Publikationen

2023

Flow Matching Beyond Kinematics: Generating Jets with Particle-ID and Trajectory Displacement Information
J. Birk, E. Buhmann, C. Ewen, G. Kasieczka, D. Shih
November 2023, e-print: 2312.00123

Machine learning techniques for model-independent searches in dijet final states
P. Harris, W. P. Mccormack, S. E. Park, T. Quadfasel, M. Sommerhalder, L. J. Moureaux, G. Kasieczka, O. Amram, P. Maksimovic, B. Maier, M. Pierini, K. A. Wozniak, T. K. Aarrestad, J. Ngadiuba, I. Zoi, S. K. Bright-Thonney, D. Shih, A. Bal
November 2023, e-print: CERN document server (CDS) link

Full Phase Space Resonant Anomaly Detection
E. Buhmann, C. Ewen, G. Kasieczka, V. Mikuni, B. Nachman, D. Shih
October 2023, e-print: 2310.06897

Statistical guarantees for stochastic Metropolis-Hastings
S. Bieringer, G. Kasieczka, M. F. Steffen, M. Trabs
October 2023, e-print: 2310.09335

EPiC-ly Fast Particle Cloud Generation with Flow-Matching and Diffusion
E. Buhmann, C. Ewen, D. A. Faroughy, T. Golling, G. Kasieczka, M. Leigh, G. Quétant, J. A. Raine, D. Sengupta, D. Shih
September 2023, e-print: 2310.00049

Back To The Roots: Tree-Based Algorithms for Weakly Supervised Anomaly Detection
T. Finke, M. Hein, G. Kasieczka, M. Krämer, A. Mück, P. Prangchaikul, T. Quadfasel, D. Shih, M. Sommerhalder
September 2023, e-print: 2309.13111

Combining Resonant and Tail-based Anomaly Detection
G. Bickendorf, M. Drees, G. Kasieczka, C. Krause, D. Shih
September 2023, e-print: 2309.12918

CaloClouds II: Ultra-Fast Geometry-Independent Highly-Granular Calorimeter Simulation
E. Buhmann, F. Gaede, G. Kasieczka, A. Korol, W. Korcari, K. Krüger, P. McKeown
September 2023, e-print: 2309.05704

Search for long-lived heavy neutral leptons decaying in the CMS muon detectors in proton-proton collisions at √s=13 TeV
The CMS collaboration
July 2023, e-print: CERN document server (CDS) link

The Interplay of Machine Learning-based Resonant Anomaly Detection Methods
T. Golling, G. Kasieczka, C. Krause, R. Mastandrea, B. Nachman, J. A. Raine, D. Sengupta, D. Shih, M. Sommerhalder
July 2023, e-print: 2307.11157

CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulation
E. Buhmann, S. Diefenbacher, E. Eren, F. Gaede, G. Kasieczka, A. Korol, W. Korcari, K. Krüger, P. McKeown
May 2023, e-print: 2305.04847

New Angles on Fast Calorimeter Shower Simulation
S. Diefenbacher, E. Eren, F. Gaede, G. Kasieczka, A. Korol, K. Krüger, P. McKeown, L. Rustige
March 2023, e-print: 2303.18150

L2LFlows: Generating High-Fidelity 3D Calorimeter Images
S. Diefenbacher, E. Eren, F. Gaede, G. Kasieczka, C. Krause, I. Shekhzadeh, D. Shih
February 2023, e-print: 2302.11594

PeakOTron: A Python Module for Fitting Charge Spectra of Silicon Photomultipliers
J. Rolph, E. Garutti, R. Klanner, T. Quadfasel, J. Schwandt
January 2023, published in: NIM A, 1056, 168544 (2023), e-print: 2301.11833

EPiC-GAN: Equivariant Point Cloud Generation for Particle Jets
E. Buhmann, G. Kasieczka, J. Thaler
January 2023, e-print: 2301.08128

2022

Morphological Classification of Radio Galaxies with wGAN-supported Augmentation
L. Rustige, J. Kummer, F. Griese, K. Borras, M. Brüggen, P. Connor, F. Gaede, G. Kasieczka, T. Knopp, P. Schleper
December 2022, published in: RAS Techniques and Instruments, Volume 2, Issue 1, January 2023, Pages 264–277, e-print: 2212.08504

