"au:"Alexander Moreno Briceño""

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Building an AI-native Research Ecosystem for Experimental Particle Physics: A Community Vision

Thea Klaeboe Aarrestad, Alaa Abdelhamid, Haider Abidi, Jahred Adelman, Jennifer Adelman-McCarthy, Shuchin Aeron, Garvita Agarwal, Usman Ali, Cristiano Alpigiani, Omar Alterkait, Mohamed Aly, Oz Amram, Saeed Ansari Fard, Aram Apyan, John Arrington, Marvin Ascencio-Sosa, Mohammad Atif, Aneesha Avasthi, Muhammad Bilal Azam, Bhim Bam, Joshua Barrow, Rainer Bartoldus, Amit Bashyal, Aashwin Basnet, Ayse Bat, Lothar A. T. Bauerdick, John Beacom, Chris Bee, Michael Begel, Matthew Bellis, Rene Bellwied, Rakitha Beminiwattha, Gabriele Benelli, Douglas Benjamin, Catrin Bernius, Binod Bhandari, Avinay Bhat, Meghna Bhattacharya, Saptaparna Bhattacharya, Prajita Bhattarai, Sudip Bhattarai, Wahid Bhimji, Jianming Bian, Burak Bilki, Mary Bishai, Kevin Black, Kenneth Bloom, Brian Bockelman, Johan Sebastian Bonilla Castro, Tulika Bose, Nilay Bostan, Othmane Bouhali, Dimitri Bourilkov, Dominic Brailsford, Gustaaf Brooijmans, Elizabeth Brost, Maria Brigida Brunetti, Quentin Buat, Brendon Bullard, Jackson Burzynski, Paolo Calafiura, Rodolfo Capdevilla, Fabian Andres Castaño Usuga, Raquel Castillo Fernandez, Fabio Catalano, Viviana Cavaliere, Flavio Cavanna, Giuseppe Cerati, Aidan Chambers, Maria Chamizo-Llatas, Philip Chang, Andrew Chappell, Arghya Chattopadhyay, Sergei Chekanov, Jian-ping Chen, Yi Chen, Zhengyang Chen, J. Taylor Childers, Hector Chinchay, Yuan-Tang Chou, Tasnuva Chowdhury, Neil Christensen, Wonyong Chung, Rafael Coelho Lopes de Sa, Simon Corrodi, Kyle Cranmer, Matteo Cremonesi, Roy Cruz, Mate Csanad, Mariarosaria D'Alfonso, Carlo Dallapiccola, Daine Danielson, Sridhara Dasu, Gavin Davies, Kaushik De, Patrick de Perio, Klaus Dehmelt, Marco Del Tutto, Carlos Ruben Dell'Aquila, Sarah Demers, Paolo Desiati, Bhesha Devkota, Sparshita Dey, Ranjan Dharmapalan, Karri Folan Di Petrillo, Markus Diefenthaler, Jeff Dillon, Zelimir Djurcic, Caterina Doglioni, Francois Drielsma, Edmond Dukes, Irene Dutta, Peter Elmer, Johannes Elmsheuser, Victor Daniel Elvira, Harold Evans, Peter Fackeldey, Cristiano Fanelli, Hao Fang, Mattia Fani, Muhammad Farooq, Matthew Feickert, Ian Fisk, Sam Foreman, Alexander Friedland, Nuwan Chaminda G. W., Louis-Guillaume Gagnon, Massimiliano Galli, Abhijith Gandrakota, Sudeshna Ganguly, Arianna Garcia Caffaro, Rob Gardner, Rocky Bala Garg, Lino Gerlach, Aishik Ghosh, Romulus Godang, Julia