Activities

Workshop and Conferences

KISS Seminars

We hold virtual seminars KISS seminars regularily:

Publications

  • New angles on fast calorimeter shower simulation Sascha Diefenbacher (Hamburg U.), Engin Eren (DESY), Frank Gaede (DESY), Gregor Kasieczka (Hamburg U. and DESY), Anatolii Korol (DESY) et al. e-Print: 2303.18150 DOI: 10.1088/2632-2153/acefa9 Published in: Mach.Learn.Sci.Tech. 4 (2023) 3, 035044

  • CaloClouds: fast geometry-independent highly-granular calorimeter simulation, Erik Buhmann (Hamburg U.), Sascha Diefenbacher (Hamburg U. and LBL, Berkeley), Engin Eren (DESY), Frank Gaede (DESY), Gregor Kasieczka (Hamburg U. and DESY) et al., e-Print: 2305.04847, DOI: 10.1088/1748-0221/18/11/P11025, Published in: JINST 18 (2023) 11, P11025

  • CaloClouds II: ultra-fast geometry-independent highly-granular calorimeter simulation, Erik Buhmann (Hamburg U.), Frank Gaede (DESY), Gregor Kasieczka (Hamburg U. and DESY), Anatolii Korol (DESY), William Korcari (Hamburg U.) et al., e-Print: 2309.05704, DOI: 10.1088/1748-0221/19/04/P04020, Published in: JINST 19 (2024) 04, P04020

  • EPiC-ly Fast Particle Cloud Generation with Flow-Matching and Diffusion, Erik Buhmann (Hamburg U.), Cedric Ewen (Hamburg U.), Darius A. Faroughy (Rutgers U., Piscataway), Tobias Golling (Geneva U.), Gregor Kasieczka (Hamburg U. and Korea Inst. Advanced Study, Seoul) et al., e-Print: 2310.00049 [hep-ph]

  • Flow Matching Beyond Kinematics: Generating Jets with Particle-ID and Trajectory Displacement Information, Joschka Birk (Hamburg U.), Erik Buhmann (Hamburg U.), Cedric Ewen (Hamburg U.), Gregor Kasieczka (Hamburg U.), David Shih (Rutgers U., Piscataway), e-Print: 2312.00123 [hep-ph]

  • Vičánek Martínez, T., Baron Perez, N., and Brüggen, M., “Simulating images of radio galaxies with diffusion models”, Astronomy and Astrophysics, vol. 691, Art. no. A360, EDP, 2024. doi:10.1051/0004-6361/202451429.

  • Geyer, F., “Deep-learning-based radiointerferometric imaging with GAN-aided training”, Astronomy and Astrophysics, vol. 677, Art. no. A167, EDP, 2023. doi:10.1051/0004-6361/202347073.

  • Convolutional L2LFlows: generating accurate showers in highly granular calorimeters using convolutional normalizing flows, Thorsten Buss (Hamburg U. and DESY), Frank Gaede (DESY), Gregor Kasieczka (Hamburg U. and DESY), Claudius Krause (Heidelberg U. and Vienna, OAW), David Shih (Rutgers U., Piscataway), e-Print: 2405.20407, DOI: 10.1088/1748-0221/19/09/P09003, Published in: JINST 19 (2024) 09, P09003, JINST 19, P09003

  • Calibrating Bayesian generative machine learning for Bayesiamplification, Sebastian Bieringer, Sascha Diefenbacher, Gregor Kasieczka, Mathias Trabs, e-Print: 2408.00838, DOI: 10.1088/2632-2153/ad9136, Published in: Mach.Learn.Sci.Tech. 5 (2024) 4, 045044, Mach.Learn.Sci.Tech. 5, 045044

  • How to understand limitations of generative networks, Ranit Das (Rutgers U., Piscataway), Luigi Favaro (U. Heidelberg, ITP), Theo Heimel (U. Heidelberg, ITP), Claudius Krause (U. Heidelberg, ITP), Tilman Plehn (U. Heidelberg, ITP) et al., e-Print: 2305.16774, DOI: 10.21468/SciPostPhys.16.1.031, Published in: SciPost Phys. 16 (2024) 1, 031, SciPost Phys. 16 (2024), 031

