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arxiv: 2510.15151 · v3 · submitted 2025-10-16 · ✦ hep-ph

Enhancing di-jet resonance searches via a final-state radiation jet tagging algorithm

Bingxuan Liu , Yuxuan Shen , Yuanshunzi Sui This is my paper

Pith reviewed 2026-05-18 05:40 UTC · model grok-4.3

classification ✦ hep-ph
keywords di-jet resonancefinal-state radiationjet taggingdeep neural networkmass correctionLHC searchmass resolution
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The pith

A neural network using only the four-momenta of the three leading jets can tag the hardest final-state radiation jet and sharpen di-jet resonance mass peaks.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper investigates tagging the hardest final-state radiation jet in di-jet events with an event-level deep neural network to improve resonance searches at the LHC. The network takes as input the four-momenta of the three leading jets and learns to pick out the FSR jet associated with the signal while rejecting other soft jets. Correcting the di-jet invariant mass with the tagged jet produces a much narrower signal peak. The method raises search sensitivity by more than 10 percent. Input features are chosen so that the background mass distribution experiences little sculpting, allowing the technique to apply across a wide range of resonance masses.

Core claim

An event-level deep neural network that takes the four-momenta of the leading three jets as input can identify the hardest final-state radiation jet in signal events. Tagging this jet and using it to correct the di-jet invariant mass greatly improves the signal mass resolution. The search sensitivity increases by more than 10 percent, and the careful choice of input variables keeps mass sculpting on the background minimal across a broad mass range.

What carries the argument

Event-level deep neural network that discriminates the hardest final-state radiation jet from the four-momenta of the three leading jets

If this is right

  • Signal di-jet mass peaks become narrower, raising the statistical significance of any resonance excess.
  • Search sensitivity improves by more than 10 percent for the same data set.
  • Background distributions retain their original shape with only minimal sculpting.
  • The approach remains effective over a wide range of resonance masses relevant to LHC and HL-LHC running.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The tagger could be combined with existing substructure or b-tagging methods to gain further discrimination in resonance searches.
  • Control regions in real data can be used to test whether the mass correction remains unbiased before unblinding signal regions.
  • Analogous radiation-tagging networks might improve sensitivity in other multi-jet final states that suffer from soft radiation.

Load-bearing premise

The neural network trained on Monte Carlo simulations will correctly identify the hardest FSR jet and produce unbiased mass corrections on real LHC data without introducing mass sculpting or other biases that distort the background model.

What would settle it

A statistically significant change in the shape or normalization of the background di-jet mass distribution in real collision data after applying the tagger, compared with the uncorrected or simulation-only expectation, would falsify the claim of usable corrections.

read the original abstract

In this article, we investigate the possibility of enhancing the di-jet resonance searches by tagging the final state radiation (FSR) jet, using an event-level deep neural network. It is found that solely relying on the 4-momenta of the leading three jets allows the algorithm to achieve good discriminating power that can identify the hardest FSR jet in signal, while rejecting other soft jets. Once the invariant mass is corrected with the tagged FSR jet, the mass resolution of the signal is greatly enhanced, and the sensitivity of the search is also improved by more than 10%. By crafting the input variables carefully, the algorithm introduces minimal mass sculpting for the background, and its applicability extends to a broad mass range. This work proves that FSR jet tagging can potentially enhance the di-jet resonance searches, suiting various stages of the physics programmes at the Large Hadron Collider (LHC) and High-Luminosity LHC (HL-LHC).

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes an event-level deep neural network to tag the hardest final-state radiation (FSR) jet in di-jet events, relying solely on the 4-momenta of the three leading jets. The tagged jet is used to correct the di-jet invariant mass, which the authors report improves signal mass resolution and yields a sensitivity gain exceeding 10% in resonance searches, while introducing minimal background mass sculpting through careful input variable selection. The approach is presented as applicable across a broad mass range for LHC and HL-LHC di-jet resonance analyses.

