summary of research activity€¦ · summary of research activity dinos bachas infn lecce seminar...
TRANSCRIPT
Summary of research activityDinos Bachas
INFN Lecce
Seminar at Demokritos, Athens, November 23, 2018
23 Nov. 2018
Education historyAcademic Qualifications
• Bachelor in Physics (2002) - Aristotle University of Thessaloniki • MSc in Experimental Particle Physics (2003) - The University of
Manchester • PhD in Physics (2008) - Aristotle University of Thessaloniki
Awards • ATLAS ‘Marc Virchaux’ prize for best PhD thesis related to the
Muon Spectrometer
Notable Projects • MSc Thesis: ’Studies of interstrip capacitance in a silicon detector’ • PhD Thesis: ’Studies of the ATLAS Muon Spectrometer with
testbeam and simulated physics data’ 2
23 Nov. 2018
Employment history
3
AUTH Research Associate 2003 - 2004
Construction and quality tests of BIS Muon chambers
CERN Applied Fellow, ATLAS 2009 – 2012
Project Leader, ATLAS Muon Spectrometer Simulation/Digitization/Det. Description
AUTH Post-Doctoral researcher, Thales’ project 2012 – 2015
Convener, ATLAS Standard Model WZ group
INFN Fellow (International competition), Sez. Di Lecce 2015 – 2017
Physics analysis in Exotics group MC Contact, Exotics to Physics Modeling group (2017- 2018)
INFN Fellow in Scientific Computing (International competition), Sez. Di Lecce 2017 – Present
Artificial Intelligence (Machine Learning, Deep Neural Networks for physics analysis)
23 Nov. 2018
Talk outline
1. Highlights from Physics research record
2. Highlights from simulation/software and performance studies record
3. Present activities in AI and Deep Learning for physics analysis
4. Outlook
4
Highlights from Physics research record
• Work in the context of my MSc thesis (Properties of ATLAS SCT silicon detectors)
• Work in the context of my PhD thesis (ZZ production)
• J/ψ cross-section measurements with early 7 TeV data (2010)
• Convener of the ATLAS Standard Model WZ group 2012-2015 • 1 publication (Phys. Rev. D 93, 092004 (2016)) and 1 CONF note
• 4 lepton inclusive production (2015) • Physics Letters B 753 (2016) 552-572
• Searches for heavy ZW and ZZ resonances in the llqq and vvqq final states at 13TeV (2015-2017)
• JHEP03(2018)009
23 Nov. 2018
WZ cross-sections and aGC limits at 8 TeV
Motivation • Test EWK theory at the
TeV scale • Investigate directly Vector
Boson Scattering (VBS) • Test of QCD calculations
(up to NLO QCD at that time)
New in this paper (with x4-more statistics wrt 7TeV):
• Differential cross sections: • (mTWZ, pTZ,W,ν, Njets, Δ|yZ-ylw|)
• σRatio(W+Z/W-Z)→Sensitive to PDFs • Use mTWZ to extract aTGC limits
• less sensitive to QCD & EWK corr., compared to pTZ
• Set upper limit for purely EWK-WZ production (VBS) and provide limits for aQGC’s
6
Measurements of W!Z production cross sections in pp collisionsat
ffiffis
p= 8 TeV with the ATLAS detector and limitson anomalous gauge boson self-couplings
G. Aad et al.*
(ATLAS Collaboration)(Received 8 March 2016; published 13 May 2016)
This paper presents measurements of W!Z production in pp collisions at a center-of-mass energy of8 TeV. The gauge bosons are reconstructed using their leptonic decay modes into electrons and muons. Thedata were collected in 2012 by the ATLAS experiment at the Large Hadron Collider and correspond to anintegrated luminosity of 20.3 fb−1. The measured inclusive cross section in the detector fiducial region isσW!Z→l0νll ¼ 35.1! 0.9ðstatÞ ! 0.8ðsysÞ ! 0.8ðlumiÞ fb, for one leptonic decay channel. In comparison,the next-to-leading-order Standard Model expectation is 30.0! 2.1 fb. Cross sections for WþZ and W−Zproduction and their ratio are presented as well as differential cross sections for several kinematicobservables. Limits on anomalous triple gauge boson couplings are derived from the transverse massspectrum of the W!Z system. From the analysis of events with a W and a Z boson associated with two ormore forward jets an upper limit at 95% confidence level on theW!Z scattering cross section of 0.63 fb, foreach leptonic decay channel, is established, while the Standard Model prediction at next-to-leading order is0.13! 0.01 fb. Limits on anomalous quartic gauge boson couplings are also extracted.
DOI: 10.1103/PhysRevD.93.092004
I. INTRODUCTION
The study of W!Z diboson production is an importanttest of the Standard Model (SM) for its sensitivity to thegauge boson self-interactions, related to the non-Abelianstructure of the electroweak interaction. It provides themeans to investigate vector boson scattering (VBS) proc-esses, which directly probe the electroweak symmetrybreaking sector of the SM, and to extract constraints onanomalous triple and quartic gauge boson couplings (aTGCand aQGC). Improved constraints can probe scalesof new physics in the multi-TeV range and provide away to look for signals of new physics in a model-independent way. Precise measurements of W!Zproduction will also help to improve the existing QCDcalculations of this process.This paper presents measurements of the W!Z produc-
tion cross section and limits on the aTGC and aQGCobtained by analyzing proton-proton (pp) collisions at acenter-of-mass energy of
ffiffiffis
p¼ 8 TeV. The leptonic decay
modes of the W and Z bosons are used and all quotedfiducial production cross sections include the branchingratio of the gauge bosons into channels with electrons ormuons. The analyzed data sample was collected in 2012 bythe ATLAS experiment at the Large Hadron Collider
(LHC), and corresponds to an integrated luminosity of20.3 fb−1. Experimentally, W!Z production has the ad-vantage of a higher cross section than ZZ production. Atthe same time, with three charged leptons and the require-ment that two of them originate from a Z boson, theleptonicW!Z final states are easier to discriminate from thebackground than the leptonic WW final states.Measurements of theW!Z production cross section have
been reported in proton-antiproton collisions at a center-of-mass energy of
ffiffiffis
p¼ 1.96 TeV by the CDF and D0
collaborations [1,2] using integrated luminosities of7.1 fb−1 and 8.6 fb−1, respectively, and for
ffiffiffis
p¼ 7 TeV
proton-proton collisions, using an integrated luminosity of4.6 fb−1, by the ATLAS Collaboration [3]. Limits onanomalous charged-current gauge couplings were alsoreported previously by the LEP, Tevatron, and LHC experi-ments [4–6]. In hadron collisions, the selection of W!Zfinal states allows direct access to theWWZ gauge couplingwithout the need of disentangling it from the WWγ gaugecoupling as in W!W∓ events from hadronic or eþe−
collisions.Compared to the previously published measurements,
this paper uses data collected at a higher center-of-massenergy with a fourfold increase in integrated luminosity andpresents additional measurements. The production crosssection is measured in a fiducial phase space inclusivelyand as single differential cross sections as a function ofeach of several kinematic variables: the transverse momen-tum pT of the W and Z bosons, the jet multiplicity, thetransverse mass of the WZ system, mWZ
T , and the pT of theneutrino associated with theW boson decay. An interesting
*Full author list given at the end of the article.