  • Precision-machine learning for the matrix element method, Theo Heimel (U. Heidelberg, ITP), Nathan Huetsch (U. Heidelberg, ITP), Ramon Winterhalder (Louvain U., CP3), Tilman Plehn (U. Heidelberg, ITP), Anja Butter (U. Heidelberg, ITP and LPNHE, Paris), e-Print: 2310.07752, DOI: 10.21468/SciPostPhys.17.5.129, Published in: SciPost Phys. 17 (2024) 5, 129

  • The MadNIS reloaded, Theo Heimel (U. Heidelberg, ITP), Nathan Huetsch (U. Heidelberg, ITP), Fabio Maltoni (Louvain U., CP3 and INFN, Bologna and U. Bologna, DIFA), Olivier Mattelaer (Louvain U., CP3), Tilman Plehn (U. Heidelberg, ITP) et al., e-Print: 2311.01548, DOI: 10.21468/SciPostPhys.17.1.023, Published in: SciPost Phys. 17 (2024) 1, 023, SciPost Phys. 17 (2024), 023

  • Kicking it off(-shell) with direct diffusion, Anja Butter (Heidelberg U. and LPNHE, Paris), Tomas Jezo (Munster U., ITP), Michael Klasen (Munster U., ITP), Mathias Kuschick (Munster U., ITP), Sofia Palacios Schweitzer (Heidelberg U.) et al., e-Print: 2311.17175, DOI: 10.21468/SciPostPhysCore.7.3.064, Published in: SciPost Phys.Core 7 (2024) 3, 064

  • Normalizing Flows for High-Dimensional Detector Simulations, Florian Ernst (U. Heidelberg, ITP and CERN), Luigi Favaro (U. Heidelberg, ITP), Claudius Krause (U. Heidelberg, ITP and Vienna, OAW), Tilman Plehn (U. Heidelberg, ITP), David Shih (Rutgers U., Piscataway), e-Print: 2312.09290, DOI: 10.21468/SciPostPhys.18.3.081 (publication), Published in: SciPost Phys. 18 (2025), 081

  • The Landscape of Unfolding with Machine Learning, Nathan Huetsch (U. Heidelberg, ITP), Javier Mariño Villadamigo (U. Heidelberg, ITP), Alexander Shmakov (UC, Irvine), Sascha Diefenbacher (LBL, Berkeley), Vinicius Mikuni (LBL, Berkeley) et al., e-Print: 2404.18807, DOI: 10.21468/SciPostPhys.18.2.070 (publication), Published in: SciPost Phys. 18 (2025), 070

  • CaloDREAM – Detector Response Emulation via Attentive flow Matching, Luigi Favaro (U. Heidelberg, ITP), Ayodele Ore (U. Heidelberg, ITP), Sofia Palacios Schweitzer (U. Heidelberg, ITP), Tilman Plehn (U. Heidelberg, ITP), e-Print: 2405.09629, DOI: 10.21468/SciPostPhys.18.3.088 (publication), Published in: SciPost Phys. 18 (2025), 088

  • Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physics, Jonas Spinner, Victor Bresó, Pim de Haan (U. Heidelberg (main)), Tilman Plehn, Jesse Thaler (Qualcomm, US) et al., e-Print: 2405.14806 [physics.data-an]

  • Differentiable MadNIS-Lite, Theo Heimel (Heidelberg U.), Olivier Mattelaer (Louvain U., CP3), Tilman Plehn (Heidelberg U. and U. Heidelberg (main)), Ramon Winterhalder (Louvain U., CP3), e-Print: 2408.01486, DOI: 10.21468/SciPostPhys.18.1.017 (publication), Published in: SciPost Phys. 18 (2025), 017

  • Machine learning study to identify collective flow in small and large colliding systems, Shuang Guo (Fudan U. and Fudan U., Shanghai), Han-Sheng Wang (Fudan U. and Fudan U., Shanghai), Kai Zhou (Frankfurt U., FIAS), Guo-Liang Ma (Fudan U. and Fudan U., Shanghai), e-Print: 2305.09937, DOI: 10.1103/PhysRevC.110.024910 (publication), Published in: Phys.Rev.C 110 (2024) 2, 2