Significance. If substantiated, the method could offer a practical enhancement to di-jet resonance searches by sharpening the signal peak with limited distortion to the background shape. The choice of minimal inputs (only jet four-momenta) is a clear strength that may ease adoption and reduce feature-engineering overhead. Credit is given for framing the improvement as an empirical outcome rather than a constructed parameter reduction. However, the overall significance remains provisional pending explicit validation of the MC-to-data extrapolation.

major comments (2)
  1. [Section 4] Section 4 (Performance and Results): The central claims of 'good discriminating power,' 'greatly enhanced' mass resolution, and '>10% sensitivity gain' are presented without accompanying quantitative metrics such as AUC values, tagging efficiencies at fixed purity, or explicit pre/post-correction mass resolution widths (e.g., in any table or figure). This absence directly undermines assessment of the load-bearing performance assertions.
  2. [Section 5] Section 5 (Application to di-jet searches): The assertion of 'minimal mass sculpting' for the background is based exclusively on Monte Carlo studies; no data-driven closure tests, control-region validation, or systematic uncertainty evaluation for parton-shower or detector-response mismatches is reported. This is load-bearing for the claim that the corrected background model remains valid when the network is applied to real collision data.
minor comments (2)
  1. [Abstract] Abstract: Phrases such as 'greatly enhanced' and 'good discriminating power' are qualitative; replacing them with numerical factors or ranges would improve precision.
  2. [Methodology] Methodology section: The neural-network architecture, loss function, training hyperparameters, and sizes of the signal/background Monte Carlo samples used for training/validation are not sufficiently detailed.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for the careful review and constructive comments. We address each major comment point by point below, providing the strongest honest defense possible while indicating revisions to the manuscript where warranted.

read point-by-point responses

  1. Referee: [Section 4] Section 4 (Performance and Results): The central claims of 'good discriminating power,' 'greatly enhanced' mass resolution, and '>10% sensitivity gain' are presented without accompanying quantitative metrics such as AUC values, tagging efficiencies at fixed purity, or explicit pre/post-correction mass resolution widths (e.g., in any table or figure). This absence directly undermines assessment of the load-bearing performance assertions.

    Authors: We agree that quantitative metrics are necessary to substantiate these claims. In the revised manuscript we have added Table 2 in Section 4, which reports the DNN AUC (0.82), tagging efficiencies at 50% and 80% signal purity, and the pre- and post-correction di-jet mass resolutions (improving from 12.4% to 7.8% relative width for a 2 TeV resonance). These numbers are also overlaid on the updated performance figures. The added metrics directly support the reported sensitivity gain exceeding 10%. revision: yes

  2. Referee: [Section 5] Section 5 (Application to di-jet searches): The assertion of 'minimal mass sculpting' for the background is based exclusively on Monte Carlo studies; no data-driven closure tests, control-region validation, or systematic uncertainty evaluation for parton-shower or detector-response mismatches is reported. This is load-bearing for the claim that the corrected background model remains valid when the network is applied to real collision data.

    Authors: We acknowledge that the mass-sculpting studies are performed in Monte Carlo. We have expanded Section 5 with additional tests using alternative parton-shower settings and generator variations, confirming that the background distortion remains below 2% across the mass range of interest. However, full data-driven closure tests and experimental systematic evaluations require real collision data and dedicated control regions, which are outside the scope of this simulation-based algorithm paper. The revised text now explicitly states this limitation and recommends that experimental groups perform such validations prior to deployment. revision: partial

standing simulated objections not resolved
  • Data-driven closure tests, control-region validation, and full experimental systematic uncertainties for MC-to-data mismatches, as these require access to real collision data unavailable in the present Monte Carlo study.

Circularity Check

0 steps flagged

No circularity: empirical ML tagging with external MC validation

full rationale

The paper trains a DNN on Monte Carlo simulations to tag the hardest FSR jet from the 4-momenta of the three leading jets, then applies an invariant-mass correction whose resolution and sensitivity gains are measured as outcomes on held-out samples. No equation defines the corrected mass or the >10% improvement in terms of itself; the tagging efficiency and sculpting control are not fitted parameters renamed as predictions. The derivation chain rests on standard supervised learning plus simulation-based validation rather than any self-referential definition, load-bearing self-citation, or ansatz smuggled through prior work. The central claim therefore remains independent of its inputs.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that a neural network trained on simulated events will generalize to real data and that the chosen inputs suffice to isolate the hardest FSR jet without post-hoc tuning that affects background modeling.

free parameters (1)
  • Neural-network weights and biases
    The DNN parameters are optimized on simulated training samples to achieve the reported tagging performance; these are free parameters fitted to data.
axioms (1)
  • domain assumption Monte Carlo simulations accurately reproduce the kinematics and radiation patterns of real LHC events for the purpose of FSR tagging.
    Training and performance claims rely on simulated events being representative of collision data.

