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Author Gomez Ambrosio, R.; ter Hoeve, J.; Madigan, M.; Rojo, J.; Sanz, V. url  doi
openurl 
  Title Unbinned multivariate observables for global SMEFT analyses from machine learning Type Journal Article
  Year 2023 Publication Journal of High Energy Physics Abbreviated Journal J. High Energy Phys.  
  Volume 03 Issue 3 Pages 033 - 66pp  
  Keywords SMEFT; Higgs Properties  
  Abstract Theoretical interpretations of particle physics data, such as the determination of the Wilson coefficients of the Standard Model Effective Field Theory (SMEFT), often involve the inference of multiple parameters from a global dataset. Optimizing such interpretations requires the identification of observables that exhibit the highest possible sensitivity to the underlying theory parameters. In this work we develop a flexible open source frame-work, ML4EFT, enabling the integration of unbinned multivariate observables into global SMEFT fits. As compared to traditional measurements, such observables enhance the sensitivity to the theory parameters by preventing the information loss incurred when binning in a subset of final-state kinematic variables. Our strategy combines machine learning regression and classification techniques to parameterize high-dimensional likelihood ratios, using the Monte Carlo replica method to estimate and propagate methodological uncertainties. As a proof of concept we construct unbinned multivariate observables for top-quark pair and Higgs+Z production at the LHC, demonstrate their impact on the SMEFT parameter space as compared to binned measurements, and study the improved constraints associated to multivariate inputs. Since the number of neural networks to be trained scales quadratically with the number of parameters and can be fully parallelized, the ML4EFT framework is well-suited to construct unbinned multivariate observables which depend on up to tens of EFT coefficients, as required in global fits.  
  Address (up) [Ambrosio, Raquel Gomez] Univ Milano Bicocca, Dipartimento Fis G Occhialini, Piazza Sci 3, I-20126 Milan, Italy, Email: raquel.gomezambrosio@unito.it;  
  Corporate Author Thesis  
  Publisher Springer Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1029-8479 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000946004000003 Approved no  
  Is ISI yes International Collaboration yes  
  Call Number IFIC @ pastor @ Serial 5501  
Permanent link to this record
 

 
Author Amerio, A.; Cuoco, A.; Fornengo, N. url  doi
openurl 
  Title Extracting the gamma-ray source-count distribution below the Fermi-LAT detection limit with deep learning Type Journal Article
  Year 2023 Publication Journal of Cosmology and Astroparticle Physics Abbreviated Journal J. Cosmol. Astropart. Phys.  
  Volume 09 Issue 9 Pages 029 - 39pp  
  Keywords gamma ray theory; Machine learning  
  Abstract We reconstruct the extra-galactic gamma-ray source-count distribution, or dN/dS, of resolved and unresolved sources by adopting machine learning techniques. Specifically, we train a convolutional neural network on synthetic 2-dimensional sky-maps, which are built by varying parameters of underlying source-counts models and incorporate the FermiLAT instrumental response functions. The trained neural network is then applied to the Fermi-LAT data, from which we estimate the source count distribution down to flux levels a factor of 50 below the Fermi-LAT threshold. We perform our analysis using 14 years of data collected in the (1, 10) GeV energy range. The results we obtain show a source count distribution which, in the resolved regime, is in excellent agreement with the one derived from cataloged sources, and then extends as dN/dS " S-2 in the unresolved regime, down to fluxes of 5 center dot 10-12 cm-2 s-1. The neural network architecture and the devised methodology have the flexibility to enable future analyses to study the energy dependence of the source-count distribution.  
  Address (up) [Amerio, A.] Univ Valencia, Inst Fis Corpuscular IFIC, Calle Catedrat Jose Beltran 2, Paterna 46980, Spain, Email: aurelio.amerio@ific.uv.es;  
  Corporate Author Thesis  
  Publisher IOP Publishing Ltd Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1475-7516 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:001097055700001 Approved no  
  Is ISI yes International Collaboration yes  
  Call Number IFIC @ pastor @ Serial 5785  
Permanent link to this record
 

 
Author Amerio, A.; Calore, F.; Serpico, P.D.; Zaldivar, B. url  doi
openurl 
  Title Deepening gamma-ray point-source catalogues with sub-threshold information Type Journal Article
  Year 2024 Publication Journal of Cosmology and Astroparticle Physics Abbreviated Journal J. Cosmol. Astropart. Phys.  