Published by the American Physical Society under the terms ofthe Creative Commons Attribution 3.0 License. Further distri-bution of this work must maintain attribution to the author(s) andthe published article’s title, journal citation, and DOI.
PHYSICAL REVIEW D 93, 092004 (2016)
2470-0010=2016=93(9)=092004(36) 092004-1 © 2016 CERN, for the ATLAS Collaboration
23 Nov. 2018
WZ Analysis Overview
Signature: • Final state: 3 high pT, isolated leptons, ETmiss • 1 OS, SF lepton pair : 66GeV< m𝓁𝓁 <116GeV • 3rd lepton + ETmiss consistent with W (mTW used) • 4 final states: eee, μee, eμμ, μμμ
The most complete and precise WZ experimental measurement that far
• total uncertainty in inclusive cross section: 4.7% (better than available theor. predict.)
7
E. Sauvan – LAPP Annecy
Measurements and Motivations
t-channel u-channel s-channel
W±Z production at LO:
SM Measurements:
New physics :
● Inclusive fiducial cross sections for W±Z, W+Z, W-Z
● Differential cross sections and differential ratios
● WZ-jj EW (VBS) inclusive cross section
● Constraints on anomalous Triple Gauge Coupling
● Constraints on anomalous Quartic GC
➘ The only VV process without inclusive NNLO predictions
First time possible
First time possible in WZ
SM Meeting 06/08/2015 - 2
Novelties of this analysis “Resonant-shape” algorithm to assign leptons to bosons based on the value of estimator expressing the product of the nominal line shapes of the W and Z resonances
Novelties of this analysis Thorough study of the background (Z+jets, ttbar) with 3 Data Driven
methods
Eve
nts
/ 2
5 G
eV
1
10
210
310
410 Data 2012 1.17)×Z (±W
Misid. leptons
ZZtt+VOthersTot. unc.
ℓ′ℓℓ
ℓ′, ℓ )µ( = e or
ATLAS-1 = 8 TeV, 20.3 fbs
[GeV]WZTm
0 200 400 600
Da
ta /
MC
0
1
2
Eve
nts
/ 1
0 G
eV
50
100
150
200
250
300Data 2012
1.17)×Z (±WMisid. leptons
ZZtt+VOthersTot. unc.
ℓ′ℓℓ
ℓ′, ℓ )µ( = e or
ATLAS-1 = 8 TeV, 20.3 fbs
[GeV]Z
Tp
0 100 200 300 400
Da
ta /
MC
0
1
2
23 Nov. 2018
Results: WZ cross section measurements at 8 TeV Measurement of fiducial and total cross sections
• ~17% higher than NLO QCD predictions
All 4 final states gave compatible results
Dominant uncertainties: • experimental: DataDriven-background, e-id efficiency • theory: μR, μF scales • For charged σRatio(W+Z/W-Z) is statistics and PDF
Diff. cross sections measured for six variables and for the charged ratio
8
Z Wfid.σ / Z W
fid.σ1 1.5 2 2.5 3
combined
µµµ
µµe
eeµ
eee
+ -
ATLAS
DataPowheg, CT10Powheg, ATLAS-epWZ12
-1 = 8 TeV, 20.3 fbs
0.19±1.46
0.22±1.92
0.14±1.26
0.14±1.47
0.08±1.51
theoryZ±Wσ / fid.
Z±Wσ0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
combined
µµµ
µµe
eeµ
eee ATLAS
DataPowheg
-1 = 8 TeV, 20.3 fbs
Z±W
0.10±1.27
0.08±1.21
0.08±1.19
0.06±1.11
0.05±1.17
[fb/G
eV
]W
ZT
m∆/
fid.
σ∆
3−10
2−10
1−10
1
Data 2012
Powheg
MC@NLO
Sherpa
ATLAS
-1 = 8 TeV, 20.3 fbs
ν →Z ±W ℓ′ ℓℓ
[fb
]fid
.σ
∆
1−10
1
10
210
[GeV]WZTm
0 100 200 300 400 500 600 700
Ra
tio t
o P
ow
he
g
0.6
1
1.4
1.8
2.2
∞
95% CL upper limit on �fid.W±Zjj-EW!`0⌫``
[fb]
VBS only VBS + tZjVBS phase space
Observed 0.63 0.67Expected 0.45 0.49
±1� Expected [0.28 ; 0.62] [0.33 ; 0.67]±2� Expected [0.08 ; 0.80] [0.19 ; 0.84]
aQGC phase space
Observed 0.25 0.25Expected 0.13 0.13
±1� Expected [0.08 ; 0.20] [0.08 ; 0.20]±2� Expected [0.04 ; 0.28] [0.06 ; 0.28]
Upper limit on VBS exp measurement is limited
by statistics
23 Nov. 2018
Results: Limits on Anomalous Triple and Quartic gauge couplings
Anomalous WWZ couplings are introduced in a generalized effective Lagrangian as deviations from the SM predictions by: ΔκΖ,Δg1Z and λΖ
aTGC limits are extracted by fits to the mTWZ distribution and in the EFT parametrization
9 4α-1 -0.5 0 0.5 1
5α
-1
-0.5
0
0.5
1ATLAS
Zjj±obs. 68% CL, WZjj±obs. 95% CL, WZjj±exp. 95% CL, W
jj±W±exp. 95% CL, WStandard Model
-1 = 8 TeV, 20.3 fbsK-matrix unitarization
Zjj± W→pp
aTGC Limits at 95% CL
-0.5 0 0.5 1 1.5
-l+lν± l→Z ±WZκΔ
Zλ
1ZgΔ
ATLAS
= 8 TeV sATLAS
= 2 TeV Λ, -120.3 fb
= 7 TeV sATLAS
= 2 TeV Λ, -14.6 fb
= 1.96 TeV sD0
= 2 TeV Λ, -14.1 fb
= 1.96 TeVsCDF
= 2 TeVΛ -1 7.1 fb
• aQGC limits extracted from fiducial cross section of the WZjj-EW VBS process
• WZjj complements the results by W±W±jj events
Limits on anomalous quartic couplings, aQGCs EFT coupling Expected [TeV�2
] Observed [TeV�2
]
cW /⇤2[�3.7 ; 7.6] [�4.3 ; 6.8]
cB/⇤2[�270 ; 180] [�320 ; 210]
cWWW /⇤2[�3.9 ; 3.8] [�3.9 ; 4.0]
Limits on anomalous triple couplings, aTGCs
23 Nov. 2018
Measurement of inclusive 4 lepton production at 8 TeV
Measurement of 4𝓁(*) production from Z/H/ZZ decays at 8 TeV in 80<m(4𝓁)<1000 GeV
• Resonance productions of qq→Z→4𝓁, gg→(H)→ZZ→4𝓁, H*
• non-resonant qq/gg→ZZ
Analysis goals: • Measure the differential
cross-section in the 4l mass range 80-1000 GeV
• Extraction of gluon-gluon production component (m(4𝓁) > 180 GeV) and compare to LO calculations
• The σ for gg→ZZ→4l was only calculated at LO 10
Physics Letters B 753 (2016) 552–572
Contents lists available at ScienceDirect
Physics Letters B
www.elsevier.com/locate/physletb
Measurements of four-lepton production in pp collisions at √s = 8 TeV with the ATLAS detector
.ATLAS Collaboration ⋆
a r t i c l e i n f o a b s t r a c t
Article history:Received 28 September 2015Received in revised form 4 December 2015Accepted 16 December 2015Available online 18 December 2015Editor: W.-D. Schlatter
The four-lepton (4ℓ, ℓ = e, µ) production cross section is measured in the mass range from 80 to 1000 GeV using 20.3 fb−1 of data in pp collisions at √s = 8 TeV collected with the ATLAS detector at the LHC. The 4ℓ events are produced in the decays of resonant Z and Higgs bosons and the non-resonant Z Z continuum originating from qq̄, gg, and qg initial states. A total of 476 signal candidate events are observed with a background expectation of 26.2 ± 3.6 events, enabling the measurement of the integrated cross section and the differential cross section as a function of the invariant mass and transverse momentum of the four-lepton system.In the mass range above 180 GeV, assuming the theoretical constraint on the qq̄ production cross section calculated with perturbative NNLO QCD and NLO electroweak corrections, the signal strength of the gluon-fusion component relative to its leading-order prediction is determined to be µgg =2.4 ± 1.0 (stat.) ± 0.5 (syst.) ± 0.8 (theory).