Search for long-lived particles using out-of-time trackless jets in proton-proton collisions at √s = 13 TeV
The CMS Collaboration
December 2022, published in: JHEP 07 (2023) 210, e-print: 2212.06695

Feature Selection with Distance Correlation
R. Das, G. Kasieczka, D. Shih
November 2022, e-print: 2212.00046

Resonant anomaly detection without background sculpting
A. Hallin, G. Kasieczka, T. Quadfasel, D. Shih, M. Sommerhalder
October 2022, published in: Phys. Rev. D 107, 114012, e-print: 2210.14924

Anomaly Detection under Coordinate Transformations
G. Kasieczka, R. Mastandrea, V. Mikuni, B. Nachman, M. Pettee, D. Shih
September 2022, published in: Phys. Rev. D 107, 015009, e-print: 2209.06225

Data Science and Machine Learning in Education
G. Benelli, G. Chachamis, T. Chen, J. Duarte, M. Feickert, M. Graham, L. Gray, D. Hackett, P. Harris, S.-C. Hsu, G. Kasieczka, E. Khoda, M. Komm, M. Liu, M. Neubauer, S. Norberg, A. Perloff, M. Rieger, A. Roy, C. Savard, K. Terao, S. Thais, J.-R. Vlimant,
July 2022, e-print: 2207.09060

Radio Galaxy Classification with wGAN-Supported Augmentation
K. Borras, M. Brüggen, P. Connor, F. Gaede, F. Griese, G. Kasieczka, J. Kummer, L. Rustige, P. Schleper
June 2022, published in: INFORMATIK 2022 Workshops, P-326 469-478 (2022), Lecture Notes in Informatics (LNI), e-print: 2206.15131

Measurement of the mass dependence of the transverse momentum of lepton pairs in Drell-Yan production in proton-proton collisions at √s = 13 TeV
CMS collaboration
May 2022, published in: Eur. Phys. J. C 83 (2023) 628, e-print: 2205.04897

Search for narrow resonances in the b-tagged dijet mass spectrum in proton-proton collisions at √s = 13 TeV
CMS collaboration
May 2022, published in: Phys. Rev. D 108, 012009, e-print: 2205.01835

New directions for surrogate models and differentiable programming for High Energy Physics detector simulation
A. Adelmann, W. Hopkins, E. Kourlitis, M. Kagan, G. Kasieczka, C. Krause, V. Mikuni, B. Nachman, K. Pedro, D. Shih, D. Winklehner
March 2022, e-print: 2203.08806

Machine Learning and LHC Event Generation
A. Butter (ed), T. Plehn (ed), S. Schumann (ed), S. Badger, S. Caron, K. Cranmer, F. A. Di Bello, E. Dreyer, S. Forte, S. Ganguly, D. Gonçalves, E. Gross, T. Heimel, G. Heinrich, L. Heinrich, A. Held, S. Höche, J. N. Howard, P. Ilten, J. Isaacson, T. Janßen, S. Jones, M. Kado, M. Kagan, G. Kasieczka, F. Kling, S. Kraml, C. Krause, F. Krauss, K. Kröninger, R. K. Barman, M. Luchmann, V. Magerya, D. Maitre, B. Malaescu, F. Maltoni, T. Martini, O. Mattelaer, B. Nachman, S. Pitz, J. Rojo, M. Schwartz, D. Shih, F. Siegert, R. Stegeman, B. Stienen, J. Thaler, R. Verheyen, D. Whiteson, R. Winterhalder, J. Zupan
March 2022, published in: SciPost Phys. 14, 079 (2023), e-print: 2203.07460

Ephemeral Learning - Augmenting Triggers with Online-Trained Normalizing Flows
A. Butter, S. Diefenbacher, G. Kasieczka, B. Nachman, T. Plehn, D. Shih, R. Winterhalder
February 2022, published in: SciPost Phys. 13, 087 (2022), e-print: 2202.09375

Calomplification -- The Power of Generative Calorimeter Models
S. Bieringer, S. Diefenbacher, E. Eren, F. Gaede, D. Hundhausen, G. Kasieczka, B. Nachman, T. Plehn, M. Trabs
February 2022, published in: JINST 17 P09028, e-print: 2202.07352