  Volume 03 Issue 3 Pages 055 - 18pp  
  Keywords gamma ray theory; Frequentist statistics  
  Abstract We propose a novel statistical method to extend Fermi-LAT catalogues of highlatitude -y-ray sources below their nominal threshold. To do so, we rely on the determination of the differential source -count distribution of sub -threshold sources which only provides the statistical flux distribution of faint sources. By simulating ensembles of synthetic skies, we assess quantitatively the likelihood for pixels in the sky with relatively low -test statistics to be due to sources, therefore complementing the source -count distribution with spatial information. Besides being useful to orient efforts towards multi -messenger and multi -wavelength identification of new -y-ray sources, we expect the results to be especially advantageous for statistical applications such as cross -correlation analyses.  
  Address (up) [Amerio, Aurelio; Zaldivar, Bryan] Univ Valencia, Inst Fis Corpuscular IFIC, Calle Catedrat Jose Beltran 2, Paterna 46980, Spain, Email: aurelio.amerio@ific.uv.es;  
  Corporate Author Thesis  
  Publisher IOP Publishing Ltd Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1475-7516 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:001194945600003 Approved no  
  Is ISI yes International Collaboration yes  
  Call Number IFIC @ pastor @ Serial 6032  
Permanent link to this record
 

 
Author ATLAS Collaboration (Aad, G. et al); Amos, K.R.; Aparisi Pozo, J.A.; Bailey, A.J.; Bouchhar, N.; Cabrera Urban, S.; Cantero, J.; Cardillo, F.; Castillo Gimenez, V.; Chitishvili, M.; Costa, M.J.; Didenko, M.; Escobar, C.; Fiorini, L.; Fullana Torregrosa, E.; Fuster, J.; Garcia, C.; Garcia Navarro, J.E.; Gomez Delegido, A.J.; Gonzalez de la Hoz, S.; Gonzalvo Rodriguez, G.R.; Guerrero Rojas, J.G.R.; Higon-Rodriguez, E.; Lacasta, C.; Lozano Bahilo, J.J.; Marti-Garcia, S.; Martinez Agullo, P.; Miralles Lopez, M.; Mitsou, V.A.; Monsonis Romero, L.; Moreno Llacer, M.; Munoz Perez, D.; Navarro-Gonzalez, J.; Poveda, J.; Prades Ibañez, A.; Rubio Jimenez, A.; Ruiz-Martinez, A.; Sabatini, P.; Salt, J.; Sanchez Sebastian, V.; Sayago Galvan, I.; Senthilkumar, V.; Soldevila, U.; Sanchez, J.; Torro Pastor, E.; Valero, A.; Valls Ferrer, J.A.; Varriale, L.; Villaplana Perez, M.; Vos, M. url  doi
openurl 
  Title Measurement of the Higgs boson mass in the H → Z Z* → 4l decay channel using 139 fb-1 of √s=13 TeV pp collisions recorded by the ATLAS detector at the LHC br Type Journal Article
  Year 2023 Publication Physics Letters B Abbreviated Journal Phys. Lett. B  
  Volume 843 Issue Pages 137880 - 23pp  
  Keywords  
  Abstract The mass of the Higgs boson is measured in the H -> Z Z* -> 4l decay channel. The analysis uses proton-proton collision data from the Large Hadron Collider at a centre-of-mass energy of 13 TeV recorded by the ATLAS detector between 2015 and 2018, corresponding to an integrated luminosity of 139 fb(-1). The measured value of the Higgs boson mass is 124.99 0.18(stat.) +/- 0.04(syst.) GeV. In final states with muons, this measurement benefits from an improved momentum-scale calibration relative to that adopted in previous publications. The measurement also employs an analytic model that takes into account the invariant-mass resolution of the four-lepton system on a per-event basis and the output of a deep neural network discriminating signal from background events. This measurement is combined with the corresponding measurement using 7 and 8 TeV pp collision data, resulting in a Higgs boson mass of 124.94 +/- 0.17(stat.) +/- 0.03(syst.) GeV.  