© 2015 CERN for the benefit of the ATLAS Collaboration. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Funded by SCOAP3.
1. Introduction
This paper presents measurements of the production of four isolated charged-leptons in proton–proton collisions at a centre-of-mass energy of
√s = 8 TeV using 20.3 fb−1 of data collected with
the ATLAS detector at the LHC. For the four-lepton (4ℓ, ℓ = e, µ) production, both the integrated cross section and the differential cross sections as functions of invariant mass (m4ℓ) and trans-verse momentum (p4ℓ
T ) of the 4ℓ system are measured in a mass range 80 < m4ℓ < 1000 GeV. In addition, the 4ℓ signal strength of gluon fusion (ggF) production relative to its leading-order (LO) QCD estimate is measured. These measurements test the validity of the Standard Model (SM) through the interplay of QCD and electroweak effects for different 4ℓ production mechanisms as de-scribed by the LO Feynman diagrams shown in Fig. 1.
The 4ℓ signal events come from the decays of resonant Z and Higgs bosons and the non-resonant ZZ continuum produced from qq̄, gg , and qg initial states, which are briefly discussed below.
• qq̄qq̄qq̄-initiated 4ℓ4ℓ4ℓ productionThe tree-level diagrams for qq̄ → 4ℓ production are shown in Fig. 1(a) and Fig. 1(b). The cross section as a function of m4ℓ is shown in Fig. 2 (the dashed black histogram). The 4ℓ event pro-duction at the Z resonance occurs predominantly via the s-channeldiagram as shown in Fig. 1(a), and was measured previously by
⋆ E-mail address: [email protected].
the ATLAS and CMS collaborations [1,2]. In the 4ℓ invariant mass region above the Z resonance the 4ℓ event production mainly pro-ceeds through the t-channel process as shown in Fig. 1(b). The cross section significantly increases when both Z bosons are pro-duced on-shell, resulting in a rise in the m4ℓ spectrum around 180 GeV. In addition, a small portion of the 4ℓ events with the qq̄ initial state can be produced from the vector-boson scattering (VBS) process.
• gggggg-initiated 4ℓ4ℓ4ℓ productionThe LO diagrams of the Higgs-boson production and non-resonant 4ℓ production via ggF are shown in Fig. 1(c) and Fig. 1(d), respec-tively. The cross sections as a function of m4ℓ are shown in Fig. 2(the coloured histograms). The features of the 4ℓ events from the decays of Higgs-boson and continuum Z Z production via gg F are described below.
(1) The dominant Higgs-boson production mechanism is ggF. Other Higgs-boson production mechanisms, vector-boson fu-sion (VBF), vector-boson associated production (VH), and top-pair associated production (tt̄ H), contribute less than 15% to the on-shell Higgs-boson decay to Z Z∗ event rate. The on-shell Higgs-boson production and decay leads to a narrow resonance around 125 GeV, which has been a key signature in the Higgs-boson discovery by the ATLAS [3] and CMS [4] col-laborations. The off-shell Higgs-boson production has a large destructive interference with continuum ZZ production from the ggF processes [5–7]. This effect can be observed in the
http://dx.doi.org/10.1016/j.physletb.2015.12.0480370-2693/© 2015 CERN for the benefit of the ATLAS Collaboration. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Funded by SCOAP3.
At high mass: Higgs* increases the 4l
rate
Non-resonant gg->ZZ->4l competes with Higgs
rate
Virtual Higgs decays to ZZ pairs and interferes with non-resonant ZZ
production via gg Fusion
Negative interference reduces event rate
23 Nov. 2018
Inclusive 4 lepton production: Main Results Differential Cross Section measurements in fiducial phase space
• Statistics dominate the uncertainties
Prediction: • NLO qq->4l (NNLO QCD and NLO EW corrections to the m4l
spectrum with both Zs on-shell) • NNLO gg->H->4l • LO Non-resonant gg->4l
11
Integrated cross-section measurements
Personal contribution to the analysis:
Estimation of background with Fake Factor method Efficiency corrections to
the MC
""
22"
Final background estimations
March 16, 2015 – 12 : 29 DRAFT 179
F Summary of predictions of the di↵erent background estimation meth-1853
ods in the signal region1854
We present the prediction of the total background, per channel, taking into account each of the reducible1855
background estimations presented in this section in Table 67. All the three background methods provide1856
compatible results for the background yield in the signal region.1857
1858
Table 67: Data-driven estimation of the reducible background events in the 8 TeV datasets.
sources Reducible Background at 8 TeVeeee eeµµ µµee µµµµ
Simultaneous fit 2.4 ± 0.32 ± 0.25 4.7 ± 0.3 ± 0.49 4.6 ± 0.74 ± 0.46 6.7 ± 0.43 ± 0.76Fake Factor I 2.04 ± 0.16 ± 0.34 2.85 ± 0.52 ± 0.13 2.86 ± 0.24 ± 0.41 7.41 ± 1.75 ± 2.27Fake Factor II 2.4 ± 0.6 ± 0.1 5.6 ± 1.8 ± 0.8 4.4 ± 1.0 ± 0.3 10.0 ± 2.6 ± 1.3
A summarized break-down of the systematics considered in each method is provided in Table 681859
Table 68: Systematic uncertainties summary for each data-driven estimation of the reducible background.