2021

Hadrons, Better, Faster, Stronger
E. Buhmann, S. Diefenbacher, E. Eren, F. Gaede, D. Hundhausen, G. Kasieczka, W. Korcari, K. Krüger, P. McKeown, L. Rustige
December 2021, published in: Mach. Learn.: Sci. Technol. 3 025014, e-print: 2112.09709

Machine Learning in the Search for New Fundamental Physics
G. Karagiorgi, G. Kasieczka, S. Kravitz, B. Nachman, D. Shih
December 2021, published in: Nat Rev Phys 4, 399–412 (2022), e-print: 2112.03769

Classifying Anomalies THrough Outer Density Estimation (CATHODE)
A. Hallin, J. Isaacson, G. Kasieczka, C. Krause, B. Nachman, T. Quadfasel, M. Schlaffer, D. Shih, M. Sommerhalder
September 2021, published in: Phys. Rev. D 106, 055006, e-print: 2109.00546

Symmetries, Safety, and Self-Supervision
B. Dillon, G. Kasieczka, H. Olischlager, T. Plehn, P. Sorrenson, L. Vogel
August 2021, published in: SciPost Phys. 12, 188 (2022) , e-print: 2108.04253

Unsupervised Hadronic SUEP at the LHC
J. Barron, D. Curtin, G. Kasieczka, T. Plehn, A. Spourdalakis
July 2021, published in: J. High Energ. Phys. 2021, 129 (2021), e-print: JHEP

New Methods and Datasets for Group Anomaly Detection From Fundamental Physics
G. Kasieczka, B. Nachman, D. Shih
July 2021, e-print: 2107.02821

Search for long-lived particles decaying in the CMS endcap muon detectors in proton-proton collisions at √s=13 TeV
The CMS collaboration
July 2021, published in: Phys. Rev. Lett. 127, 261804 (2021) e-print: journals.aps.org

Shared Data and Algorithms for Deep Learning in Fundamental Physics
L. Benato, E. Buhmann, M. Erdmann, P. Fackeldey, J. Glombitza, N. Hartmann, G. Kasieczka, W. Korcari, T. Kuhr, J. Steinheimer, H.Stöcker, T. Plehn, K. Zhou
July 2021, published in: Comput Softw Big Sci 6, 9 (2022), e-print: 2107.00656

Amplifying Statistics with Ensembles of Generative Models
A. Butter, S. Diefenbacher, G. Kasieczka, B. Nachman, T. Plehn in: simDL Workshop at ICLR 2021
May 2021, e-print: e-print

Generative Models for Particle Shower Simulation in Fundamental Physics
E. Buhmann, S. Diefenbacher, E. Eren, F. Gaede, D. Hundhausen, G. Kasieczka, W. Korcari, A. Korol, K. Krüger, P. McKeown, L. Rustige in: simDL Workshop at ICLR 2021
May 2021, e-print: e-print

Decoding Photons: Physics in the Latent Space of a BIB-AE Generative Network
E. Buhmann, S. Diefenbacher, E. Eren, F. Gaede, G. Kasieczka, A. Korol, K. Krüger
February 2021, published in: EPJ Web of Conferences 251, 03003 (2021), e-print: 2102.12491

The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics
G. Kasieczka, B. Nachman, D. Shih, et al.
January 2021, published in: Rep Prog Phys. 2021 Nov. 4, e-print: iopscience

2020

Amplifying Statistics using Generative Models
A. Butter, S. Diefenbacher, G. Kasieczka, B. Nachman, T. Plehn
December 2020, published as: ML4PS Workshop at NeurIPS 2020, e-print: Link

How to GAN Higher Jet Resolution
P. Baldi, L. Blecher, A. Butter, J. Collado, J. N. Howard, F. Keilbach, T. Plehn, G. Kasieczka, D. Whiteson
December 2020, e-print: 2012.11944

DCTRGAN: Improving the Precision of Generative Models with Reweighting
S. Diefenbacher, E. Eren, G. Kasieczka, A. Korol, B. Nachman, D. Shih
September 2020, published in: JINST 15 P11004, e-print: 2009.03796