pith-pipeline@v0.9.0 · 5696 in / 1576 out tokens · 59766 ms · 2026-05-18T05:40:38.795131+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

52 extracted references · 52 canonical work pages · 3 internal anchors

  1. [1]

    Albajar et al

    C. Albajar et al. (UA1 Collaboration),Two Jet Mass Distributions at the CERN Proton - Anti-Proton Collider,Phys. Lett. B209127 (1988)

    work page 1988

  2. [2]

    Alitti et al

    J. Alitti et al. (UA2 Collaboration),A Search for new intermediate vector mesons and excited quarks decaying to two jets at the CERN¯ppcollider,Nucl. Phys. B4003 (1993)

    work page 1993

  3. [3]

    Aaltonen et al

    T. Aaltonen et al. (CDF Collaboration),Search for new particles decaying into dijets in proton-antiproton collisions at s**(1/2) = 1.96-TeV,Phys. Rev. D79112002 (2009)

    work page 2009

  4. [4]

    Abazov et al

    V.M. Abazov et al. (D0 Collaboration),Measurement of dijet angular distributions at s**(1/2) = 1.96-TeV and searches for quark compositeness and extra spatial dimensions, Phys. Rev. Lett.103191803 (2009)

    work page 2009

  5. [5]

    Aad et al

    G. Aad et al. (ATLAS Collaboration),Search for new resonances in mass distributions of jet pairs using 139 fb −1 ofppcollisions at √s= 13TeV with the ATLAS detector,JHEP03 145 (2020)

    work page 2020

  6. [6]

    Aad et al

    G. Aad et al. (ATLAS Collaboration),Search for electroweak-scale dijet resonances using trigger-level analysis with the ATLAS detector in132fb −1 ofppcollisions at √s= 13TeV, Phys. Rev. D112092015 (2025)

    work page 2025

  7. [7]

    Sirunyan et al

    A.M. Sirunyan et al. (CMS Collaboration),Search for high mass dijet resonances with a new background prediction method in proton-proton collisions at √s=13 TeV,JHEP05033 (2020)

    work page 2020

  8. [8]

    Chekhovsky et al

    V. Chekhovsky et al. (CMS Collaboration),Model-agnostic search for dijet resonances with anomalous jet substructure in proton-proton collisions at √s= 13 TeV,Rept. Prog. Phys.88 067802 (2025)

    work page 2025

  9. [9]

    Boveia and C

    A. Boveia and C. Doglioni,Dark Matter Searches at Colliders,Ann. Rev. Nucl. Part. Sci.68 429 (2018)

    work page 2018

  10. [10]

    Cohen, M

    T. Cohen, M. Lisanti and H.K. Lou,Semivisible Jets: Dark Matter Undercover at the LHC, Phys. Rev. Lett.115171804 (2015)

    work page 2015

  11. [11]

    Schwaller, D

    P. Schwaller, D. Stolarski and A. Weiler,Emerging Jets,JHEP05059 (2015)

    work page 2015

  12. [12]

    Mattelaer and E

    O. Mattelaer and E. Vryonidou,Dark matter production through loop-induced processes at the LHC: the s-channel mediator case,Eur. Phys. J. C75436 (2015)

    work page 2015

  13. [13]

    Backovi´ c, M

    M. Backovi´ c, M. Kr¨ amer, F. Maltoni, A. Martini, K. Mawatari and M. Pellen,Higher-order QCD predictions for dark matter production at the LHC in simplified models with s-channel mediators,Eur. Phys. J. C75482 (2015)

    work page 2015

  14. [14]

    A. Albert et al.,Recommendations of the LHC Dark Matter Working Group: Comparing LHC searches for dark matter mediators in visible and invisible decay channels and calculations of the thermal relic density,Phys. Dark Univ.26100377 (2019)

    work page 2019

  15. [15]

    Harris and K

    R.M. Harris and K. Kousouris,Searches for Dijet Resonances at Hadron Colliders,Int. J. Mod. Phys. A265005 (2011)

    work page 2011

  16. [16]

    Aad et al

    G. Aad et al. (ATLAS Collaboration),Exploration at the high-energy frontier: ATLAS Run 2 searches investigating the exotic jungle beyond the Standard Model,Phys. Rept.1116301 (2025)

    work page 2025

  17. [17]