  Address (up) [Amerl, M.; Filmer, E. K.; Jackson, P.; Kong, A. X. Y.; Potti, H.; Ruggeri, T. A.; Ting, E. X. L.; White, M. J.] Univ Adelaide, Dept Phys, Adelaide, Australia  
  Corporate Author Thesis  
  Publisher Elsevier Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0370-2693 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:001256747200001 Approved no  
  Is ISI yes International Collaboration yes  
  Call Number IFIC @ pastor @ Serial 6194  
Permanent link to this record
 

 
Author ATLAS Collaboration (Aad, G. et al); Amos, K.R.; Aparisi Pozo, J.A.; Bailey, A.J.; Bouchhar, N.; Cabrera Urban, S.; Cantero, J.; Cardillo, F.; Castillo Gimenez, V.; Chitishvili, M.; Costa, M.J.; Didenko,, M.; Escobar, C.; Fiorini, L.; Fullana Torregrosa, E.; Fuster, J.; Garcia, C.; Garcia Navarro, J.E.; Gomez Delegido, A.J.; Gonzalez de la Hoz, S.; Gonzalvo Rodriguez, G.R.; Guerrero Rojas, J.G.R.; Lacasta, C.; Lozano Bahilo, J.J.; Marti-Garcia, S.; Martinez Agullo, P.; Miralles Lopez, M.; Mitsou, V.A.; Monsonis Romero, L.; Moreno Llacer, M.; Munoz Perez, D.; Navarro-Gonzalez, J.; Poveda, J.; Prades Ibañez, A.; Rubio Jimenez, A.; Ruiz-Martinez, A.; Sabatini, P.; Salt, J.; Sanchez Sebastian, V.; Sayago Galvan, I.; Senthilkumar, V.; Soldevila, U.; Sanchez, J.; Torro Pastor, E.; Valero, A.; Valiente Moreno, E.; Valls Ferrer, J.A.; Varriale, L.; Villaplana Perez, M.; Vos, M. url  doi
openurl 
  Title Search for new phenomena in multi-body invariant masses in events with at least one isolated lepton and two jets using √s=13 TeV proton-proton collision data collected by the ATLAS detector Type Journal Article
  Year 2023 Publication Journal of High Energy Physics Abbreviated Journal J. High Energy Phys.  
  Volume 07 Issue 7 Pages 202 - 44pp  
  Keywords Hadron-Hadron Scattering; Particle and Resonance Production  
  Abstract A search for resonances in events with at least one isolated lepton (e or mu) and two jets is performed using 139 fb(-1) of root s = 13 TeV proton-proton collision data recorded by the ATLAS detector at the LHC. Deviations from a smoothly falling background hypothesis are tested in three- and four-body invariant mass distributions constructed from leptons and jets, including jets identified as originating from bottom quarks. Model-independent limits on generic resonances characterised by cascade decays of particles leading to multiple jets and leptons in the final state are presented. The limits are calculated using Gaussian shapes with different widths for the invariant masses. The multi-body invariant masses are also used to set 95% confidence level upper limits on the cross-section times branching ratios for the production and subsequent decay of resonances predicted by several new physics scenarios.  
  Address (up) [Amerl, M.; Filmer, E. K.; Jackson, P.; Kong, A. X. Y.; Potti, H.; Ruggeri, T. A.; Ting, E. X. L.; White, M. J.] Univ Adelaide, Dept Phys, Adelaide, SA, Australia  
  Corporate Author Thesis  
  Publisher Springer Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1029-8479 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:001062358800001 Approved no  
  Is ISI yes International Collaboration yes  
  Call Number IFIC @ pastor @ Serial 5727  
Permanent link to this record
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