sources Systematic e↵ects
Simultaneous fit Experimental uncertainties (nuisance)PDF uncertainties (nuisance)
Model dependence on tt̄ shape (nuisance)Variation of the control regions definition
Cross section uncertainties on non-fitted backgrounds (nuisance)Fake Factor I (work ongoing)Fake Factor II FF statistical uncertainty
FF bin sizeFF average weighting procedure (see Table 66)Variation of selection cuts of FF control regions
The comparison of the predicted background distributions for the three methods is also presented in1860
Figure (todo).1861
1862
The total background estimation is presented in Table ?? where we include the irreducible back-1863
ground estimations, from MC, to each of the data-driven reducible background estimations. We also1864
include the MC predictions for the overall background.1865
All the different estimation techniques provide compatible results
""
March 16, 2015 – 12 : 29 DRAFT 65
5.2.5 Minimization of statistical uncertainties for Z+X(ee) background.777
It was observed that the MC expectations of Z+jets in the signal region for the electron channels were778
quite limited by the low available statistics (Table 12).779
We take events from MC Z+X (X = jets, �) in a loose control region where the 4th lepton from the780
quadruplet (lowest pT lepton from subleading Z) has no isolation no IP significance cut applied.In this781
region the expected Z+jets (Z/�⇤) events are 5.50± 1.92 (3.6± 0.6) and 1.72± 0.69 (1.17± 0.38) for the782
2µ2e and 4e channels respectively.783
We weight each MC event with the e�ciency of non prompt leptons to pass the isolation and IP signif-784
icancy cut. This e↵ectively extrapolates the prediction of the MC Z+X events back to the signal region.785
This e�ciency is verified in a dedicated control region explained in Section B.786
The final expected events for Z+X(ee) (X = jets, �) are in Table 17.787
5.2.6 Post-fit expected events788
Applying the scale factors determined in the previous subsections, our final estimation for the fitted789
background events, per channel, is given in Table 17. In the same table we also add the remaining790
reducible backgrounds that are not corrected from the fit but taken directly from the simulation. It is the791
sum of these contributions that can be directly compared with the expectations from the other background792
studies.793
Process/Channel 4e 2µ2e 2e2µ 4µtt̄ 0.45 ± 0.10 ± 0.20 0.60 ± 0.11 ± 0.28 0.70 ± 0.10 ± 0.07 0.68 ± 0.12 ± 0.07
Z + jets 0.60 ± 0.25 ± 0.10 1.92 ± 0.70 ± 0.35 3.14 ± 0.25 ± 0.47 5.06 ± 0.40 ± 0.75WZ 0.78 ± 0.10 ± 0.12 0.65 ± 0.09 ± 0.09 0.66 ± 0.09 ± 0.09 0.69 ± 0.10 ± 0.09Z� 0.41 ± 0.13 ± 0.02 1.26 ± 0.20 ± 0.06 0.08 ± 0.08 ± 0.0 0.00 ± 0.00 ± 0.0tZ 0.11 ± 0.01 0.15 ± 0.02 0.11 ± 0.01 0.23 ± 0.03
Overall reducible background 2.4 ± 0.32 ± 0.25 4.6 ± 0.74 ± 0.46 4.7 ± 0.3 ± 0.49 6.7 ± 0.43 ± 0.76Overall background 3.6 ± 0.33 ± 0.31 6.0 ± 0.75 ± 0.52 6.4 ± 0.31 ± 0.55 8.7 ± 0.44 ± 0.83
Table 17: Final expectations for the di↵erent reducible background processes per channel with associ-ated statistical and systematical uncertainties. The last line summarizes the total background estimationincluding the irreducible processes presented in Table 12.The systematic uncertainties for the processestaken from MC are explained in Section 6.
5.2.7 Background distributions in the signal region794
A description of the background shape is required for the extraction of the di↵erential cross section795
versus the quadruplet lepton mass.796
Some of the background processes from simulation contain only few events in the signal region. For797
these processes (tt̄ in all channels and Z+jets in the sub-leading Z electron pair channels) we take the798
shape from control regions. In the particular case of the Zjets background for the muonic channels we799
take the shape from the Zbb simulation to describe the overall Zjets contribution.800
Figure 42 the smoothed distributions.801
The distributions of the total background versus the four-lepton mass, per channel and the overall802
signal region, are shown in Figure 43.803
Final background estimation :
Comparison between the three background methods :
Total expected backround : 24.7 +/- 0.98 +/- 1.78 events
23 Nov. 2018
Searches for heavy ZW and ZZ resonances in the llqq final state at 13TeV
Motivation: • Search for a diboson resonance in mass
range 300 - 5000 GeV.
Look for a peak in the Mlljj/MllJ invariant mass spectrum over a smoothly falling SM background
llqq event selection: • Merged large-R jet high-purity and low-
purity regions • Resolved 2-jet: tagged (2b-jets) and
untagged (<2 b-jets)
12
2
Search for high mass resonance in llqq and vvqq channels: Motivation and Selection
Search for a d iboson resonance in mass range 300 - 5000 GeV.
llqq/ννqq event selection: Merged large�R jet high-purity and low-purity regions llqq resolved 2-jet: tagged (2b-jets) and untagged (<2 b-jets) ννqq: Requires large ET
miss
Backgrounds: Z+Jets, Dibosons, top, W+Jets Leading systematics: Large-R jet energy scale/resolution and sub-structure variables
In the merged channel, jet-substructure techniques are used to identify the qq pair, reconstructed as a single large-radius jet.
24/11/16 N. V. Biesuz - K. Bachas 5
Vector-boson hadronic decays
● Wide range of resonance mass:
MX~ 300-5000 GeV;
● Wide range of boson pT;
●Δ Rq1q2
∼2×MV
PT
● Resolved analysis: reconstruct two small-R jets (Anti-Kt 0.4):● Merged analysis: jets are detected as one object, a large-R
jet (Anti-Kt 1.0):● Need of new techniques:● Grooming for pile-up suppression;● Boson tagging background rejection;
0.5 1.0 1.5 2.0 2.5 3.0m(H) [TeV]
0.0
0.2
0.4
0.6
0.8
1.0
Acc
epta
nce�
Effi
cien
cy ATLAS SimulationggF H � ZZ � ��qq
ggF cat. mergedggF cat. resolvedVBF cat. mergedVBF cat. resolvedCombinedTotal Uncertainty
(a) (b)
Figure 4: Selection acceptance times e�ciency for the H ! ZZ ! ``qq events from MC simulations as a functionof the Higgs boson mass for (a) ggF and (b) VBF production, combining the HP and LP signal regions of theZV ! ``J selection and the b-tagged and untagged regions of the ZV ! `` j j selection. The hatched bandrepresents the total statistical and systematic uncertainties.
HVT W0 signal, the widths of the m``J and m`` j j distributions are slightly larger at 3–4% since the W0
boson in this production mode has an intrinsic width of approximately 2.6% of its mass. The widths ofthe m``J and m`` j j distributions are 4% at 500 GeV for the bulk RS graviton signal with k/MPl = 1 andrise to 8% at 5000 GeV. The increase can be attributed to the increase in the intrinsic width of the signal.For a given resonance mass, the m``J distributions are narrower than those of m`` j j.
5.5 Data control regions and background estimation
The dominant backgrounds to the X ! ZV ! ``qq search are the Z+jets, top quark and diboson pro-cesses. Their contributions are estimated from a combination of MC and data-driven techniques. In allcases, the shapes of kinematic variables, including those of the final discriminants m``J and m`` j j, aretaken from MC simulations. The multijet background is estimated to be negligible.