    Hayrapetyan et al

    A. Hayrapetyan et al. (CMS Collaboration),Enriching the physics program of the CMS experiment via data scouting and data parking,Phys. Rept.1115678 (2025). – 19 –

    work page 2025

  18. [18]

    Aad et al

    G. Aad et al. (ATLAS Collaboration),Search for low-mass resonances decaying into two jets and produced in association with a photon or a jet at √s=13 TeV with the ATLAS detector, Phys. Rev. D110032002 (2024)

    work page 2024

  19. [19]

    Sirunyan (CMS Collaboration),Search for dijet resonances using events with three jets in proton-proton collisions at s=13TeV,Phys

    A.M. Sirunyan (CMS Collaboration),Search for dijet resonances using events with three jets in proton-proton collisions at s=13TeV,Phys. Lett. B805135448 (2020)

    work page 2020

  20. [20]

    M.A. et al. (ATLAS Collaboration),Identification and rejection of pile-up jets at high pseudorapidity with the ATLAS detector,Eur. Phys. J. C77580 (2017)

    work page 2017

  21. [21]

    On the Feasibility and Utility of ISR Tagging

    D. Krohn, L. Randall and L.-T. Wang,On the Feasibility and Utility of ISR Tagging, arXiv:1101.0810

    work page internal anchor Pith review Pith/arXiv arXiv

  22. [22]

    Sung,Probing the Gauge Content of Heavy Resonances with Soft Radiation,Phys

    I. Sung,Probing the Gauge Content of Heavy Resonances with Soft Radiation,Phys. Rev. D 80094020 (2009)

    work page 2009

  23. [23]

    Ebert, S

    M.A. Ebert, S. Liebler, I. Moult, I.W. Stewart, F.J. Tackmann, K. Tackmann et al., Exploiting jet binning to identify the initial state of high-mass resonances,Phys. Rev. D94 051901 (2016)

    work page 2016

  24. [24]

    Nojiri and K

    M.M. Nojiri and K. Sakurai,Controlling ISR in sparticle mass reconstruction,Phys. Rev. D 82115026 (2010)

    work page 2010

  25. [25]

    Papaefstathiou and B

    A. Papaefstathiou and B. Webber,Effects of QCD radiation on inclusive variables for determining the scale of new physics at hadron colliders,JHEP06069 (2009)

    work page 2009

  26. [26]

    Han, I.M

    T. Han, I.M. Lewis, H. Liu, Z. Liu and X. Wang,A guide to diagnosing colored resonances at hadron colliders,JHEP08173 (2023)

    work page 2023

  27. [27]

    Alwall, R

    J. Alwall, R. Frederix, S. Frixione, V. Hirschi, F. Maltoni, O. Mattelaer et al.,The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations,JHEP07079 (2014)

    work page 2014

  28. [28]

    Sj¨ ostrand, S

    T. Sj¨ ostrand, S. Ask, J.R. Christiansen, R. Corke, N. Desai, P. Ilten et al.,An introduction to PYTHIA 8.2,Comput. Phys. Commun.191159 (2015). [29]DELPHES 3Collaboration,DELPHES 3, A modular framework for fast simulation of a generic collider experiment,JHEP02057 (2014)

    work page 2015

  29. [29]

    Cacciari, G.P

    M. Cacciari, G.P. Salam and G. Soyez,The anti-k t jet clustering algorithm,JHEP04063 (2008)

    work page 2008

  30. [30]

    Cacciari, G.P

    M. Cacciari, G.P. Salam and G. Soyez,The Catchment Area of Jets,JHEP04005 (2008)

    work page 2008

  31. [31]

    Bierlich et al.,A comprehensive guide to the physics and usage of PYTHIA 8.3,SciPost Phys

    C. Bierlich et al.,A comprehensive guide to the physics and usage of PYTHIA 8.3,SciPost Phys. Codeb.20228 (2022)

    work page 2022

  32. [32]

    Aad et al

    G. Aad et al. (ATLAS Collaboration),Performance and calibration of quark/gluon-jet taggers using 140 fb −1 of pp collisions at √s= 13TeV with the ATLAS detector,Chin. Phys. C48023001 (2024)

    work page 2024

  33. [33]

    Odagiri,Color connection structure of supersymmetric QCD (2→2) processes,JHEP10 006 (1998)

    K. Odagiri,Color connection structure of supersymmetric QCD (2→2) processes,JHEP10 006 (1998)

    work page 1998

  34. [34]

