Publications

Order by: publication date number of citations

• Generative unfolding with distribution mapping

  Anja Butter, Sascha Diefenbacher, Nathan Huetsch, Vinicius Mikuni, Benjamin Nachman, Sofia Palacios Schweitzer, Tilman Plehn

  SciPost Phys. 18, 200 (2025) · published 20 June 2025 | Toggle abstract · pdf

  Machine learning enables unbinned, highly-differential cross section measurements. A recent idea uses generative models to morph a starting simulation into the unfolded data. We show how to extend two morphing techniques, Schrödinger Bridges and Direct Diffusion, in order to ensure that the models learn the correct conditional probabilities. This brings distribution mapping (DM) to a similar level of accuracy as the state-of-the-art conditional generative unfolding methods. Numerical results are presented with a standard benchmark dataset of single jet substructure as well as for a new dataset describing a 22-dimensional phase space of $Z+2$-jets.

  3 citations

• Choose your diffusion: Efficient and flexible ways to accelerate the diffusion model in fast high energy physics simulation

  Cheng Jiang, Sitian Qian, Huilin Qu

  SciPost Phys. 18, 195 (2025) · published 18 June 2025 | Toggle abstract · pdf

  The diffusion model has demonstrated promising results in image generation, recently becoming mainstream and representing a notable advancement for many generative modeling tasks. Prior applications of the diffusion model for both fast event and detector simulation in high energy physics have shown exceptional performance, providing a viable solution to generate sufficient statistics within a constrained computational budget in preparation for the High Luminosity LHC. However, many of these applications suffer from slow generation with large sampling steps and face challenges in finding the optimal balance between sample quality and speed. The study focuses on the latest benchmark developments in efficient ODE/SDE-based samplers, schedulers, and fast convergence training techniques. We test on the public CaloChallenge and JetNet datasets with the designs implemented on the existing architecture, the performance of the generated classes surpass previous models, achieving significant speedup via various evaluation metrics.

  1 citation

• Snowmass 2021 cross frontier report: Dark matter complementarity

  Antonio Boveia, Mohamed Berkat, Thomas Y. Chen, Aman Desai, Caterina Doglioni, Alex Drlica-Wagner, Susan Gardner, Stefania Gori, Joshua Greaves, Patrick Harding, Philip C. Harris, W. Hugh Lippincott, Maria Elena Monzani, Katherine Pachal, Chanda Prescod-Weinstein, Gray Rybka, Bibhushan Shakya, Jessie Shelton, Tracy R. Slatyer, Amanda Steinhebel, Philip Tanedo, Natalia Toro, Yun-Tse Tsai, Mike Williams, Lindley Winslow, Jaehoon Yu, Tien-Tien Yu

  SciPost Phys. Comm. Rep. 7 (2025) · published 19 May 2025 | Toggle abstract · pdf

  The fundamental nature of Dark Matter is a central theme of the Snowmass 2021 process, extending across all frontiers. In the last decade, advances in detector technology, analysis techniques and theoretical modeling have enabled a new generation of experiments and searches while broadening the types of candidates we can pursue. Over the next decade, there is great potential for discoveries that would transform our understanding of dark matter. In the following, we outline a road map for discovery developed in collaboration among the frontiers. A strong portfolio of experiments that delves deep, searches wide, and harnesses the complementarity between techniques is key to tackling this complicated problem, requiring expertise, results, and planning from all Frontiers of the Snowmass 2021 process.

  2 citations

• New physics through flavor tagging at FCC-ee

  Admir Greljo, Hector Tiblom, Alessandro Valenti

  SciPost Phys. 18, 152 (2025) · published 12 May 2025 | Toggle abstract · pdf

  Leveraging recent advancements in machine learning-based flavor tagging, we develop an optimal analysis for measuring the hadronic cross-section ratios $R_b$, $R_c$, and $R_s$ at the FCC-ee during its $WW$, $Zh$, and $t\bar{t}$ runs. Our results indicate up to a two-order-of-magnitude improvement in precision, providing an unprecedented test of the SM. Using these observables, along with $R_\ell$ and $R_t$, we project sensitivity to flavor non-universal four-fermion (4F) interactions within the SMEFT, contributing both at the tree-level and through the renormalization group (RG). We highlight a subtle complementarity with RG-induced effects at the FCC-ee's $Z$-pole. Our analysis demonstrates significant improvements over the current LEP-II and LHC bounds in probing flavor-conserving 4F operators involving heavy quark flavors and all lepton flavors. As an application, we explore simplified models addressing current $B$-meson anomalies, demonstrating that FCC-ee can effectively probe the relevant parameter space. Finally, we design optimized search strategies for quark flavor-violating 4F interactions.

  9 citations

• MACK: Mismodeling addressed with contrastive knowledge

  Liam Rankin Sheldon, Dylan Sheldon Rankin, Philip Harris

  SciPost Phys. 18, 150 (2025) · published 6 May 2025 | Toggle abstract · pdf

  The use of machine learning methods in high energy physics typically relies on large volumes of precise simulation for training. As machine learning models become more complex they can become increasingly sensitive to differences between this simulation and the real data collected by experiments. We present a generic methodology based on contrastive learning which is able to greatly mitigate this negative effect. Crucially, the method does not require prior knowledge of the specifics of the mismodeling. While we demonstrate the efficacy of this technique using the task of jet-tagging at the Large Hadron Collider, it is applicable to a wide array of different tasks both in and out of the field of high energy physics.