Gonski, Loukas Gouskos, Richard Gran, Heather Gray, Andrei Gritsan, Gaia Grosso, Craig Group, Jiawei Guo, Shubham Gupta, Gajendra Gurung, Phillip Gutierrez, Oliver Gutsche, Tyler Hague, Joseph Haley, Eva Halkiadakis, Francis Halzen, Michael Hance, Philip Harris, Harry Hausner, Karsten Heeger, Lukas Heinrich, Alexander Held, Matthew Herndon, Ken Herner, Max Herrmann, David Hertzog, Christian Herwig, Aaron Higuera, Alexander Himmel, Timothy Hobbs, Stefan Hoeche, Tova Holmes, Tae Min Hong, Ben Hooberman, Walter Hopkins, Jessica N. Howard, Shih-Chieh Hsu, Fengping Hu, Patrick Huber, Dirk Hufnagel, Daniel Humphreys, Ia Iashvili, Joseph Incandela, Josh Isaacson, Wasikul Islam, Kirill Ivanov, Wooyoung Jang, Naomi Jarvis, Brij Kishor Jashal, Pratik Jawahar, Dulitha Jayakodige, Torri Jeske, Sergo Jindariani, Jay Hyun Jo, Bhishm Shankar Joshi, Xiangyang Ju, Andreas Jung, Thomas Junk, Michael Kagan, Daisy Kalra, Matthias Kaminski, Edward Karavakis, Stefan Katsarov, Stergios Kazakos, Paul King, Michael Kirby, Max Knobbe, Young Ju Ko, Dmitry Kondratyev, Rostislav Konoplich, Charis Koraka, Scott Kravitz, Lukas Kretschmann, Brandon Kriesten, Georgios K Krintiras, Iason Krommydas, Michelle Kuchera, Audrey Kvam, Martin Kwok, Theodota Lagouri, Sabine Lammers, Eric Lancon, Greg Landsberg, David Lange, Kevin Lannon, Joseph Lau, Luca Lavezzo, Benjamin Lawrence-Sanderson, Duc-Truyen Le, Matt LeBlanc, Sung-Won Lee, Trevin Lee, Charles Leggett, Kayla Leonard DeHolton, James Letts, Hao Li, Haoyang Li, Aklima Khanam Lima, Guilherme Lima, Mia Liu, Qibin Liu, Yinrui Liu, Zhen Liu, Shivani Lomte, Guillermo Loustau de Linares, Lu Lu, Pietro Lugato, Adam Lyon, Yang Ma, Christopher Madrid, Akhtar Mahmood, Kendall Mahn, Devin Mahon, Akshay Malige, Sudhir Malik, Abhishikth Mallampalli, Yurii Maravin, Ralph Marinaro, Pete Markowitz, Matthew Maroun, Kyla Martinez, Verena Ingrid Martinez Outschoorn, Sanjit Masanam, Mario Masciovecchio, Konstantin Matchev, Malek Mazouz, Simone Mazza, Thomas McCauley, Shawn McKee, Karim Mehrabi, Poonam Mehta, Andrew Melo, Mark Messier, Elias Mettner, Christopher Meyer, Jessie Micallef, Sophie Middleton, David W. Miller, Hamlet Mkrtchyan, Abdollah Mohammadi, Abhinav Mohan, Ajit Mohapatra, Farouk Mokhtar, Peter Monaghan, Claudio Silverio Montanari, Michael Mooney, Casey Morean, Eric Moreno, Alexander Moreno Briceño, Stephen Mrenna, Justin Mueller, Daniel Murnane, Benjamin Nachman, Emilio Nanni, Nitish Nayak, Miquel Nebot-Guinot, Orgho Neogi, Chris Neu, Mark Neubauer, Norbert Neumeister, Harvey