Machine Learning-Assisted Measurement of Lepton-Jet Azimuthal Angular Asymmetries in Deep-Inelastic Scattering at HERA

Autor: The H1 collaboration, Andreev, V., Arratia, M., Baghdasaryan, A., Baty, A., Begzsuren, K., Bolz, A., Boudry, V., Brandt, G., Britzger, D., Buniatyan, A., Bystritskaya, L., Campbell, A. J., Avila, K. B. Cantun, Cerny, K., Chekelian, V., Chen, Z., Contreras, J. G., Cvach, J., Dainton, J. B., Daum, K., Deshpande, A., Diaconu, C., Drees, A., Eckerlin, G., Egli, S., Elsen, E., Favart, L., Fedotov, A., Feltesse, J., Fleischer, M., Fomenko, A., Gal, C., Gayler, J., Goerlich, L., Gogitidze, N., Gouzevitch, M., Grab, C., Greenshaw, T., Grindhammer, G., Haidt, D., Henderson, R. C. W., Hessler, J., Hladký, J., Hoffmann, D., Horisberger, R., Hreus, T., Huber, F., Jacobs, P. M., Jacquet, M., Janssen, T., Jung, A. W., Katzy, J., Kiesling, C., Klein, M., Kleinwort, C., Klest, H. T., Kogler, R., Kostka, P., Kretzschmar, J., Krücker, D., Krüger, K., Landon, M. P. J., Lange, W., Laycock, P., Lee, S. H., Levonian, S., Li, W., Lin, J., Lipka, K., List, B., List, J., Lobodzinski, B., Long, O. R., Malinovski, E., Martyn, H. -U., Maxfield, S. J., Mehta, A., Meyer, A. B., Meyer, J., Mikocki, S., Mikuni, V. M., Mondal, M. M., Müller, K., Nachman, B., Naumann, Th., Newman, P. R., Niebuhr, C., Nowak, G., Olsson, J. E., Ozerov, D., Park, S., Pascaud, C., Patel, G. D., Perez, E., Petrukhin, A., Picuric, I., Pitzl, D., Radescu, V., Raicevic, N., Ravdandorj, T., Reichelt, D., Reimer, P., Rizvi, E., Robmann, P., Roosen, R., Rostovtsev, A., Rotaru, M., Sankey, D. P. C., Sauter, M., Sauvan, E., Schmitt, S., Schmookler, B. A., Schnell, G., Schoeffel, L., Schöning, A., Sefkow, F., Shushkevich, S., Soloviev, Y., Sopicki, P., South, D., Specka, A., Steder, M., Stella, B., Straumann, U., Sun, C., Sykora, T., Thompson, P. D., Acosta, F. Torales, Traynor, D., Tseepeldorj, B., Tu, Z., Tustin, G., Valkárová, A., Vallée, C., Van Mechelen, P., Wegener, D., Wünsch, E., Žáček, J., Zhang, J., Zhang, Z., Žlebčík, R., Zohrabyan, H., Zomer, F.

In deep-inelastic positron-proton scattering, the lepton-jet azimuthal angular asymmetry is measured using data collected with the H1 detector at HERA. When the average transverse momentum of the lepton-jet system, $\lvert \vec{P}_\perp \rvert $, is

Externí odkaz: http://arxiv.org/abs/2412.14092

On conditional diffusion models for PDE simulations

Autor: Shysheya, Aliaksandra, Diaconu, Cristiana, Bergamin, Federico, Perdikaris, Paris, Hernández-Lobato, José Miguel, Turner, Richard E., Mathieu, Emile

Modelling partial differential equations (PDEs) is of crucial importance in science and engineering, and it includes tasks ranging from forecasting to inverse problems, such as data assimilation. However, most previous numerical and machine learning

Externí odkaz: http://arxiv.org/abs/2410.16415

Multi-Sensor Deep Learning for Glacier Mapping

Autor: Diaconu, Codruţ-Andrei, Heidler, Konrad, Bamber, Jonathan L., Zekollari, Harry

The more than 200,000 glaciers outside the ice sheets play a crucial role in our society by influencing sea-level rise, water resource management, natural hazards, biodiversity, and tourism. However, only a fraction of these glaciers benefit from con

Externí odkaz: http://arxiv.org/abs/2409.12034

First search for dark photon dark matter with a MADMAX prototype

Autor: Egge, J., Leppla-Weber, D., Knirck, S., Garcia, B. Ary dos Santos, Bergermann, D., Caldwell, A., Dabhi, V., Diaconu, C., Diehl, J., Dvali, G., Ekmedžić, M., Gallo, F., Garutti, E., Heyminck, S., Hubaut, F., Ivanov, A., Jochum, J., Karst, P., Kramer, M., Kreikemeyer-Lorenzo, D., Krieger, C., Lee, C., Lindner, A., Maldonado, J. P. A., Majorovits, B., Martens, S., Martini, A., Miyazaki, A., Öz, E., Pralavorio, P., Raffelt, G., Ringwald, A., Redondo, J., Roset, S., Salama, N., Schaffran, J., Schmidt, A., Steffen, F., Strandhagen, C., Usherov, I., Wang, H., Wieching, G., Cancelo, G., Di Federico, M., Hoshino, G., Stefanazzi, L.

We report the first result from a dark photon dark matter search in the mass range from ${78.62}$ to $83.95~\mathrm{\mu eV}/c^2$ with a dielectric haloscope prototype for MADMAX (Magnetized Disc and Mirror Axion eXperiment). Putative dark photons wou

Externí odkaz: http://arxiv.org/abs/2408.02368

In-Context In-Context Learning with Transformer Neural Processes

Autor: Ashman, Matthew, Diaconu, Cristiana, Weller, Adrian, Turner, Richard E.

Neural processes (NPs) are a powerful family of meta-learning models that seek to approximate the posterior predictive map of the ground-truth stochastic process from which each dataset in a meta-dataset is sampled. There are many cases in which prac

Externí odkaz: http://arxiv.org/abs/2406.13493