We predict glueball/oddball resonances lying on the pomeron/odderon trajectories. A simple new form of the trajectories, with threshold and asymptotic behaviour required by analyticity and unitarity, ...is proposed. The parameters of these (pomeron and odderon) trajectories are fitted to the data on high-energy elastic proton-proton and proton-antiproton scattering. The fitted trajectories are extrapolated to the resonance region to predict masses and widths of glueballs and oddballs. The (pomeron and odderon) trajectories may be used to calculate processes of central exclusive diffraction (CED).
After the discovery of the Higgs boson in 2012 at the Large Hadron Collider (LHC), the effort to understand the detailed properties of the Higgs bosons started. Of particular importance is study of ...the Higgs coupling to the top quark. This coupling can be studied through the associated production of Higgs boson with top-antitop quark pair, tt¯H. This process however suffers from the indistinguishable background tt¯Hb¯, since the Higgs boson decays predominately into bottom anti-bottom quark pair, bb¯. This study presents systematic approach of using machine learning (ML), specifically neural network method to distinguish between the process tt¯H and tt¯bb¯. Using input variables of kinematic variables (momentum), we found a signal efficiency of 46.7 % for signal events that have passed the preselection criteria. We conclude that the currently used input variables are not sufficient to discriminate between signal and background events, and we suggest that inclusion of input variables calculated from the fully reconstructed event could provide stronger discrimination between signal and background.
In the first three years of running, the LHC has delivered a wealth of new data that is now being analysed. With over 20 fb−1 of integrated luminosity, both ATLAS and CMS have performed many searches ...for new physics that theorists are eager to test their model against. However, tuning the detector simulations, understanding the particular analysis details and interpreting the results can be a tedious task.
Checkmate (Check Models At Terascale Energies) is a program package which accepts simulated event files in many formats for any model. The program then determines whether the model is excluded or not at 95% C.L. by comparing to many recent experimental analyses. Furthermore the program can calculate confidence limits and provide detailed information about signal regions of interest. It is simple to use and the program structure allows for easy extensions to upcoming LHC results in the future.
Program title: CheckMATE
Catalogue identifier: AEUT_v1_0
Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEUT_v1_0.html
Program obtainable from: CPC Program Library, Queen’s University, Belfast, N. Ireland
Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html
No. of lines in distributed program, including test data, etc.: 179960
No. of bytes in distributed program, including test data, etc.: 6089336
Distribution format: tar.gz
Programming language: C++, Python.
Computer: PC, Mac.
Operating system: Linux, Mac OS.
RAM: Bytes
Classification: 11.9.
External routines: ROOT, Python, Delphes (included with the distribution)
Nature of problem:
The LHC has delivered a wealth of new data that is now being analysed. Both ATLAS and CMS have performed many searches for new physics that theorists are eager to test their model against. However, tuning the detector simulations, understanding the particular analysis details and interpreting the results can be a tedious and repetitive task.
Solution method:
CheckMATE is a program package which accepts simulated event files in many formats for any model. The program then determines whether the model is excluded or not at 95% C.L. by comparing to many recent experimental analyses. Furthermore the program can calculate confidence limits and provide detailed information about signal regions of interest. It is simple to use and the program structure allows for easy extensions to upcoming LHC results in the future.
Restrictions:
Only a subset of available experimental results have been implemented.
Additional comments:
Checkmate is built upon the tools and hard work of many people. If Checkmate is used in your publication it is extremely important that all of the following citations are included,
•Delphes 3 1.•FastJet 2, 3.•Anti-kt jet algorithm 4.•CLs prescription 5.•In analyses that use the MT2 kinematical discriminant we use the Oxbridge Kinetics Library 6, 7 and the algorithm developed by Cheng and Han 8.•All experimental analyses that were used to set limits in the study.•The Monte Carlo event generator that was used.Running time:
The running time scales about linearly with the number of input events provided by the user. The detector simulation/analysis of 20000 events needs about 50 s/1 s for a single core calculation on an Intel Core i5-3470 with 3.2 GHz and 8 GB RAM.