The Z+jets events are expected to have smooth distributions of mJ and m j j, while the signal events shouldexhibit resonance structures at the mass of the vector-boson V . Thus, a Z+jets control region (CR) isdefined for every signal region by reversing the mJ or m j j requirement. Events in the control regions areselected in exactly the same way as those in their corresponding signal regions except for the requirementon mJ or m j j. For the ZV ! ``J selection, the leading large-R jet mass is required to be outside thelarge-R jet mass window of the 80% working point of the boson tagging. For the ZV ! `` j j selection, arequirement of 50 < m j j < 62 GeV or 105 < m j j < 150 GeV is applied. These CRs are dominated by theZ+jets contribution, with a purity higher than 96% in all regions, except for the b-tagged CR where thetop quark and Z+jets contributions are comparable. They are therefore used to constrain its contributionin signal regions through simultaneous fits as discussed in Section 8.
Top-quark production is a significant background source in the b-tagged signal region of the resolvedZV ! `` j j selection. Its contribution is constrained using a top-quark-enhanced control region. Events inthis control region must have two di↵erent-flavour leptons, eµ, with their invariant mass within [76, 106] GeV,
12
23 Nov. 2018
Searches for heavy ZW and ZZ resonances in the llqq final state at 13TeV - Results
Backgrounds: Z+Jets, Dibosons, top, W +Jets
Leading systematics: Large-R jet energy scale/resolution and sub-structure variables
No evidence for new heavy resonances
Upper bounds on the production cross sections times their decay branching ratios to ZZ or ZW derived
13
Spin-0 Higgs Boson
ggF VBF
1.7 pb at 300 GeV to 1.2 fb at 3000 GeV for ggF->H->ZZ
0.42 pb at 300 GeV to 0.87 fb at 3000 GeV for VBF H->ZZ
Personal contribution to the analysis: Estimation of background with Template Fit Substructure variables selection efficiency Central Jet Veto Studies Selection optimizations Data/MC matching investigations
Highlights from Simulation , Software, and
Performance studies record• Isolation Scale Factors in W,Z events
• Eur. Phys. J. C (2017) 77: 367
• Measurement of probability for muon Catastrophic Energy Loss
• PhD Thesis
• Novel performance studies for high eta muons (2.5 < η < 2.7) • Eur. Phys. J. C (2014) 74: 3130.
• Project Leader MC SimulationATLAS MS , Digitization - Emulation of MDT electronics response
• Work as an applied fellow at Cern
• A software package for muon energy measurements in the ATLAS calorimeter
• PhD Thesis
23 Nov. 2018
Measurement of probability for Catastrophic muon energy losses (1/2)
Probability that a muon looses a significant amount of its energy when traversing the ATLAS calorimeters (LArEM and the TileCal)
• Increases with increasing momentum • Crucial for precise momentum measurement of high-pT muons
Measurement performed in the H8 Testbeam with muons of E=350GeV • Main challenge was to separate muons from pions which contaminated the
beam
15
Energy in Tile A [GeV]0 50 100 150 200 250 300 350
Ener
gy in
Tile
BC
[GeV
]
0
50
100
150
200
250
300
350
Muon energy in Tile BC vs Tile A
Energy in Tile A [GeV]0 50 100 150 200 250 300 350
Ener
gy in
Tile
BC
[GeV
]
0
50
100
150
200
250
300
350
Pion energy in Tile BC vs Tile A
23 Nov. 2018
Measurement of probability for Catastrophic muon energy losses (2/2)
Muons and pions classified according to energy loss in the calorimeter compartments
• Muons act as mips in each calo compartment
• Pions shower in more than one compartment
Require that Muons are mips in all or all but 1 compartment
16 [GeV]thrslossE
20 30 40 50 60 70 80
Frac
tion
of e
vent
s [%
]
0.01
0.1
1
10threshold>ElossFraction of events with E
DataMC
Energy [GeV]0 50 100 150 200 250 300 350
Entr
ies/
10G
eV
1
10
210
310
410 DataMC
Energy loss in calorimeters
Eloss Threshold [GeV]
Probability Data[%]
Probability MC [%]
15 1.17 ± 0.09 1.26 ± 0.13
20 0.80 ± 0.08 0.84 ± 0.10
30 0.42 ± 0.06 0.43 ± 0.07
50 0.24 ± 0.04 0.30 ± 0.06
80 0.11 ± 0.03 0.14 ± 0.04
23 Nov. 2018
Measurement of the energy loss of muon in the ATLAS calorimeters
Developed an accurate energy deposition measurement algorithm
Used by the official software tools which provide the isolation variables in the ATLAS reconstruction
Methodology: • Tracks from the ID or the MS are extrapolated to
each calorimeter layer taking into account multiple scattering and magnetic field
• Sum up the cell energy in a cone accounting for calorimenter noise
• Repeat for all calo layers
17 η0 0.5 1 1.5 2 2.5 3
(GeV
)lo
ssTE
0
0.5
1
1.5
2
2.5
3
3.5lossTTrue E
lossTMeasured E
η0 0.5 1 1.5 2 2.5 3
(GeV
)lo
ssTE
0
0.5
1
1.5
2
2.5
3
3.5
4 lossTTrue E
lossTMeasured E
pT =10 GeV pT =100 GeV
23 Nov. 2018
Muon Reconstruction Efficiency in the Forward region (2.5 < |η| < 2.7)
The ATLAS Muon Spectrometer forward region 2.5 < |η| < 2.7 lies outside the Inner Detector acceptance (|η| < 2.5)
• No Tag and Probe can be applied for efficiency extraction
• No SF correction to the MC available
Important for physics analyses to extend the acceptance of the MS in this region
• Significant gain in acceptance O(~10%) • Very important for Higgs search
Introduced double ratio measurement for SF calculation
Systematic uncertainty ~3-5%
18
Eur. Phys. J. C (2014) 74:3130 Page 9 of 34 3130
SF =N Data(2.5< |ηfwd|< 2.7)N MC(2.5< |ηfwd|< 2.7)
N Data(2.2< |ηfwd|< 2.5)N MC(2.2< |ηfwd|< 2.5)
, (8)
where the numerator is the ratio of the number of Z → µµ
candidates in data and in MC for which one of the muons,called the forward muon, is required to be in the high-η region2.5 < |ηfwd| < 2.7 while the other muon from the Z decay,called the central muon, is required to have |η| < 2.5. Thedenominator is the ratio of Z → µµ candidates in data overMC with the forward muon lying in the control region 2.2 <
|ηfwd| < 2.5 and the central muon in the region |η| < 2.2.In both the numerator and denominator the central muon isrequired to be a CB muon while the forward muon can eitherbe a CB or SA muon. The simulation of muons with |η| < 2.5is corrected using the standard SF described in the previoussection.
The selection of the central muon is similar to that of thetag muon in the tag-and-probe method. It is required to havetriggered the event readout, to be isolated and to have trans-verse momentum pT > 25 GeV. The requirements for theforward muon include calorimeter-based isolation, requiringthe transverse energy ET measured in the calorimeter in acone of "R = 0.2 (excluding the energy lost by the muonitself) around the muon track, to be less than 10 % of themuon pT. The central and forward muons are required to haveopposite charge, a dimuon invariant mass within 10 GeV ofthe Z mass, and a separation in (η,φ) space of "R > 0.2.