Newman, Duong Nguyen, Gavin Niendorf, Paul Nilsson, Scarlet Norberg, Andrzej Novak, Sungbin Oh, Isabel Ojalvo, Olaiya Olokunboyo, Yasar Onel, Joseph Osborn, Ianna Osborne, Arantza Oyanguren, Nurcan Ozturk, Paul Padley, Simone Pagan Griso, Pritam Palit, Bishnu Pandey, Vishvas Pandey, Zisis Papandreou, Ganesh Parida, Ki Ryeong Park, Ajib Paudel, Manfred Paulini, Christoph Paus, Gregory Pawloski, Kevin Pedro, Gabriel Perdue, Troels Christian Petersen, Alexey Petrov, Deborah Pinna, Marc-André Pleier, Andrea Pocar, Prafull Purohit, Nived Puthumana Meleppattu, Mateusz Płoskoń, Sitian Qian, Xin Qian, Geting Qin, Aleena Rafique, Srini Rajagopalan, Dylan Rankin, Rebecca Rapp, Salvatore Rappoccio, Rohit Raut, Sagar Regmi, Benedikt Riedel, Andres Rios-Tascon, Stephen Roche, Jenna Roderick, Rimsky Rojas, Dmitry Romanov, Subhojit Roy, Rita Sadek, Dikshant Sagar, Nihar Ranjan Sahoo, Tai Sakuma, Juan Pablo Salas, Mayly Sanchez, Jay Sandesara, Alexander Savin, Ryan Schmitz, Kate Scholberg, Henry Schreiner, Reinhard Schwienhorst, Gabriella Sciolla, Saba Sehrish, Seon-Hee, Seo, Elizabeth Sexton-Kennedy, Oksana Shadura, Bijaya Sharma, Varun Sharma, Suyog Shrestha, Ryan Simeon, Jack Simoni, Siddharth Singh, Kim Siyeon, Louise Skinnari, Jinseop Song, Simone Sottocornola, Alexandre Sousa, Sairam Sri Vatsavai, Giordon Stark, Justin Stevens, Tyler Stokes, Nadja Strobbe, Indara Suarez, Manjukrishna Suresh, Andrew Sutton, Holly Szumila-Vance, Vardan Tadevosyan, Anyes Taffard, Buddhiman Tamang, Hirohisa Tanaka, Erdinch Tatar, Abdel Nasser Tawfik, Vikas Teotia, Kazuhiro Terao, Mitanshu Thakore, Jesse Thaler, Ameya Thete, Inar Timiryasov, Anthony Timmins, Andrew Toler, Dat Tran, Nhan Tran, Patrick Tsang, Ho Fung Tsoi, Vakho Tsulaia, Pham Tuan, Christopher Tully, Shengquan Tuo, Richard Tyson, Darren Upton, Hilary Utaegbulam, Zoya Vallari, Peter van Gemmeren, Vassil Vassilev, Nikhilesh Venkatasubramanian, Renzo Vizarreta, Emmanouil Vourliotis, Ilija Vukotic, Carl Vuosalo, Liv Våge, Tammy Walton, Linyan Wan, Biao Wang, Gensheng Wang, Michael Wang, Yuxuan Wang, Gordon Watts, Yingjie Wei, Derek Weitzel, Shawn Westerdale, Andrew White, Leigh Whitehead, Michael Wilking, Mike Williams, Stephane Willocq, Jeffery Winkler, Frank Winklmeier, Holger Witte, Peter Wittich, Douglas Wright, Yongcheng Wu, Yujun Wu, Wei Xie, Fang Xu, Barbara Yaeggy, Zhen Yan, Liang Yang, Wei Yang, Alejandro Yankelevich, Yiheng Ye, Oguzhan Yer, Efe Yigitbasi, Shin-Shan Yu, Jon Zarling, Chao Zhang, Licheng Zhang, Larry Zhao, Junjie Zhu, Jure Zupan