References:
1J. de Favereau, C. Delaere, P. Demin, A. Giammanco, V. Lematre, et al., DELPHES 3, A modular framework for fast simulation of a generic collider experiment arXiv:1307.6346.2M. Cacciari, G.P. Salam, G. Soyez, FastJet User Manual, Eur. Phys. J. C72 (2012) 1896. arXiv:1111.6097, http://dx.doi.org/10.1140/epjc/s10052-012-1896-2.3M. Cacciari, G.P. Salam, Dispelling the N3 myth for the kt jet-finder, Phys. Lett. B641 (2006) 57–61. arXiv:hep-ph/0512210, http://dx.doi.org/10.1016/j.physletb.2006.08.037.4M. Cacciari, G.P. Salam, G. Soyez, The Anti-k(t) jet clustering algorithm, JHEP 0804 (2008) 063. arXiv:0802.1189, http://dx.doi.org/10.1088/1126-6708/2008/04/063.5A.L. Read, Presentation of search results: the cl’s technique, Journal of Physics G: Nuclear and Particle Physics 28 (10) (2002) 2693. URL http://stacks.iop.org/0954-3899/28/i=10/a=3136C. Lester, D. Summers, Measuring masses of semiinvisibly decaying particles pair produced at hadron colliders, Phys. Lett. B463 (1999) 99–103. arXiv:hep-ph/9906349, http://dx.doi.org/10.1016/S0370-2693(99)00945-4.7A. Barr, C. Lester, P. Stephens, m(T2): The Truth behind the glamour, J. Phys. G29 (2003) 2343–2363. arXiv:hep-ph/0304226, http://dx.doi.org/10.1088/0954-3899/29/10/304.8H.-C. Cheng, Z. Han, Minimal Kinematic Constraints and m(T2), JHEP 0812 (2008) 063. arXiv:0810.5178, http://dx.doi.org/10.1088/1126-6708/2008/12/063.
Parton distributions with LHC data Ball, Richard D.; Bertone, Valerio; Carrazza, Stefano ...
Nuclear physics. B,
02/2013, Letnik:
867, Številka:
2
Journal Article
Recenzirano
Odprti dostop
We present the first determination of parton distributions of the nucleon at NLO and NNLO based on a global data set which includes LHC data: NNPDF2.3. Our data set includes, besides the deep ...inelastic, Drell–Yan, gauge boson production and jet data already used in previous global PDF determinations, all the relevant LHC data for which experimental systematic uncertainties are currently available: ATLAS and LHCb W and Z rapidity distributions from the 2010 run, CMS W electron asymmetry data from the 2011 run, and ATLAS inclusive jet cross-sections from the 2010 run. We introduce an improved implementation of the FastKernel method which allows us to fit to this extended data set, and also to adopt a more effective minimization methodology. We present the NNPDF2.3 PDF sets, and compare them to the NNPDF2.1 sets to assess the impact of the LHC data. We find that all the LHC data are broadly consistent with each other and with all the older data sets included in the fit. We present predictions for various standard candle cross-sections, and compare them to those obtained previously using NNPDF2.1, and specifically discuss the impact of ATLAS electroweak data on the determination of the strangeness fraction of the proton. We also present collider PDF sets, constructed using only data from HERA, the Tevatron and the LHC, but find that this data set is neither precise nor complete enough for a competitive PDF determination.
Cryo-assemblies with the Nb 3 Sn MQXFA low-beta quadrupoles for the High Luminosity LHC (HL-LHC) upgrade will be tested at Fermilab's magnet test facility. A total of 10 cryo-assemblies will be ...delivered to CERN within the US HL-LHC Accelerator Upgrade Project (AUP). The horizontal test stand at Fermilab already has been used for testing the existing LHC inner-triplet quadrupoles, but the stand and corresponding electrical and cryogenic sub-systems were not operational for more than a decade. In order to restore the test stand functions and meet the design and test requirements for the HL-LHC magnets, the existing horizontal test facility at Fermilab underwent a significant refurbishment of the cryogenic and mechanical components. Most of the upgrades were completed and verified during so called zero-magnet test by late 2020, and then final commissioning of the upgraded horizontal test stand was completed during the first cryo-assembly test in 2023. These tests verified the major cryo-mechanical installations, as well as the basic test stand operations, including controlled cooldown and operation at 1.9 K, magnet protection and process controls. Overview of the Fermilab's horizontal test facility upgrade and commissioning of these upgrades are presented in this paper.