Different sources of systematic uncertainties have beenconsidered: a first group is obtained by varying the pT andisolation cuts on the central muons and the dimuon masswindow. These variations produce effects of less than 0.3 %in the efficiency SF for the pT range 20–60 GeV. The effectof the calorimetric isolation on the efficiency SF yields anuncertainty of less than 1 %, which is estimated by compar-ing the nominal SF values with the ones extracted when nocalorimetric isolation is applied on the forward muons andby studying the dependence of this cut on the number ofpp interactions. The contribution from the background pro-cesses, mainly dimuons from b and b̄ decays, has been studiedusing MC background samples and found to be negligible.
The theoretical uncertainty from higher-order correctionsis estimated by varying the renormalization and factorizationscales in the POWHEG NLO calculation at the generatorlevel and is found to produce a negligible effect on the ratioof Eq. (8). The uncertainty from the knowledge of the partondensities is estimated by reweighting the PDFs used in theMC samples from CT10 to MSTW2008NLO [24] and bystudying, at the generator level, the effect of the uncertaintyassociated to the MSTW2008 PDF set on the double ratio ofEq. (8), obtaining an overall theoretical uncertainty of lessthan 0.55 %.
Effic
ienc
y
0.86
0.88
0.9
0.92
0.94
0.96
0.98
1
1.02
-1 = 8 TeV, L = 20.3 fbs
Z Data
|<2.7ηCB+SA Muons, 2.5<|ATLAS
[GeV]T
p20 40 60 80 100 120
Scal
e Fa
ctor
0.95
1
1.05
Fig. 7 Reconstruction efficiency for muons within 2.5 < |η| < 2.7from Z → µµ events. The upper plot shows the efficiency obtained asthe product of scale factor (Eq. 8) and the MC efficiency. The lowerplot shows the scale factor. The error bars correspond to the statisticaluncertainty while the green shaded band corresponds to the statisticaland systematic uncertainty added in quadrature
The efficiency in this region is obtained as the product ofthe SF and the “true” MC efficiency, calculated as the fractionof generator-level muons that are successfully reconstructed.The reconstruction efficiency and the SF for muons in thehigh-η region is shown in Fig. 7 as a function of the muonpT.
4.3 Scale factor maps
The standard approach used in ATLAS for physics analy-sis is to correct the muon reconstruction efficiency in thesimulation using efficiency scale factors (SFs). The SFs areobtained with the tag-and-probe method using Z → µµ
events, as described above, and are provided to the analysesin the form of η–φ maps. Since no significant pT dependenceof the SF has been observed, no pT binning is used in the SFmaps. Different maps are produced for different data tak-ing sub-periods with homogeneous detector conditions. Thewhole 2012 dataset is divided into 10 sub-periods. For eachanalysis, the final map is obtained as an average of the mapsfor all sub-periods, weighted by the periods’ contribution tothe integrated luminosity under study.
Figures 8 and 9 show the maps of the efficiencies mea-sured using the data in the η–φ plane and the correspondingScale Factors. The large data sample allows for a preciseresolution of localized efficiency losses, for example in the
123
Numerator: ratio of Z → μμ candidates in data and in MC for which one of the muons is in 2.5 < |η| < 2.7 while the other muon is in |η| < 2.5.
Denominator: ratio of Z → μμ candidates in data over MC with one muon lying in the control region 2.2 < |η| < 2.5 and the other muon in |η| < 2.2
Effic
ienc
y
0.86
0.88
0.9
0.92
0.94
0.96
0.98
1
1.02
-1 = 8 TeV, L = 20.3 fbs
Z Data
|<2.7ηCB+SA Muons, 2.5<|ATLAS
[GeV]T
p20 40 60 80 100 120
Scal
e Fa
ctor
0.95
1
1.05
23 Nov. 2018
Emulation of the MDT electronicsWork as a Muon Spectrometer simulation/digitization Project Leader (under CERN fellow)
• Responsible for the MDT digitization code which emulates the signal formation and the electronics response
Default digitization scheme provided a very detailed simulation of the signal formation and electronics response
• Takes into account most of the known effects that affect intrinsic resolution
• propagation of e clusters to the tube’s wire
• diffusion • cluster size
• degraded resolution effects near the tube wall
• Uses r-t relation from MC • Very detailed but cpu time consuming
• Important for MC production and pile-up 19
drift radius
23 Nov. 2018
New approach for electronics emulation in the ATLAS MDT MC digitization
Developed a new digitization scheme using r-t relations from ATLAS data
• converts Geant 4 drift radius into drift time, using a t − r relation from data
Resolution effects are incorporated into the data r-t relation • Get resolution on the radius σr and convert to σt by σt = σr/vdrift
Use data r-t information to smear the time according to the resolution
• Also provide a parametrized ADC response of the tube
20
Validation with Simulation
Comparing the residuals between r simuldrift and rdigit
drift obtained from thedefault and the new digitization schemes
RT Relation DB DigiTool
Mean -0.03031RMS 0.2145
calcdrift - rtrue
driftr-2 -1.5 -1 -0.5 0 0.5 1 1.5 20
5000
10000
15000
20000
25000
30000
35000
40000
Mean -0.03031RMS 0.2145
Residuals - RT_DB_Tool
Default digitization
Mean -0.02451RMS 0.2044
calcdrift - rtrue
driftr-2 -1.5 -1 -0.5 0 0.5 1 1.5 20
5000
10000
15000
20000
25000
30000
35000
40000
45000
Mean -0.02451RMS 0.2044
Residuals - MDT_Response_Tool
Distributions show comparable results in both tools
Comparing the average Cpu-time consumption of the driving digitization algorithm(MDT Digitizer) using Perfmon on single muon sample
Default digitization: 14.8ms
RT Relation DB DigiTool: 4.3ms
Improvement already by factor of x3.5 but there should be plenty of room for more!
Dinos Bachas (CERN) Status of Muon Simulation/DD/Digitization Muon Week 9 / 15
Validation with Simulation
Comparing the residuals between r simuldrift and rdigit
drift obtained from thedefault and the new digitization schemes
RT Relation DB DigiTool
Mean -0.03031RMS 0.2145
calcdrift - rtrue
driftr-2 -1.5 -1 -0.5 0 0.5 1 1.5 20
5000
10000
15000
20000
25000
30000
35000
40000
Mean -0.03031RMS 0.2145
Residuals - RT_DB_Tool
Default digitization
Mean -0.02451RMS 0.2044
calcdrift - rtrue
driftr-2 -1.5 -1 -0.5 0 0.5 1 1.5 20
5000
10000
15000
20000
25000
30000
35000
40000
45000
Mean -0.02451RMS 0.2044
Residuals - MDT_Response_Tool
Distributions show comparable results in both tools
Comparing the average Cpu-time consumption of the driving digitization algorithm(MDT Digitizer) using Perfmon on single muon sample
Default digitization: 14.8ms
RT Relation DB DigiTool: 4.3ms
Improvement already by factor of x3.5 but there should be plenty of room for more!