  • Building imaginary-time thermal field theory with artificial neural networks*, Tian Xu (Beihang U.), Lingxiao Wang (Wako, RIKEN), Lianyi He (Tsinghua U., Beijing), Kai Zhou (Shenzhen, SUSTC and SUSTech, Shenzhen and SUSTech, SKLQSE, Shenzhen and Frankfurt U.), Yin Jiang (Beihang U.), e-Print: 2405.10493, DOI: 10.1088/1674-1137/ad5f80, Published in: Chin.Phys.C 48 (2024) 10, 103101

  • Phase Transition Study Meets Machine Learning, Yu-Gang Ma (Fudan U. and Fudan U., Shanghai), Long-Gang Pang (Hua-Zhong Normal U., LQLP and CCNU, Wuhan, Inst. Part. Phys.), Rui Wang (Fudan U. and SINAP, Shanghai and INFN, Catania), Kai Zhou (Frankfurt U., FIAS), e-Print: 2311.07274, DOI: 10.1088/0256-307X/40/12/122101, Published in: Chin.Phys.Lett. 40 (2023) 12, 122101, Chin.Phys.Lett. 40 (2023), 122101

  • Diffusion models as stochastic quantization in lattice field theory, Lingxiao Wang (Frankfurt U., FIAS), Gert Aarts (Swansea U. and ECT, Trento and Fond. Bruno Kessler, Trento), Kai Zhou (Frankfurt U., FIAS), e-Print: 2309.17082, DOI: 10.1007/JHEP05(2024)060, Published in: JHEP 05 (2024), 060

  • Mass and tidal parameter extraction from gravitational waves of binary neutron stars mergers using deep learning, Shriya Soma (Frankfurt U., FIAS and Frankfurt U.), Horst Stöcker (Frankfurt U., FIAS and Frankfurt U. and Darmstadt, GSI), Kai Zhou (Frankfurt U., FIAS), e-Print: 2306.17488, DOI: 10.1088/1475-7516/2024/01/009, Published in: JCAP 01 (2024), 009

  • Exploring QCD matter in extreme conditions with Machine Learning, Kai Zhou (Frankfurt U., FIAS), Lingxiao Wang (Frankfurt U., FIAS), Long-Gang Pang (CCNU, Wuhan, Inst. Part. Phys.), Shuzhe Shi (Stony Brook U.), e-Print: 2303.15136, DOI: 10.1016/j.ppnp.2023.104084, Published in: Prog.Part.Nucl.Phys. 135 (2024), 104084, Prog.Part.Nucl.Phys. 104084, 2023

  • QCD Equation of State of Dense Nuclear Matter from a Bayesian Analysis of Heavy-Ion Collision Data, Manjunath Omana Kuttan (Frankfurt U. and Frankfurt U., FIAS and Goethe U., Frankfurt, IAP and Unlisted), Jan Steinheimer (Frankfurt U., FIAS), Kai Zhou (Frankfurt U., FIAS), Horst Stoecker (Darmstadt, GSI and Frankfurt U., FIAS and Goethe U., Frankfurt, IAP), e-Print: 2211.11670, DOI: 10.1103/PhysRevLett.131.202303, Published in: Phys.Rev.Lett. 131 (2023) 20, 202303

  • Generative Diffusion Models for Lattice Field Theory, Lingxiao Wang (Frankfurt U., FIAS), Gert Aarts (Swansea U. and ECT, Trento), Kai Zhou (Frankfurt U., FIAS), e-Print: 2311.03578

  • Improved selective background Monte Carlo simulation at Belle II with graph attention networks and weighted events, Boyang Yu (Munich U.), Nikolai Hartmann (Munich U.), Luca Schinnerl (Munich U.), Thomas Kuhr (Munich U.), e-Print: 2307.06434

  • Ultra-high-granularity detector simulation with intra-event aware generative adversarial network and self-supervised relational reasoning, Baran Hashemi (Munich, Tech. U.), Nikolai Hartmann (Munich U.), Sahand Sharifzadeh (Munich U.), James Kahn (KIT, Karlsruhe, SCC), Thomas Kuhr (Munich U.), e-Print: 2303.08046, DOI: 10.1038/s41467-024-49104-4, Published in: Nature Commun. 15 (2024) 1, 4916, Nature Commun. 115 (2024), 5825 (erratum)

