Publications

Order by: publication date number of citations

• FASER experiment and first results from LHC run 3

  Umut Kose, on behalf of FASER collaboration

  SciPost Phys. Proc. 17, 018 (2025) · published 23 July 2025 | Toggle abstract · pdf

  FASER is designed to search for light, extremely weakly interacting and long-lived beyond standard model particles at the CERN Large Hadron Collider. Such particles, e.g., dark photons, may be produced in the high-energy proton-proton collisions at the ATLAS interaction point and then decay to visible particles in FASER, which is placed 480 m downstream and aligned with the collision axis line-of-sight. The detector covers a previously unexplored range of pseudorapidity larger than 8.8, which allows it to have sensitivity to new physics in the far-forward region. FASER also has a sub-detector called FASER$\nu$, which is specifically designed to detect and investigate high-energy collider neutrino interactions in the TeV regime, extending current cross-section measurements. In this proceeding, the FASER detector and present recent results obtained during LHC Run 3 will be introduced.

• Status and prospects of the JUNO experiment

  Matthias Raphael Stock, on behalf of the JUNO collaboration

  SciPost Phys. Proc. 17, 020 (2025) · published 23 July 2025 | Toggle abstract · pdf

  The Jiangmen Underground Neutrino Observatory (JUNO) is a multi-purpose neutrino experiment currently under construction in China. It is located 52.5 km away from two nuclear power plants in a newly constructed 700-m-deep underground laboratory. JUNO will be the largest liquid scintillator (LS) detector in the world comprising 20 kt of ultra-pure LS filled in an acrylic sphere. Its main goal is to determine the neutrino mass ordering by measuring the energy spectrum of reactor neutrinos with highest accuracy. In addition, JUNO will cover precision measurements of oscillation parameters and several aspects in the field of astroparticle physics. Data taking will start in late 2024.

• TAMBO: Searching for tau neutrinos in the Peruvian Andes

  William G. Thompson, on behalf of the TAMBO collaboration.

  SciPost Phys. Proc. 17, 021 (2025) · published 23 July 2025 | Toggle abstract · pdf

  TAMBO is a planned next-generation neutrino observatory sensitive to 1-100 PeV Earth-skimming tau neutrinos. This tau neutrino specificity will provide a high-purity sample of astrophysical neutrinos whose locations on the sky can then be used to improve the sensitivities of all-flavor neutrino observatories. The observatory will be located in a deep canyon and will comprise an array of water-Cherenkov and plastic scintillator air-shower detectors. In this proceeding, I will summarize the current status of the updated TAMBO simulation package, which is currently being used for detector optimization and reconstruction studies.

• Measurement of the muon magnetic anomaly to 0.20 ppm by the Muon g-2 experiment at Fermilab

  Lorenzo Cotrozzi, on behalf of the Muon g-2 collaboration

  SciPost Phys. Proc. 17, 022 (2025) · published 23 July 2025 | Toggle abstract · pdf

  The Muon $g-2$ experiment at Fermilab aims to measure the muon magnetic moment anomaly, $a_{\mu}=(g-2)/2$, with a final accuracy of 0.14 parts per million (ppm). The experiment’s first result was published in 2021, based on data collected in 2018, and in 2023 a new result was published based on two more years of data taking, 2019 and 2020. The combination of the two results from Fermilab and the previous one from Brookhaven National Laboratory brought the uncertainty on the experimental measurement of $a_{\mu}$ to the unprecedented value of 0.19 ppm. This paper will present details about the improvements of statistical and systematic uncertainties on $a_{\mu}$ since the 2021 result.

• An alternative evaluation of the leading-order hadronic contribution to the muon $g-2$ with MUonE

  Riccardo N. Pilato

  SciPost Phys. Proc. 17, 024 (2025) · published 23 July 2025 | Toggle abstract · pdf

  The MUonE experiment proposes a novel approach to determine the hadronic contribution to the muon $g-2$, $a_\mu^\text{HLO}$, based on the measurement of the hadronic running of the QED coupling through the analysis of $\mu-e$ elastic scattering events. This could clarify the tensions in the current evaluations of $a_\mu^\text{HLO}$, which are limiting the comparison between theory and experiment for the muon $g-2$. The measurement will be performed at CERN’s North Area by scattering a 160 GeV muon beam on the atomic electrons of a low-Z target. The status and future plans of the experiment will be presented. Furthermore, an alternative method to extract $a_\mu^\text{HLO}$ from MUonE data will be discussed.