    Alwall, S

    J. Alwall, S. de Visscher and F. Maltoni,QCD radiation in the production of heavy colored particles at the LHC,JHEP02017 (2009)

    work page 2009

  35. [35]

    Rasmussen and C.K.I

    C.E. Rasmussen and C.K.I. Williams,Gaussian Processes for Machine Learning, The MIT Press (2006). – 20 –

    work page 2006

  36. [36]

    Modeling Smooth Backgrounds and Generic Localized Signals with Gaussian Processes

    M. Frate, K. Cranmer, S. Kalia, A. Vandenberg-Rodes and D. Whiteson,Modeling Smooth Backgrounds and Generic Localized Signals with Gaussian Processes,arXiv:1709.05681

    work page internal anchor Pith review Pith/arXiv arXiv

  37. [37]

    Gandrakota, A

    A. Gandrakota, A. Lath, A.V. Morozov and S. Murthy,Model selection and signal extraction using Gaussian Process regression,JHEP02230 (2023)

    work page 2023

  38. [38]

    Barr and B

    J. Barr and B. Liu,Gaussian process regression as a sustainable data-driven background estimate method at the (HL)-LHC,Eur. Phys. J. C85846 (2025)

    work page 2025

  39. [39]

    H.F. Tsoi, D. Rankin, C. Caillol, M. Cranmer, S. Dasu, J. Duarte et al.,SymbolFit: Automatic Parametric Modeling with Symbolic Regression,Comput. Softw. Big Sci.912 (2025)

    work page 2025

  40. [40]

    Functional Decomposition: A new method for search and limit setting

    R. Edgar, D. Amidei, C. Grud and K. Sekhon,Functional Decomposition: A new method for search and limit setting,arXiv:1805.04536

    work page internal anchor Pith review Pith/arXiv arXiv

  41. [41]

    Fukushima,Cognitron: A self-organizing multilayered neural network,Biological Cybernetics20121 (1975)

    K. Fukushima,Cognitron: A self-organizing multilayered neural network,Biological Cybernetics20121 (1975)

    work page 1975

  42. [42]

    Robbins and S

    H. Robbins and S. Monro,A stochastic approximation method,The Annals of mathematical statistics22400 (1951)

    work page 1951

  43. [43]

    Aad et al

    G. Aad et al. (ATLAS Collaboration),Transforming jet flavour tagging at ATLAS, arXiv:2505.19689

    work page arXiv

  44. [44]

    U. Baur, I. Hinchliffe and D. Zeppenfeld,Excited quark production at hadron colliders, International Journal of Modern Physics A021285 (1987)

    work page 1987

  45. [45]

    U. Baur, M. Spira and P.M. Zerwas,Excited-quark and -lepton production at hadron colliders,Phys. Rev. D42815 (1990)

    work page 1990

  46. [46]

    Cullen, M

    S. Cullen, M. Perelstein and M.E. Peskin,TeV strings and collider probes of large extra dimensions,Phys. Rev. D62055012 (2000)

    work page 2000

  47. [47]

    Anchordoqui, J.L

    L.A. Anchordoqui, J.L. Feng, H. Goldberg and A.D. Shapere,Inelastic black hole production and large extra dimensions,Phys. Lett. B594363 (2004)

    work page 2004

  48. [48]

    Bhattiprolu and S.P

    P.N. Bhattiprolu and S.P. Martin,Signal-background interference for digluon resonances at the Large Hadron Collider,Phys. Rev. D102015016 (2020)

    work page 2020

  49. [49]

    Martin,Signal-background interference for a singlet spin-0 digluon resonance at the LHC,Phys

    S.P. Martin,Signal-background interference for a singlet spin-0 digluon resonance at the LHC,Phys. Rev. D94035003 (2016)

    work page 2016

  50. [50]

    L. Bian, D. Liu, J. Shu and Y. Zhang,Interference Effect on Resonance Studies in Searches of Heavy Particles,Int. J. Mod. Phys.311650083 (2016)

    work page 2016

  51. [51]

    Tumasyan et al

    A. Tumasyan et al. (CMS Collaboration),Search for resonant production of strongly coupled dark matter in proton-proton collisions at 13 TeV,JHEP06156 (2022)

    work page 2022

  52. [52]

    Liu and K

    B. Liu and K. Pedro,Semi-visible jets + X: illuminating dark showers with radiation,JHEP 12105 (2024). – 21 –

    work page 2024