Feb 19, 2026·hep-ex·PDF

Experimental particle physics seeks to understand the universe by probing its fundamental particles and forces and exploring how they govern the large-scale processes that shape cosmic evolution. This whitepaper presents a vision for how Artificial Intelligence (AI) can accelerate discovery in this field. We outline grand challenges that must be addressed to enable transformative breakthroughs and describe how current and planned experimental facilities can implement this vision to advance our understanding of the vast and complex physical world from the smallest to the largest scales. We show how facilities currently under construction, such as the HL-LHC, DUNE and soon EIC, can both benefit from and serve as proving grounds for this vision, while also enabling a longer-term goal for how future experiments -- like FCC-ee at CERN, IceCube-Gen2, a Muon Collider in the U.S., and smaller to mid-scale projects -- can be fully AI-native. We describe how a truly national-scale collaboration, jointly managed across large funding partners, and involving both DOE laboratories and universities, can make this happen.

Probing the Type I Seesaw Mechanism with Displaced Vertices at the LHC

Alberto M. Gago, Pilar Hernández, Joel Jones-Pérez, Marta Losada, Alexander Moreno Briceño

May 21, 2015·hep-ph·PDF

The observation of Higgs decays into heavy neutrinos would be strong evidence for new physics associated to neutrino masses. In this work we propose a search for such decays within the Type I seesaw model in the few-GeV mass range via displaced vertices. Using 300 fb$^{-1}$ of integrated luminosity, at 13 TeV, we explore the region of parameter space where such decays are measurable. We show that, after imposing pseudorapidity cuts, there still exists a region where the number of events is larger than $\mathcal{O}(10)$. We also find that conventional triggers can greatly limit the sensitivity of our signal, so we display several relevant kinematical distributions which might aid in the optimization of a dedicated trigger selection.

Julia in HEP

Graeme Andrew Stewart, Alexander Moreno Briceño, Philippe Gras, Benedikt Hegner, Uwe Hernandez Acosta, Tamas Gal, Jerry Ling, Pere Mato, Mikhail Mikhasenko, Oliver Schulz, Sam Skipsey

Mar 11, 2025·hep-ex·PDF

Julia is a mature general-purpose programming language, with a large ecosystem of libraries and more than 12000 third-party packages, which specifically targets scientific computing. As a language, Julia is as dynamic, interactive, and accessible as Python with NumPy, but achieves run-time performance on par with C/C++. In this paper, we describe the state of adoption of Julia in HEP, where momentum has been gathering over a number of years. HEP-oriented Julia packages can already, via UnROOT.jl, read HEP's major file formats, including TTree and RNTuple. Interfaces to some of HEP's major software packages, such as through Geant4.jl, are available too. Jet reconstruction algorithms in Julia show excellent performance. A number of full HEP analyses have been performed in Julia. We show how, as the support for HEP has matured, developments have benefited from Julia's core design choices, which makes reuse from and integration with other packages easy. In particular, libraries developed outside HEP for plotting, statistics, fitting, and scientific machine learning are extremely useful. We believe that the powerful combination of flexibility and speed, the wide selection of scientific programming tools, and support for all modern programming paradigms and tools, make Julia the ideal choice for a future language in HEP.

Training on Data Analysis Reproducibility via Containerization with Apptainer

Roy Cruz Candelaria, Wouter Deconinck, Aman Desai, Guillermo Fidalgo Rodríguez, Michel Hernandez Villanueva, Kilian Lieret, Valeriia Lukashenko, Sudhir Malik, Marco Mambelli, Tetiana Mazurets, Alexander Moreno Briceño, Andres Rios-Tascon, Richa Sharma

Apr 5, 2026·physics.ed-ph·PDF

We present the material and resources developed for training physicists on containerization technologies enabled by Apptainer. In the context of analysis preservation using Apptainer's capabilities, we have developed examples that execute common tools in High Energy Physics (HEP) and Nuclear Physics within containers. Training physicists on containerization technologies is of utmost importance in today's research landscape. By embracing these technologies, users can achieve enhanced reproducibility, portability, collaboration, and resource efficiency, assuring the conditions and integrity of the scientific analysis process. This training module,``Introduction to Apptainer/Singularity'', is part of the HEP Software Foundation Training Center, which aims to equip newcomers to the field of High Energy Physics with the necessary software skills and best practices.

The Critical Importance of Software for HEP

HEP Software Foundation, :, Christina Agapopoulou, Claire Antel, Saptaparna Bhattacharya, Steven Gardiner, Krzysztof L. Genser, James Andrew Gooding, Alexander Held, Michel Hernandez Villanueva, Michel Jouvin, Tommaso Lari, Valeriia Lukashenko, Sudhir Malik, Alexander Moreno Briceño, Stephen Mrenna, Inês Ochoa, Joseph D. Osborn, Jim Pivarski, Alan Price, Eduardo Rodrigues, Richa Sharma, Nicholas Smith, Graeme Andrew Stewart, Anna Zaborowska, Dirk Zerwas, Maarten van Veghel