The LHC family includes nuclear-encoded, integral thylakoid membrane proteins, most of which coordinate chlorophyll and xanthophyll chromophores. By assembling with the core complexes of both ...photosystems, LHCs form a flexible peripheral moiety for enhancing light-harvesting cross-section, regulating its efficiency and providing protection against photo-oxidative stress. Upon its first appearance, LHC proteins underwent evolutionary diversification into a large protein family with a complex genetic redundancy. Such differentiation appears as a crucial event in the adaptation of photosynthetic organisms to changing environmental conditions and land colonization. The structure of photosystems, including nuclear- and chloroplast-encoded subunits, presented the cell with a number of challenges for the control of the light harvesting function. Indeed, LHC-encoding messages are translated in the cytosol, and pre-proteins imported into the chloroplast, processed to their mature size and targeted to the thylakoids where are assembled with chromophores. Thus, a tight coordination between nuclear and plastid gene expression, in response to environmental stimuli, is required to adjust LHC composition during photoacclimation. In recent years, remarkable progress has been achieved in elucidating structure, function and regulatory pathways involving LHCs; however, a number of molecular details still await elucidation. In this review, we will provide an overview on the current knowledge on LHC biogenesis, ranging from organization of pigment–protein complexes to the modulation of gene expression, import and targeting to the photosynthetic membranes, and regulation of LHC assembly and turnover. Genes controlling these events are potential candidate for biotechnological applications aimed at optimizing light use efficiency of photosynthetic organisms. This article is part of a Special Issue entitled: Chloroplast biogenesis.
•LHCs form a flexible moiety which enhances light-harvesting efficiency.•LHC evolutionary diversification was a crucial event in land colonization.•Environmental stimuli adjust antennae composition during photoacclimation.•The review highlights progress achieved in elucidating regulatory pathways of LHCs.•Genes controlling these events are candidate for biotechnological applications.
Unintegrated gluon distributions sensitive to the transverse spatial distribution of gluons in the proton are extracted from data on exclusive and diffractive final states at HERA in the dipole ...approach. These unintegrated gluon distributions can be used to compute inclusive hadron production in
p
+
p
collisions at the LHC. In this paper, we consider a number of saturation models with differing dynamical assumptions that give good fits to the available HERA data. We apply these models to study the rapidity and transverse momentum dependence of the LHC data up to
s
=
7
TeV
. We examine the sensitivity of these results to parameters that are not constrained by the HERA data and comment on similarities and differences with previous work. We compute the
n-particle inclusive multiplicity distribution and show that the LHC
p
+
p
results are in agreement with predictions for multi-particle production in the Color Glass Condensate approach. This result has significant ramifications for the interpretation of multi-particle correlations in high multiplicity events at the LHC.
Machine learning has played an important role in the analysis of high-energy physics data for decades. The emergence of deep learning in 2012 allowed for machine learning tools which could adeptly ...handle higher-dimensional and more complex problems than previously feasible. This review is aimed at the reader who is familiar with high-energy physics but not machine learning. The connections between machine learning and high-energy physics data analysis are explored, followed by an introduction to the core concepts of neural networks, examples of the key results demonstrating the power of deep learning for analysis of LHC data, and discussion of future prospects and concerns.
CheckMATE 2: From the model to the limit Dercks, Daniel; Desai, Nishita; Kim, Jong Soo ...
Computer physics communications,
December 2017, 2017-12-00, Letnik:
221
Journal Article
Recenzirano
Odprti dostop
We present the latest developments to the CheckMATE program that allows models of new physics to be easily tested against the recent LHC data. To achieve this goal, the core of CheckMATE now contains ...over 60 LHC analyses of which 12 are from the 13 TeV run. The main new feature is that CheckMATE 2 now integrates the Monte Carlo event generation via MadGraph5_aMC@NLO and Pythia 8. This allows users to go directly from a SLHA file or UFO model to the result of whether a model is allowed or not. In addition, the integration of the event generation leads to a significant increase in the speed of the program. Many other improvements have also been made, including the possibility to now combine signal regions to give a total likelihood for a model.
Program Title: CheckMATE
Program Files doi:http://dx.doi.org/10.17632/k4pnk5wrfm.1
Licensing provisions: GPLv3
Programming language: C++, Python
External routines/libraries: ROOT, Python, HepMC (optional) Pythia 8 (optional), Madgraph5_aMC@NLO (optional)
Subprograms used: Delphes
Nature of problem: The LHC experiments have performed a huge number of searches for new physics in the past few years. However the results can only be given for a few benchmark models out of the huge number that exist in the literature.
