Zobrazeno 1 - 10
of 1 199
pro vyhledávání: '"SHIH, DAVID"'
The Boltzmann equation relates the equilibrium phase space distribution of stars in the Milky Way to the Galaxy's gravitational potential. However, observations of stellar populations are biased by extinction from foreground dust, which complicates m
Externí odkaz:
http://arxiv.org/abs/2412.14236
Autor:
Amram, Oz, Anzalone, Luca, Birk, Joschka, Faroughy, Darius A., Hallin, Anna, Kasieczka, Gregor, Krämer, Michael, Pang, Ian, Reyes-Gonzalez, Humberto, Shih, David
Foundation models are deep learning models pre-trained on large amounts of data which are capable of generalizing to multiple datasets and/or downstream tasks. This work demonstrates how data collected by the CMS experiment at the Large Hadron Collid
Externí odkaz:
http://arxiv.org/abs/2412.10504
Autor:
Das, Ranit, Finke, Thorben, Hein, Marie, Kasieczka, Gregor, Krämer, Michael, Mück, Alexander, Shih, David
Resonant anomaly detection methods have great potential for enhancing the sensitivity of traditional bump hunt searches. A key component of these methods is a high quality background template used to produce an anomaly score. Using the LHC Olympics R
Externí odkaz:
http://arxiv.org/abs/2411.00085
Autor:
Krause, Claudius, Giannelli, Michele Faucci, Kasieczka, Gregor, Nachman, Benjamin, Salamani, Dalila, Shih, David, Zaborowska, Anna, Amram, Oz, Borras, Kerstin, Buckley, Matthew R., Buhmann, Erik, Buss, Thorsten, Cardoso, Renato Paulo Da Costa, Caterini, Anthony L., Chernyavskaya, Nadezda, Corchia, Federico A. G., Cresswell, Jesse C., Diefenbacher, Sascha, Dreyer, Etienne, Ekambaram, Vijay, Eren, Engin, Ernst, Florian, Favaro, Luigi, Franchini, Matteo, Gaede, Frank, Gross, Eilam, Hsu, Shih-Chieh, Jaruskova, Kristina, Käch, Benno, Kalagnanam, Jayant, Kansal, Raghav, Kim, Taewoo, Kobylianskii, Dmitrii, Korol, Anatolii, Korcari, William, Krücker, Dirk, Krüger, Katja, Letizia, Marco, Li, Shu, Liu, Qibin, Liu, Xiulong, Loaiza-Ganem, Gabriel, Madula, Thandikire, McKeown, Peter, Melzer-Pellmann, Isabell-A., Mikuni, Vinicius, Nguyen, Nam, Ore, Ayodele, Schweitzer, Sofia Palacios, Pang, Ian, Pedro, Kevin, Plehn, Tilman, Pokorski, Witold, Qu, Huilin, Raikwar, Piyush, Raine, John A., Reyes-Gonzalez, Humberto, Rinaldi, Lorenzo, Ross, Brendan Leigh, Scham, Moritz A. W., Schnake, Simon, Shimmin, Chase, Shlizerman, Eli, Soybelman, Nathalie, Srivatsa, Mudhakar, Tsolaki, Kalliopi, Vallecorsa, Sofia, Yeo, Kyongmin, Zhang, Rui
We present the results of the "Fast Calorimeter Simulation Challenge 2022" - the CaloChallenge. We study state-of-the-art generative models on four calorimeter shower datasets of increasing dimensionality, ranging from a few hundred voxels to a few t
Externí odkaz:
http://arxiv.org/abs/2410.21611
Autor:
Das, Ranit, Shih, David
A key step in any resonant anomaly detection search is accurate modeling of the background distribution in each signal region. Data-driven methods like CATHODE accomplish this by training separate generative models on the complement of each signal re
Externí odkaz:
http://arxiv.org/abs/2410.20537
Autor:
Drnevich, Jenny, Tan, Frederick J., Almeida-Silva, Fabricio, Castelo, Robert, Culhane, Aedin C., Davis, Sean, Doyle, Maria A., Holmes, Susan, Lahti, Leo, Mahmoud, Alexandru, Nishida, Kozo, Ramos, Marcel, Rue-Albrecht, Kevin, Shih, David J. H., Gatto, Laurent, Soneson, Charlotte
Modern biological research is increasingly data-intensive, leading to a growing demand for effective training in biological data science. In this article, we provide an overview of key resources and best practices available within the Bioconductor pr
Externí odkaz:
http://arxiv.org/abs/2410.01351
Autor:
Hallin, Anna, Kasieczka, Gregor, Kraml, Sabine, Lessa, André, Moureaux, Louis, von Schwartz, Tore, Shih, David
We develop a machine learning method for mapping data originating from both Standard Model processes and various theories beyond the Standard Model into a unified representation (latent) space while conserving information about the relationship betwe
Externí odkaz:
http://arxiv.org/abs/2407.20315
Publikováno v:
2024 JINST 19 P09003
In the quest to build generative surrogate models as computationally efficient alternatives to rule-based simulations, the quality of the generated samples remains a crucial frontier. So far, normalizing flows have been among the models with the best
Externí odkaz:
http://arxiv.org/abs/2405.20407
We present SkyCURTAINs, a data driven and model agnostic method to search for stellar streams in the Milky Way galaxy using data from the Gaia telescope. SkyCURTAINs is a weakly supervised machine learning algorithm that builds a background enriched
Externí odkaz:
http://arxiv.org/abs/2405.12131
There have been many applications of deep neural networks to detector calibrations and a growing number of studies that propose deep generative models as automated fast detector simulators. We show that these two tasks can be unified by using maximum
Externí odkaz:
http://arxiv.org/abs/2404.18992