Apr 1, 2025·hep-ex·PDF

Particle physics has an ambitious and broad global experimental programme for the coming decades. Large investments in building new facilities are already underway or under consideration. Scaling the present processing power and data storage needs by the foreseen increase in data rates in the next decade for HL-LHC is not sustainable within the current budgets. As a result, a more efficient usage of computing resources is required in order to realise the physics potential of future experiments. Software and computing are an integral part of experimental design, trigger and data acquisition, simulation, reconstruction, and analysis, as well as related theoretical predictions. A significant investment in computing and software is therefore critical. Advances in software and computing, including artificial intelligence (AI) and machine learning (ML), will be key for solving these challenges. Making better use of new processing hardware such as graphical processing units (GPUs) or ARM chips is a growing trend. This forms part of a computing solution that makes efficient use of facilities and contributes to the reduction of the environmental footprint of HEP computing. The HEP community already provided a roadmap for software and computing for the last EPPSU, and this paper updates that, with a focus on the most resource critical parts of our data processing chain.

t-channel dark matter at the LHC -- a whitepaper

Chiara Arina, Benjamin Fuks, Luca Panizzi, Michael J. Baker, Alan S. Cornell, Jan Heisig, Benedikt Maier, Rute Pedro, Dominique Trischuk, Diyar Agin, Alexandre Arbey, Giorgio Arcadi, Emanuele Bagnaschi, Kehang Bai, Disha Bhatia, Mathias Becker, Alexander Belyaev, Ferdinand Benoit, Monika Blanke, Jackson Burzynski, Jonathan M. Butterworth, Antimo Cagnotta, Lorenzo Calibbi, Linda M. Carpenter, Xabier Cid Vidal, Emanuele Copello, Louie Corpe, Francesco D'Eramo, Aldo Deandrea, Aman Desai, Caterina Doglioni, Sunil M. Dogra, Mathias Garny, Mark D. Goodsell, Sohaib Hassan, Philip Coleman Harris, Julia Harz, Alejandro Ibarra, Alberto Orso Maria Iorio, Felix Kahlhoefer, Deepak Kar, Shaaban Khalil, Valery Khoze, Pyungwon Ko, Sabine Kraml, Greg Landsberg, Andre Lessa, Laura Lopez-Honorez, Alberto Mariotti, Vasiliki A. Mitsou, Kirtimaan Mohan, Chang-Seong Moon, Alexander Moreno Briceño, María Moreno Llácer, Léandre Munoz-Aillaud, Taylor Murphy, Anele M. Ncube, Wandile Nzuza, Clarisse Prat, Lena Rathmann, Thobani Sangweni, Dipan Sengupta, William Shepherd, Sukanya Sinha, Tim M. P. Tait, Andrea Thamm, Michel H. G. Tytgat, Zirui Wang, David Yu, Shin-Shan Yu

Apr 14, 2025·hep-ph·PDF

This report, summarising work achieved in the context of the LHC Dark Matter Working Group, investigates the phenomenology of $t$-channel dark matter models, spanning minimal setups with a single dark matter candidate and mediator to more complex constructions closer to UV-complete models. For each considered class of models, we examine collider, cosmological and astrophysical implications. In addition, we explore scenarios with either promptly decaying or long-lived particles, as well as featuring diverse dark matter production mechanisms in the early universe. By providing a unified analysis framework, numerical tools and guidelines, this work aims to support future experimental and theoretical efforts in exploring $t$-channel dark matter models at colliders and in cosmology.