Solution method: CheckMATE is a program that automatically calculates limits for new physics models. The original version required the user to generate Monte Carlo events themselves before CheckMATE could be run but the new version now integrates this step. The simplest output of CheckMATE is whether the model is ruled out at 95% CLs or not. However, more complicated statistical metrics are also available, including the combination of many signal regions.
Restrictions: Only a subset of available experimental results have been implemented.
Additional comments:
•CheckMATE is built upon the tools and hard work of many people. If CheckMATE is used in your publication it is extremely important that all of the following citations are included, –Delphes 3 1.https://cp3.irmp.ucl.ac.be/projects/delphes–FastJet 2,3.http://fastjet.fr/–Anti-kt jet algorithm 4.–CLS prescription 5.–All experimental analyses that were used to set limits in the study and if the analysis was implemented by non- CheckMATE authors, the relevant implementation reference.–MadGraph5_aMC@NLO 6 if it is used to calculate the hard matrix element from within CheckMATE.https://launchpad.net/mg5amcnlo–Pythia8.2 7 if showering or matching is done from within CheckMATE.http://home.thep.lu.se/~torbjorn/Pythia.html–The Monte Carlo event generator that was used if .hepmc or .lhe files were generated externally.–In analyses that use the mT2 kinematical discriminant 8,9 we use the mt2_bisect library 10. We also include the MT2bℓ and MT2W derivatives 11.http://particle.physics.ucdavis.edu/hefti/projects/doku.php?id=wimpmasshttps://sites.google.com/a/ucdavis.edu/mass/–In analyses that use the MCT family of kinematical discriminants we use the MctLib library that includes the following variables, MCT 12, MCT corrected 13, MCT parallel and perpendicular 14.https://mctlib.hepforge.org/–In analyses that use topness variable we use the topness library 15.https://github.com/michaelgraesser/topness–Super-Razor 16 in analyses that use this variable.
1 J. de Favereau et al. DELPHES 3 Collaboration, JHEP 1402 (2014) 057 arXiv:1307.6346 hep-ex.
2 M. Cacciari, G. P. Salam and G. Soyez, Eur. Phys. J. C 72 (2012) 1896 arXiv:1111.6097 hep-ph.
3 M. Cacciari and G. P. Salam, Phys. Lett. B 641 (2006) 57 hep-ph/0512210.
4 M. Cacciari, G. P. Salam and G. Soyez, JHEP 0804 (2008) 063 arXiv:0802.1189 hep-ph.
5 A. L. Read, J. Phys. G 28 (2002) 2693.
6 J. Alwall et al., JHEP 1407 (2014) 079 arXiv:1405.0301 hep-ph.
7 T. Sjöstrand et al., Comput. Phys. Commun. 191 (2015) 159 arXiv:1410.3012 hep-ph.
8 C. G. Lester and D. J. Summers, Phys. Lett. B 463 (1999) 99 hep-ph/9906349.
9 A. Barr, C. Lester and P. Stephens, J. Phys. G 29 (2003) 2343 hep-ph/0304226.
10 H. C. Cheng and Z. Han, JHEP 0812 (2008) 063 arXiv:0810.5178 hep-ph.
11 Y. Bai, H. C. Cheng, J. Gallicchio and J. Gu, JHEP 1207 (2012) 110 arXiv:1203.4813 hep-ph.
12 D. R. Tovey, JHEP 0804 (2008) 034 arXiv:0802.2879 hep-ph.
13 G. Polesello and D. R. Tovey, JHEP 1003 (2010) 030 arXiv:0910.0174 hep-ph.
14 K. T. Matchev and M. Park, Phys. Rev. Lett. 107 (2011) 061801 arXiv:0910.1584 hep-ph.
15 M. L. Graesser and J. Shelton, Phys. Rev. Lett. 111 (2013) no.12, 121802 arXiv:1212.4495 hep-ph.
16 M. R. Buckley, J. D. Lykken, C. Rogan and M. Spiropulu, Phys. Rev. D 89 (2014) no.5, 055020 arXiv:1310.4827 hep-ph.
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