Teaching machine learning with an application in collider particle physics

L. Benato, P.L.S. Connor, G. Kasieczka, D. Krücker, M. Meyer

September 2020, published in: JINST 15 (2020) 09, C09011, e-print: iopscience

GANplifying Event Samples

A. Butter, S. Diefenbacher, G. Kasieczka, B. Nachman, T. Plehn

August 2020, published in: SciPost Phys. 10, 139 (2021), e-print: 2008.06545

ABCDisCo: Automating the ABCD Method with Machine Learning

G. Kasieczka, B. Nachman, M. Schwartz, D. Shih

July 2020, e-print: 2007.14400

published February 2021 in: Phys. Rev. D 103, 035021, e-print: journals.aps.org

Towards Machine Learning Analytics for Jet Substructure

G. Kasieczka, S. Marzani, G. Soyez, G. Stagnitto

July 2020, published in: JHEP 09 (2020) 195, e-print: 2007.04319

Invertible Networks or Partons to Detector and Back Again

M. Bellagente, A. Butter, G. Kasieczka, T. Plehn, A. Rousselot

June 2020, published in: SciPost Phys. 9, 074 (2020), e-print: 2006.06685

Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed

E. Buhmann, S. Diefenbacher, E. Eren, F. Gaede, G. Kasieczka, A. Korol, K. Krüger

May 2020, published in: Comput Softw Big Sci 5, 13 (2021), e-print: 2005.05334

Per-Object Systematics using Deep-Learned Calibration

G. Kasieczka, M. Luchmann, F. Otterpohl, T. Plehn

March 2020, published in: SciPost Phys. 9, 089 (2020), e-print: 2003.11099

Robust Jet Classifiers through Distance Correlation

G. Kasieczka, D. Shih

January 2020, published in: Phys.Rev.Lett. 125 (2020) 12, 122001

e-print: 2001.05310

2019

How to GAN away Detector Effects

M. Bellagente, A. Butter, G. Kasieczka, T. Plehn, R. Winterhalder

December 2019, published in: SciPost Phys. 8 (2020) 4, 070, e-print: 1912.00477

CapsNets Continuing the Convolutional Quest

S. Diefenbacher, H. Frost, G. Kasieczka, T. Plehn, J. Thompson

June 2019, published in: SciPost Phys. 8 (2020) 2, 023, e-print: 1906.11265

Morphological classification of radio galaxies: Capsule Networks versus Convolutional Neural Networks

P.N. Best, M.Brüggen, J. H. Croston, G. Kasieczka, V. Lukic, B. Mingo

May 2019, published in: MNRAS 487 (2019) 2, e-print: 1905.03274

Deep-Learning Jets with Uncertainties and More

S. Bollweg, M. Haußmann, G. Kasieczka, M. Luchmann, T. Plehn

April 2019, published in: SciPost Phys. 8 (2020) 1, 006, e-print: 1904.10004

The Machine Learning Landscape of Top Taggers

G. Kasieczka (ed), T. Plehn (ed), A. Butter, K. Cranmer, D. Debnath et. al.

February 2019, published in: SciPost Phys. 7 (2019) 1, 014, e-print: 1902.09914

2018

Quark-Gluon Tagging: Machine Learning vs Detector

G. Kasieczka, N. Kiefer, T. Plehn, T. Thompson

December 2018, published in: SciPost Phys. 6 (2019) 6, 069, e-print: 1812.09223

QCD or What?

T. Heimel, G. Kasieczka, T. Plehn, J. Thompson

August 2018, published in: SciPost Phys. 6 (2019) 3, 030, e-print: 1808.08979

Boosted Top Tagging Method Overview

G. Kasieczka

January 2018, contribution to: TOP2017, e-print: 1801.04180

2017

Deep-learned Top Tagging with a Lorentz Layer

A. Butter, G. Kasieczka, T. Plehn, M. Russell

July 2017, published in: SciPost Phys. 5 (2018) 3, 028, e-print: 1707.08966

Deep-learning Top Taggers or The End of QCD?

G. Kasieczka, T. Plehn, M. Russell, T. Schell

January 2017, published in: JHEP 05 (2017) 006, e-print: 1701.08784