  • Efficient phase-space generation for hadron collider event simulation, Enrico Bothmann (Gottingen U.), Taylor Childers (Argonne), Walter Giele (Fermilab), Florian Herren (Fermilab), Stefan Hoeche (Fermilab) et al., e-Print: 2302.10449, DOI: 10.21468/SciPostPhys.15.4.169, Published in: SciPost Phys. 15 (2023) 4, 169

  • A portable parton-level event generator for the high-luminosity LHC, Enrico Bothmann (Gottingen U.), Taylor Childers (Argonne, PHY), Walter Giele (Fermilab), Stefan Höche (Fermilab), Joshua Isaacson (Fermilab) et al., e-Print: 2311.06198 [hep-ph], DOI: 10.21468/SciPostPhys.17.3.081

  • Unweighting multijet event generation using factorisation-aware neural networks, Timo Janßen (Gottingen U.), Daniel Maître (Durham U., IPPP), Steffen Schumann (Gottingen U.), Frank Siegert (Dresden, Tech. U.), Henry Truong (Durham U., IPPP), e-Print: 2301.13562, DOI: 10.21468/SciPostPhys.15.3.107, Published in: SciPost Phys. 15 (2023) 3, 107, SciPost Phys. 15 (2023), 107

  • Development of the time-of-flight particle identification for future Higgs factories, Bohdan Dudar (DESY and Hamburg U.), Ulrich Einhaus (DESY), Jenny List (DESY), Konrad Helms (DESY and Gottingen U.), Frank Gaede (DESY), e-Print: 2311.04720, DOI: 10.22323/1.449.0548, Published in: PoS EPS-HEP2023 (2024), 548

  • Improving Monte Carlo simulations in high energy physics using machine learning techniques, Timo Janßen (U. Gottingen (main)), Thesis, DOI: 10.53846/goediss-10094

  • Event generation with Sherpa 3, Sherpa Collaboration • Enrico Bothmann (Gottingen U.) et al., e-Print: 2410.22148, DOI: 10.1007/JHEP12(2024)156, Published in: JHEP 12 (2024), 156

  • Learning Optimal and Interpretable Summary Statistics of Galaxy Catalogs with SBI, Kai Lehman, Sven Krippendorf, Jochen Weller, Klaus Dolag, e-Print: 2411.08957 [astro-ph.CO]

  • MCBench: A Benchmark Suite for Monte Carlo Sampling Algorithms Z Ding, C Grunwald, K Ickstadt, K Kröninger, S La Cagnina, e-Print:2501.03138

  • Full phase space resonant anomaly detection, Erik Buhmann (Hamburg U.), Cedric Ewen (Hamburg U.), Gregor Kasieczka (Hamburg U.), Vinicius Mikuni (LBL, Berkeley), Benjamin Nachman (LBL, Berkeley and UC, Berkeley) et al., e-Print: 2310.06897, DOI: 10.1103/PhysRevD.109.055015, Published in: Phys.Rev.D 109 (2024) 5, 055015

  • Accurate Surrogate Amplitudes with Calibrated Uncertainties, Henning Bahl (Heidelberg U.), Nina Elmer (Heidelberg U.), Luigi Favaro (Heidelberg U. and Louvain U., CP3), Manuel Haußmann (Southern Denmark U., CP3-Origins), Tilman Plehn (Heidelberg U. and U. Heidelberg (main)) et al., e-Print: 2412.12069

  • Phase space sampling with Markov Chain Monte Carlo methods, Salvatore La Cagnina (Dortmund U.), Cornelius Grunwald (Dortmund U.), Timo Janßen (Gottingen U.), Kevin Kröninger (Dortmund U. and Tech. U., Dortmund (main)), Steffen Schumann (Gottingen U.), e-Print: 2412.12963

  • Advancing Tools for Simulation-Based Inference, Henning Bahl (U. Heidelberg, ITP), Victor Bresó (U. Heidelberg, ITP), Giovanni De Crescenzo (U. Heidelberg, ITP), Tilman Plehn (U. Heidelberg, ITP and U. Heidelberg (main)), e-Print: 2410.07315