1)

Proceedings of Science(1)

Reports on Progress in Physics(1)

Área temáticas

Ciencias de la computación(1)

Funcionamiento de bibliotecas y archivos(1)

Física aplicada(1)

Física moderna(1)

Área de conocimiento

Física de partículas(2)

Análisis de datos(1)

Aprendizaje automático(1)

Ciencias de la computación(1)

Año de Publicación

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Palabras Claves

Anomaly detection(1)

Beyond standard model(1)

Detector alignment and calibration methods (lasers, sources, particle-beams)(1)

Detector design and construction technologies and materials(1)

Measurement of the top quark mass from leptonic observables in pp collisions

Conference Object

Abstract: A novel technique for measuring the top quark mass using only leptonic observables is discussed. Top

Palabras claves:

Cristina Mantilla Suarez

Tag N’ Train: a technique to train improved classifiers on unlabeled data

Abstract: There has been substantial progress in applying machine learning techniques to classification proble

Palabras claves:

Beyond standard model, Exotics, Hadron-Hadron scattering (experiments), Jet substructure, Jets

Amram O., Cristina Mantilla Suarez

The CMS Phase-1 pixel detector upgrade

Abstract: The CMS detector at the CERN LHC features a silicon pixel detector as its innermost subdetector. The

Palabras claves:

Detector alignment and calibration methods (lasers, sources, particle-beams), Detector design and construction technologies and materials, Instrumentation for particle accelerators and storage rings - high energy (linear accelerators, synchrotrons), Particle tracking detectors

Adam W., Agram J.L., Allard Y., Andrea J., Baulieu G., Beaumont W., Beghin D., Bergauer T., Bilin B., Blekman F., Blöch D., Bols E.S., Bondu O., Bonnin C., Boudoul G., Bourgatte G., Brigljević V., Brom J.M., Brücken E., Bruno G., Caponetto L., Caputo C., Chabert E., Chanon N., Charles L., Chhibra S.S., Clerbaux B., Collard C., Contardo D., Cristina Mantilla Suarez, Dangelser E., David P., De Clercq J., De Lentdecker G., Delaere C., Delannoy H., Delcourt M., Dené P., Deng W., Di Croce D., Dragicevic M., D’Hondt J., Eerola P., Favart L., Ferenček D., Frühwirth R., Gädda A., Giammanco A., Goerlach U., Goldouzian R., Grebenyuk A., Grimault C., Gross L., Hinger V., Janssen X., Jiménez E.S., Kalsi A.K., Krauth M., Lampén T., Lauwers J., Lemaitre V., Lowette S., Luukka P., Mahdavikhorrami M., Majumder D., Makarenko I., Marono M.V., Martikainen L., Moortgat S., Morton A., Moureaux L., Ollivier-Henry N., Popov A., Postiau N., Prisciandaro J., Robert F., Roguljić M., Saggio A., Sahasransu A.R., Skovpen K., Song Z., Starodumov A., Steininger H., Susa T., Szilasi N., Thomas L., Tuominen E., Turpeinen R., Tuuva T., Van Mechelen P., Van Mulders P., Van Remortel N., Vanlaer P., Vannerom D., Vischia P., Wang H., Wang Q., Winkler A., Yang Y., Zobec J.

The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics

Abstract: A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and ai

Palabras claves:

Anomaly detection, beyond the standard model, Machine learning, model-agnostic methods, semisupervised learning, unsupervised learning, weakly supervised learning

Amram O., Andreassen A., Benkendorfer K., Bortolato B., Brooijmans G., Canelli F., Collins J.H., Cristina Mantilla Suarez, Dai B., De Freitas F.F., Dillon B.M., Dinu I.M., Dong Z., Donini J., Duarte J., Faroughy D.A., Gonski J., Harris P., Kahn A., Kamenik J.F., Kasieczka G., Khosa C.K., Komiske P., Le Pottier L., Martín-Ramiro P., Matevc A., Metodiev E., Mikuni V., Murphy C.W., Nachman B., Ochoa I., Park S.E., Pierini M., Rankin D., Sanz V., Sarda N., Seljak U., Shih D., Smolkovic A., Stein G., Szewc M., Thaler J., Tsan S., Udrescu S.M., Vaslin L., Vlimant J.R., Williams D., Yunus M.