Dinos Bachas (CERN) Status of Muon Simulation/DD/Digitization Muon Week 9 / 15
New approach x4 faster that default digitization
Current research activity
21
23 Nov. 2018
Artificial intelligence in Exotics resonance search
Application of ML in Exotics ZV → ℓℓqq
The current signal selection efficiency is roughly ~0.45 for the merged selection
• Can we do better than that at the same background rejection?
Use Machine Learning(ML) and Deep Neural Networks(DNN) to improve sensitivity achieved with cut-based analysis
• Currently applied to Merged llqq analysis
22
23 Nov. 2018
Data preparation steps
Work flow with hands-on making a DNN for classification presented during the latest ATLAS Exotics workshop in Rome. Based on Jupyter Notebooks and Cern SWAN service-> anyone can exercise online! https://gitlab.cern.ch/kbachas/RomeExoticsWorkshopHandsOnML
23
Standalone python scripts to read analysis ROOT
ntuples and convert to Pandas
Dataframes
Mixing and shuffling of Signal and
Background events
Each input feature is scaled such that it
has 0 mean and unit variance. This is
commonly used in ML applications to
help the convergence of the
gradient decent process
Input features arrays (X) and target labels (y) are created for
each event
Due to imbalanced classes, mapping class indices to a weight, used for
weighting the loss function (during
training only). Tells the model to "pay more attention" to
signal being statistically under-
represented
ROOT to Pandas Data Mixing Feature
ScalingInputs/Targets
Class Reweighting
23 Nov. 2018
Example of Deep Neural Network setup in llqq analysis for S/B classification
24
Setup ML libraries and tools: Miniconda Numpy, scikit-learn, Keras, Pandas, Theano
Data Split to Train and Test Samples
Test30%
Train70%
Network implemented with Keras using Theano backend
Very simple input variables to the NNTrain with basic kinematic information
Epochs 100
Dropout 20%
Input/Hidden LayerActivation ReLu
Output Layer activation Sigmoid
loss function binary_crossentropy
Hidden layers 3
Nodes per layer 60Training/TestingAt each signal mass point At 2 points in the analysis flow
23 Nov. 2018
DNN performance in merged analysisScan 12 combinations of N_neurons, hidden layers and variable set
• Adding more input variables improves DNN performance as expected
• DNN with 64 nodes, 3(or 2) layers and full info performs the best
• Adding more hidden layers does not improve significantly the result
Significant gain in merged signal efficiency @ same background rejection wrt cut-based analysis
25
23 Nov. 2018
DNN Score distributions
26
ggF 700 GeV ggF 1000 GeV
ggF 700 GeVggF 1000 GeV
After full ggF merged selection
After early selection
Nice separation power illustrated by the DNN score on Signal and background dedicated samples
23 Nov. 2018
Problem: Since the mass of the resonance is unknown how to best train a DNN?
• Train a single DNN at one intermediate value of the mass and use it for all other mass values
• Best for trained mass point in but performance degrades at other masses
• Train a set of DNNs for a set of mass values
• Best S/B performance at each trained point but discontinuities in selection efficiencies across masses, and interpolation of the observed limits not possible
• We don’t have infinite MC statistics and CPU resources! 27
23 Nov. 2018
‘Parameterized neural networks for high-energy physics ‘
• Use the approach as in Eur. Phys. J. C (2016) 76:235 where a single parameterized DNN tackles the full set of related tasks
• This is done by simply extending the list of input features to include one or more parameters that describe the larger scope of the problem such as a new particle’s mass.
• A parameterized classifier can smoothly interpolate between masses and replace sets of classifiers trained at individual values.
• Simplifies the training process and gives improved performance at intermediate values
28
Eur. Phys. J. C (2016) 76 :235 Page 3 of 7 235
t
t̄
W+ +
νq
q
b̄
bq
q̄
X
W−
g
g
t
t̄
W+ +
νq
q
b̄
b
W−
Fig. 3 Feynman diagrams showing the production and decay of thehypothetical particle X → t t̄ , as well as the dominant standard modelbackground process of topquark pair production. In both cases, the t t̄pair decay to a single charged lepton (ℓ), a neutrino (ν) and severalquarks (q, b)
most powerful decay mode, in which t t̄ → W+bW−b̄ →qq ′bℓνb̄. The dominant background is standard model t t̄production, which is identical in final state but distinct inkinematics due to the lack of an intermediate resonance.Figure 3 shows diagrams for both the signal and backgroundprocesses.
We first explore the performance in a one-dimensionalcase. The single event-level feature of the network ismWWbb,the reconstructed resonance mass, calculated using tech-niques described in Ref. [14]. Specifically, we assumeresolved top quarks in each case, for simplicity. Eventsare simulated at the parton level with madgraph5 [15],using pythia [16] for showering and hadronization anddelphes [17] with the ATLAS-style configuration for detec-tor simulation. Figure 4a shows the distribution of recon-structed masses for the background process as well as sev-eral values of mX , the mass of the hypothetical X particle.Clearly the nature of the discrimination problem is distinctat each mass, though similar across masses.
In a typical application of neural networks, one might con-sider various options:
• Train a single neural network at one intermediate value ofthe mass and use it for all other mass values as was done inRefs. [11,12]. This approach gives the best performanceat the mass used in the training sample, but performancedegrades at other masses.
• Train a single neural network using an unlabeled mixtureof signal samples and use it for all other mass values. Thisapproach may reduce the loss in performance away fromthe single mass value used in the previous approach, butit also degrades the performance near that mass point, asthe signal is smeared.
• Train a set of neural networks for a set of mass valuesas done in Refs. [9,10]. This approach gives the bestsignal-background classification performance at each ofthe trained mass values. However, performance degradesfor mass values away from the ones used in training.Most importantly, this approach leads to discontinuitiesin selection efficiencies across masses, and interpolation
Fig. 4 Topdistributions of neural network input mWWbb for the back-ground and two signal cases. Bottom, ROC curves for individual fixednetworks as well as the parameterized network evaluated at the truemass, but trained only at other masses
of the observed limits is not possible, as the degradationof the performance away from the training points is notdefined.
In contrast, we train a single neural network with an addi-tional parameter, the true mass, as an input feature. For alearning task with nevent-level features and m parameters,one can trivially reconcieve this as a learning task with n+mfeatures. Evaluating the network requires supplying the setof event-level features as well as the desired values of theparameters.
We note that Ref. [18] previously applied a similar ideawith the same goal of improving the interpolation amongmodel parameters. However, in that study the application ofBDTs led to a marked decrease in sensitivity at each pointcompared to isolated algorithms at specific values, and nodemonstration was made of the ability to interpolate complexproblems in high-dimensional spaces.