  1 citation

• Identifying the quantum properties of hadronic resonances using machine learning

  Jakub Filipek, Shih-Chieh Hsu, John Kruper, Kirtimaan Mohan, Benjamin Nachman

  SciPost Phys. Core 8, 039 (2025) · published 28 April 2025 | Toggle abstract · pdf

  With the great promise of deep learning, discoveries of new particles at the Large Hadron Collider (LHC) may be imminent. Following the discovery of a new Beyond the Standard model particle in an all-hadronic channel, deep learning can also be used to identify its quantum numbers. Convolutional neural networks (CNNs) using jet-images can significantly improve upon existing techniques to identify the quantum chromodynamic (QCD) ('color') as well as the spin of a two-prong resonance using its substructure. Additionally, jet-images are useful in determining what information in the jet radiation pattern is useful for classification, which could inspire future taggers. These techniques improve the categorization of new particles and are an important addition to the growing jet substructure toolkit, for searches and measurements at the LHC now and in the future.

• Full event particle-level unfolding with variable-length latent variational diffusion

  Alexander Shmakov, Kevin Greif, Michael James Fenton, Aishik Ghosh, Pierre Baldi, Daniel Whiteson

  SciPost Phys. 18, 117 (2025) · published 1 April 2025 | Toggle abstract · pdf

  The measurements performed by particle physics experiments must account for the imperfect response of the detectors used to observe the interactions. One approach, unfolding, statistically adjusts the experimental data for detector effects. Recently, generative machine learning models have shown promise for performing unbinned unfolding in a high number of dimensions. However, all current generative approaches are limited to unfolding a fixed set of observables, making them unable to perform full-event unfolding in the variable dimensional environment of collider data. A novel modification to the variational latent diffusion model (VLD) approach to generative unfolding is presented, which allows for unfolding of high- and variable-dimensional feature spaces. The performance of this method is evaluated in the context of semi-leptonic $t\bar{t}$ production at the Large Hadron Collider.

  10 citations

• Codebase release 1.0 for Kolya

  Matteo Fael, Ilija S. Milutin, K. Keri Vos

  SciPost Phys. Codebases 55-r1.0 (2025) · published 25 March 2025 | Toggle abstract · src

  We introduce the code kolya, an open-source tool for phenomenological analyses of inclusive semileptonic $B\to X_c l\bar \nu_l$ decays. It contains a library to compute predictions for the total rate and various kinematic moments within the framework of the heavy quark expansion, utilizing the so-called kinetic scheme. The library currently includes power corrections up to $1/m_b^5$. All available QCD perturbative corrections are implemented via interpolation grids for fast numerical evaluation. We also include effects from new physics parameterized as Wilson coefficients of dimension-six operators in the weak effective theory below the electroweak scale. The library is interfaced to CRunDec for easy evaluation of the quark masses and strong coupling constant at different renormalization scales. The library is developed in Python and does not require compilation. The code is flexible and can be extended to other kinds of inclusive decays such as $B$ meson lifetimes, $B \to X_u l\bar \nu_l$ and $D$ meson decays.

  1 citation

• Kolya: An open-source package for inclusive semileptonic B decays

  Matteo Fael, Ilija S. Milutin, K. Keri Vos

  SciPost Phys. Codebases 55 (2025) · published 25 March 2025 | Toggle abstract · pdf

  We introduce the code kolya, an open-source tool for phenomenological analyses of inclusive semileptonic $B\to X_c l\bar \nu_l$ decays. It contains a library to compute predictions for the total rate and various kinematic moments within the framework of the heavy quark expansion, utilizing the so-called kinetic scheme. The library currently includes power corrections up to $1/m_b^5$. All available QCD perturbative corrections are implemented via interpolation grids for fast numerical evaluation. We also include effects from new physics parameterized as Wilson coefficients of dimension-six operators in the weak effective theory below the electroweak scale. The library is interfaced to CRunDec for easy evaluation of the quark masses and strong coupling constant at different renormalization scales. The library is developed in Python and does not require compilation. The code is flexible and can be extended to other kinds of inclusive decays such as $B$ meson lifetimes, $B \to X_u l\bar \nu_l$ and $D$ meson decays.

  1 citation

• CaloDREAM – Detector response emulation via attentive flow matching

  Luigi Favaro, Ayodele Ore, Sofia Palacios Schweitzer, Tilman Plehn

  SciPost Phys. 18, 088 (2025) · published 11 March 2025 | Toggle abstract · pdf

  Detector simulations are an exciting application of modern generative networks. Their sparse high-dimensional data combined with the required precision poses a serious challenge. We show how combining Conditional Flow Matching with transformer elements allows us to simulate the detector phase space reliably. Namely, we use an autoregressive transformer to simulate the energy of each layer, and a vision transformer for the high-dimensional voxel distributions. We show how dimension reduction via latent diffusion allows us to train more efficiently and how diffusion networks can be evaluated faster with bespoke solvers. We showcase our framework, CaloDREAM, on datasets 2 and 3 of the CaloChallenge.

  12 citations