Reconstruction of decays to merged photons using end-to-end deep learning with domain continuation in the CMS detector Article Swipe
A. Tumasyan
,
W. Adam
,
Janik Walter Andrejkovic
,
T. Bergauer
,
S. Chatterjee
,
K. Damanakis
,
M. Dragicevic
,
A. Escalante Del Valle
,
R. Frühwirth
,
M. Jeitler
,
Natascha Krammer
,
L. Lechner
,
D. Liko
,
I. Mikulec
,
P. Paulitsch
,
F. M. Pitters
,
J. Schieck
,
R. Schöfbeck
,
D. Schwarz
,
S. Templ
,
W. Waltenberger
,
C.-E. Wulz
,
M. R. Darwish
,
E. A. De Wolf
,
X. Janssen
,
T. Kello
,
A. Lelek
,
Haifa Rejeb Sfar
,
P. Van Mechelen
,
S. Van Putte
,
N. Van Remortel
,
E. S. Bols
,
J. D’Hondt
,
A. De Moor
,
M. Delcourt
,
H. El Faham
,
S. Lowette
,
S. Moortgat
,
A. Morton
,
D. Müller
,
A. R. Sahasransu
,
S. Tavernier
,
W. Van Doninck
,
D. Vannerom
,
D. Beghin
,
B. Clerbaux
,
G. De Lentdecker
,
L. Favart
,
K. Lee
,
M. Mahdavikhorrami
,
I. Makarenko
,
S. Paredes
,
L. Pétré
,
A. Popov
,
N. Postiau
,
E. Starling
,
Laurent Thomas
,
M. Vanden Bemden
,
C. Vander Velde
,
P. Vanlaer
,
Didar Dobur
,
J. Knolle
,
Luka Lambrecht
,
G. Mestdach
,
M. Niedziela
,
César Rendón
,
C. Roskas
,
A. Samalan
,
K. Skovpen
,
M. Tytgat
,
N. Van Den Bossche
,
B. Vermassen
,
Liam Wezenbeek
,
A. Benecke
,
A. Bethani
,
G. Bruno
,
F. Bury
,
C. Caputo
,
P. David
,
C. Delaere
,
I. S. Donertas
,
A. Giammanco
,
K. Jaffel
,
Sa. Jain
,
V. Lemaı̂tre
,
K. Mondal
,
J. Prisciandaro
,
A. Taliercio
,
T. T. Tran
,
P. Vischia
,
S. Wertz
,
G. A. Alves
,
C. Hensel
,
A. Moraes
,
P. Rebello Teles
,
W. L. Aldá Júnior
,
M. Alves Gallo Pereira
,
M. Barroso Ferreira Filho
,
H. Brandao Malbouisson
,
W. Carvalho
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1103/physrevd.108.052002
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1103/physrevd.108.052002
A novel technique based on machine learning is introduced to reconstruct the decays of highly Lorentz \nboosted particles. Using an end-to-end deep learning strategy, the technique bypasses existing rule-based \n particle reconstruction methods typically used in high energy physics analyses. It uses minimally processed \n detector data as input and directly outputs particle properties of interest. The new technique is demonstrated \n for the reconstruction of the invariant mass of particles decaying in the CMS detector. The decay of a \n hypothetical scalar particle A into two photons, A → γγ, is chosen as a benchmark decay. Lorentz boosts \n γL ¼ 60–600 are considered, ranging from regimes where both photons are resolved to those where the \n photons are closely merged as one object. A training method using domain continuation is introduced, \n enabling the invariant mass reconstruction of unresolved photon pairs in a novel way. The new technique is \n validated using π0 → γγ decays in LHC collision data.
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- article
- Language
- en
- Landing Page
- https://doi.org/10.1103/physrevd.108.052002
- http://link.aps.org/pdf/10.1103/PhysRevD.108.052002
- OA Status
- hybrid
- Cited By
- 6
- References
- 48
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386453521
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https://openalex.org/W4386453521Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1103/physrevd.108.052002Digital Object Identifier
- Title
-
Reconstruction of decays to merged photons using end-to-end deep learning with domain continuation in the CMS detectorWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-09-05Full publication date if available
- Authors
-
A. Tumasyan, W. Adam, Janik Walter Andrejkovic, T. Bergauer, S. Chatterjee, K. Damanakis, M. Dragicevic, A. Escalante Del Valle, R. Frühwirth, M. Jeitler, Natascha Krammer, L. Lechner, D. Liko, I. Mikulec, P. Paulitsch, F. M. Pitters, J. Schieck, R. Schöfbeck, D. Schwarz, S. Templ, W. Waltenberger, C.-E. Wulz, M. R. Darwish, E. A. De Wolf, X. Janssen, T. Kello, A. Lelek, Haifa Rejeb Sfar, P. Van Mechelen, S. Van Putte, N. Van Remortel, E. S. Bols, J. D’Hondt, A. De Moor, M. Delcourt, H. El Faham, S. Lowette, S. Moortgat, A. Morton, D. Müller, A. R. Sahasransu, S. Tavernier, W. Van Doninck, D. Vannerom, D. Beghin, B. Clerbaux, G. De Lentdecker, L. Favart, K. Lee, M. Mahdavikhorrami, I. Makarenko, S. Paredes, L. Pétré, A. Popov, N. Postiau, E. Starling, Laurent Thomas, M. Vanden Bemden, C. Vander Velde, P. Vanlaer, Didar Dobur, J. Knolle, Luka Lambrecht, G. Mestdach, M. Niedziela, César Rendón, C. Roskas, A. Samalan, K. Skovpen, M. Tytgat, N. Van Den Bossche, B. Vermassen, Liam Wezenbeek, A. Benecke, A. Bethani, G. Bruno, F. Bury, C. Caputo, P. David, C. Delaere, I. S. Donertas, A. Giammanco, K. Jaffel, Sa. Jain, V. Lemaı̂tre, K. Mondal, J. Prisciandaro, A. Taliercio, T. T. Tran, P. Vischia, S. Wertz, G. A. Alves, C. Hensel, A. Moraes, P. Rebello Teles, W. L. Aldá Júnior, M. Alves Gallo Pereira, M. Barroso Ferreira Filho, H. Brandao Malbouisson, W. CarvalhoList of authors in order
- Landing page
-
https://doi.org/10.1103/physrevd.108.052002Publisher landing page
- PDF URL
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https://link.aps.org/pdf/10.1103/PhysRevD.108.052002Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://link.aps.org/pdf/10.1103/PhysRevD.108.052002Direct OA link when available
- Concepts
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Physics, Photon, Invariant mass, Detector, Particle physics, Scalar (mathematics), Large Hadron Collider, Invariant (physics), Nuclear physics, Optics, Mathematical physics, Geometry, MathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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6Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 2, 2023: 3Per-year citation counts (last 5 years)
- References (count)
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48Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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