• A data-driven method to estimate the antiproton background in Mu2e

  Namitha Chithirasreemadam, Simone Donati, Pavel Murat, on behalf of the Mu2e collaboration

  SciPost Phys. Proc. 17, 025 (2025) · published 23 July 2025 | Toggle abstract · pdf

  The Mu2e experiment will search for the CLFV process of neutrinoless, coherent conversion of muon to electron in the field of an Al nucleus. One of the expected backgrounds is antiprotons produced by the proton beam at the Production Target and annihilating in the Stopping Target to produce signal-like electrons. Although not a dominant background, it has a large uncertainty and cannot be suppressed by the timing cuts used to reduce the prompt background. However, at Mu2e energies, $p\bar{p}$ annihilation is the only source of events with multiple, simultaneous particles coming from the Stopping Target. We utilized this unique feature and developed a novel approach to reconstruct multi-track events and estimate the antiproton background.

• Towards universal unfolding of detector effects in high-energy physics using denoising diffusion probabilistic models

  Camila Pazos, Shuchin Aeron, Pierre-Hugues Beauchemin, Vincent Croft, Zhengyan Huan, Martin Klassen, Taritree Wongjirad

  SciPost Phys. Core 8, 064 (2025) · published 2 October 2025 | Toggle abstract · pdf

  Correcting for detector effects in experimental data, particularly through unfolding, is critical for enabling precision measurements in high-energy physics. However, traditional unfolding methods face challenges in scalability, flexibility, and dependence on simulations. We introduce a novel approach to multidimensional object-wise unfolding using conditional Denoising Diffusion Probabilistic Models (cDDPM). Our method utilizes the cDDPM for a non-iterative, flexible posterior sampling approach, incorporating distribution moments as conditioning information, which exhibits a strong inductive bias that allows it to generalize to unseen physics processes without explicitly assuming the underlying distribution. Our results highlight the potential of this method as a step towards a "universal" unfolding tool that reduces dependence on truth-level assumptions, while enabling the unfolding of a wide range of measured distributions with improved adaptability and accuracy.

• Phase space compression of a positive muon beam in two spatial dimensions

  A. Antognini, N. J. Ayres, I. Belosevic, V. Bondar, A. Eggenberger, M. Hildebrandt, R. Iwai, K. Kirch, A. Knecht, G. Lospalluto, J. Nuber, A. Papa, M. Sakurai, I. Solovyev, D. Taqqu, T. Yan

  SciPost Phys. Core 8, 071 (2025) · published 20 October 2025 | Toggle abstract · pdf

  We present the first demonstration of simultaneous phase space compression in two spatial dimensions of a positive muon beam, the first stage of the novel high-brightness muon beam under development by the muCool collaboration at the Paul Scherrer Institute. The keV-energy, sub-mm size beam would enable a factor $10^5$ improvement in brightness for precision $\mu$SR, and atomic and particle physics measurements with positive muons. This compression is achieved within a cryogenic helium gas target with a strong density gradient, placed in a homogeneous magnetic field, under the influence of a complex electric field. In the next phase, the muon beam will be extracted into vacuum.

• Higgs production via vector-boson fusion at the LHC

  Gaetano Barone, Jiayi Chen, Stephane Cooperstein, Nikita Dolganov, Silvia Ferrario Ravasio, Yacine Haddad, Stefan Höche, Barbara Jäger, Alexander Karlberg, Alexander Mück, Mathieu Pellen, Christian T. Preuss, Daniel Reichelt, Simon Reinhardt, Marco Zaro

  SciPost Phys. Comm. Rep. 13 (2025) · published 5 November 2025 | Toggle abstract · pdf

  In this article, we summarise the recent experimental measurements and theoretical work on Higgs boson production via vector-boson fusion at the LHC. Along with this, we provide state-of-the-art predictions at fixed order as well as with parton-shower corrections within the Standard Model at $13.6\,\text{TeV}$. The results are presented in the form of multi-differential distributions as well as in the Simplified Template Cross Section bins. All materials and outputs of this study are available on public repositories. Finally, following findings in the literature, recommendations are made to estimate theoretical uncertainties related to parton-shower corrections.

• Learning selection cuts with gradients

  Mike Hance, Juan Robles

  SciPost Phys. Core 8, 079 (2025) · published 7 November 2025 | Toggle abstract · pdf

  Many analyses in high-energy physics rely on selection thresholds (cuts) applied to detector, particle, or event properties. Initial cut values can often be guessed from physical intuition, but cut optimization, especially for multiple features, is commonly performed by hand, or skipped entirely in favor of multivariate algorithms like BDTs or neural networks. We revisit this problem, and develop a cut optimization approach based on gradient descent. Cut thresholds are learned as parameters of a network with a simple architecture, and can be tuned to achieve a target signal efficiency through the use of custom loss functions. Contractive terms in the loss can be used to ensure a smooth evolution of cuts as functions of efficiency, particle kinematics, or event features. The method is used to classify events in a search for Supersymmetry, and the performance is compared with common classification tools. An implementation of this approach is available in a public code repository and python package.