First Look at the Physics Case of TLEP

M. Bicer, H. Duran Yildiz, I. Yildiz, G. Coignet, M. Delmastro, T. Alexopoulos, C. Grojean, S. Antusch, T. Sen, H. -J. He, K. Potamianos, S. Haug, A. Moreno, A. Heister, V. Sanz, G. Gomez-Ceballos, M. Klute, M. Zanetti, L. -T. Wang, M. Dam, C. Boehm, N. Glover, F. Krauss, A. Lenz, M. Syphers, C. Leonidopoulos, V. Ciulli, P. Lenzi, G. Sguazzoni, M. Antonelli, M. Boscolo, U. Dosselli, O. Frasciello, C. Milardi, G. Venanzoni, M. Zobov, J. van der Bij, M. de Gruttola, D. -W. Kim, M. Bachtis, A. Butterworth, C. Bernet, C. Botta, F. Carminati, A. David, D. d'Enterria, L. Deniau, G. Ganis, B. Goddard, G. Giudice, P. Janot, J. M. Jowett, C. Lourenco, L. Malgeri, E. Meschi, F. Moortgat, P. Musella, J. A. Osborne, L. Perrozzi, M. Pierini, L. Rinolfi, A. de Roeck, J. Rojo, G. Roy, A. Sciaba, A. Valassi, C. S. Waaijer, J. Wenninger, H. Woehri, F. Zimmermann, A. Blondel, M. Koratzinos, P. Mermod, Y. Onel, R. Talman, E. Castaneda Miranda, E. Bulyak, D. Porsuk, D. Kovalskyi, S. Padhi, P. Faccioli, J. R. Ellis, M. Campanelli, Y. Bai, M. Chamizo, R. B. Appleby, H. Owen, H. Maury Cuna, C. Gracios, G. A. Munoz-Hernandez, L. Trentadue, E. Torrente-Lujan, S. Wang, D. Bertsche, A. Gramolin, V. Telnov, M. Kado, P. Petroff, P. Azzi, O. Nicrosini, F. Piccinini, G. Montagna, F. Kapusta, S. Laplace, W. da Silva, N. Gizani, N. Craig, T. Han, C. Luci, B. Mele, L. Silvestrini, M. Ciuchini, R. Cakir, R. Aleksan, F. Couderc, S. Ganjour, E. Lancon, E. Locci, P. Schwemling, M. Spiro, C. Tanguy, J. Zinn-Justin, S. Moretti, M. Kikuchi, H. Koiso, K. Ohmi, K. Oide, G. Pauletta, R. Ruiz de Austri, M. Gouzevitch, S. Chattopadhyay

Aug 28, 2013·hep-ex·PDF

The discovery by the ATLAS and CMS experiments of a new boson with mass around 125 GeV and with measured properties compatible with those of a Standard-Model Higgs boson, coupled with the absence of discoveries of phenomena beyond the Standard Model at the TeV scale, has triggered interest in ideas for future Higgs factories. A new circular e+e- collider hosted in a 80 to 100 km tunnel, TLEP, is among the most attractive solutions proposed so far. It has a clean experimental environment, produces high luminosity for top-quark, Higgs boson, W and Z studies, accommodates multiple detectors, and can reach energies up to the t-tbar threshold and beyond. It will enable measurements of the Higgs boson properties and of Electroweak Symmetry-Breaking (EWSB) parameters with unequalled precision, offering exploration of physics beyond the Standard Model in the multi-TeV range. Moreover, being the natural precursor of the VHE-LHC, a 100 TeV hadron machine in the same tunnel, it builds up a long-term vision for particle physics. Altogether, the combination of TLEP and the VHE-LHC offers, for a great cost effectiveness, the best precision and the best search reach of all options presently on the market. This paper presents a first appraisal of the salient features of the TLEP physics potential, to serve as a baseline for a more extensive design study.

Remarks on Semileptonic B and D Decays into Orbitally Excited Mesons

H. B. Mayorga, A. Moreno Briceno, J. H. Munoz

Sep 3, 2002·hep-ph·PDF

We have obtained the differential decay rate and calculated the branching ratios of the exclusive semileptonic decays $B(D) \to Xlν$, where $X$ is a p-wave meson, using the nonrelativistic ISGW quark model. Our results are compared with the predictions of the ISGW2 model. We have computed some branching ratios that were not reported or were reported with 0.00 in this model. For example, we find that $Br(B_c^- \to \bar{B_{s2}^{*0}}l^-\barν) = 4.03 \times 10^{-5}$, $Br(B_c^- \to \bar{B_2^{*0}}l^- \barν) =3.65 \times 10^{-6}$ and $Br(D_s^+ \to f_2l^+ν) = 2.7 \times 10^{-5}$, which seems to be at the reach of forthcoming experiments. Furthermore, we have classified the $B_{u,d,s} \to Tlν$ decays in two groups and compared the semileptonic and nonleptonic decays including a tensor meson in the final state.

Building an AI-native Research Ecosystem for Experimental Particle Physics: A Community Vision

Feb 19, 2026·hep-ex·PDF