123
Eur. Phys. J. C (2016) 76:235
235 Page 2 of 7 Eur. Phys. J. C (2016) 76 :235
x1x2
fa(x1 ,x2)
= a
x1x2
f(x1 ,x2, )
x1x2
fb(x1 ,x2)
= b
Fig. 1 Left, individual networks with input features (x1, x2), eachtrained with examples with a single value of some parameter θ = θa, θb.The individual networks are purely functions of the input features. Per-formance for intermediate values of θ is not optimal nor does it nec-essarily vary smoothly between the networks. Right, a single networktrained with input features (x1, x2) as well as an input parameter θ ; sucha network is trained with examples at several values of the parameter θ
tion of θ̄ introduces additional considerations in the trainingprocedure. While traditionally the training only requires theconditional distribution of x̄ given θ̄ (which is predicted bythe theory and detector simulation), now the training datahas some implicit prior distribution over θ̄ as well (which isarbitrary). When the network is used in practice it will beto predict y conditional on both x̄ and θ̄ , so the distributionof θ̄ used for training is only relevant in how it affects thequality of the resulting parameterized network – it does notimply that the resulting inference is Bayesian. In the studiespresented below, we simply use equal sized samples for a fewdiscrete values of θ̄ . Another issue is that some or all of thecomponents of θ̄ may not be meaningful for a particular targetclass. For instance, the mass of a new particle is not meaning-ful for the background training examples. In what follows,we randomly assign values to those components of θ̄ accord-ing to the same distribution used for the signal class. In theexamples studied below, the networks have enough general-ization capacity and the training sets are large enough thatthe resulting parameterized classifier performs well withoutany tuning of the training procedure. However, the robust-ness of the resulting parameterized classifier to the implicitdistribution of θ̄ in the training sample will in general dependon the generalization capacity of the classifier, the number oftraining examples, the physics encoded in the distributionsp(x̄ |θ̄ , y), and how much those distributions change with θ̄ .
3 Toy example
As a demonstration for a simple toy problem, we construct aparameterized network which has a single input feature x anda single parameter θ . The network, with one hidden layer ofthree nodes and sigmoid activation functions, is trained usinglabeled examples where examples with label 0 are drawnfrom a uniform background and examples with label 1 are
Fig. 2 Top training samples in which the signal is drawn from a Gaus-sian and the background is uniform. Bottom, neural network responseas a function of the value of the input feature x , for various choices ofthe input parameter θ ; note that the single parameterized network hasseen no training examples for θ = −1.5,−0.5, 0.5, 1.5
drawn from a Gaussian with mean θ and width σ = 0.25.Training samples are generated with θ = −2,−1, 0, 1, 2;see Fig. 2a.
As shown in Fig. 2, this network generalizes the solu-tion and provides reasonable output even for values of theparameter where it was given no examples. Note that theresponse function has the same shape for these values (θ =−1.5,−0.5, 0.5, 1.5) as for values where training data wasprovided, indicating that the network has successfully param-eterized the solution. The signal-background classificationaccuracy is as good for values where training data exist as itis for values where training data does not.
4 1D physical example
A natural physical case is the application to the search for newparticle of unknown mass. As an example, we consider thesearch for a new particle X which decays to t t̄ . We treat the
123
Eur. Phys. J. C (2016) 76:235
23 Nov. 2018
Parameterized DNN Performance on ‘unseen’ mass points
• Compare the parameterized DNN trained on (ALL-1600) mass points and the one trained on ALL, both evaluated at Mx=1600.
• Both curves overlap in the ROC and AUC metrics
• Proves that the parameterized DNN is able to generalize to cases not seen during the training phase by interpolating the signal behaviour from nearby mass points
29
23 Nov. 2018
Research interests - outlookThe expected increase in luminosity at the LHC upgrade is extremely challenging
• Need detectors that can cope with the harsh radiation environment • Need new techniques to reconstruct and analyze the HL-LHC data
I would be very keen to work on New Physics searches and/or detector development for Run-3 at the LHC
Ideally, I would like to combine the above with my broad experience in software, simulation and Machine Learning
• The search of New Physics, especially in the fierce environment of HL-LHC would call for new techniques to be developed
• Jet imaging, reconstruction in the tracker, event classification, regression problems
• Most of them can find applications to the industry and society • Medicine(imaging, regreassion, classification), big data/internet applications etc
I believe that my research profile would bring additional expertise, state-of-art analysis methods and skills and enthusiasm in the research activities of Demokritos
30
23 Nov. 2018
BACKUP SLIDES
31
23 Nov. 2018Lutz Feld, Uni Freiburg
SCT Forward Disk
outer modules
inner modules
cooling block
power tapes
optical fibres
middle modules(on backside)
ATLAS Semi-Conductor Tracker and Inter-strip Capacitance
Basic detecting element (module): p-type semiconductor implant, divided into strips, placed on an n-type semiconductor bulk. (pn junction)
• Aluminum layer on top of p+ implant strips allows signal to be collected by the read-out electronics.
Operating Principle: collection of charge released in the depleted volume of a reverse biased diode
Cis is the capacitance formed by neighboring strips is one of the primary sources of the detector's noise
32
23 Nov. 2018
Cis dependence on time, R.Humidity and Temparature
Cis decreases exponentially with time after bias application
Cis stabilization time varies between a few minutes at high RH and days for low RH
Strong correlation between the time constant of Cis and T. • Decreasing T causes the time constant to increase dramatically.
Modules need to be switched on long before measurements start, in order for the detectors to settle and minimize the noise due to Cis and I
33
RH ~ 1.5%T ~ 25oC
23 Nov. 2018
Exraction of the gg->4l component
34
Main Results (3)
2015/7/14 Tuesday Y. Wu 6
Extraction of gg->4l
gg component is extracted
from data in m(4l)>180GeV
region, where qq contribution
is predicted at NNLO
The determined gg signal strength
w.r.t. the LO prediction is
Post-Fit
Compatible with NNLO k-factors calculated for off-shell Higgs or for the
interference between off-shell Higgs and non-Higgs gg->4l.
Use variable with low theory modeling uncertainties to extract a signal strength on gg component Obvious choice : m(4l) Assume the best current knowledge on the qqZZ component : 1 parameter fit
23 Nov. 2018
Searches for heavy ZW and ZZ resonances in the llqq final state at 13TeV (2/2)
Backgrounds: Z+Jets, Dibosons, top, W +Jets
Leading systematics: Large-R jet energy scale/resolution and sub-structure variables
35
23 Nov. 2018
Example of DNN setup approach for S/B classification
Signal: ggH at 1000 GeV point , Background: Z+jets, ttbar and Diboson events
Preselection: Number of leptons = 2 (ee or μμ) and Number of fat jets >= 1 (highest pT jet selected)
DNN implemented with Keras and Theano backend
Number of hidden layers: [2,3,…]
Number of neurons per hidden and input layer [32,64,…]
36
Epochs 100
Dropout 0.2
Input/Hidden Layer Activation ReLu
Output Layer activation Sigmoid
loss function binary_crossentropy
Input Variables
Leptons pt, E
Leptons eta, phi
Fatjet pt, E
Fatjet eta, phi
Fatjet D2,C2
Z(ll) mass, pt MET
Set 0 x x x x x x xSet 1 x x x xSet 2 x x
Parameter scan
23 Nov. 2018
DNN trained on all mass points - Performance
• DNN trained on all mass points (1200, 1400, 1600, 1800, 2000)
37
23 Nov. 2018
S/B with DNN in llqq analysis. Example Data Samples
For illustration only as new MC production is expected and denser mass grid
38
Signal Samples
Even
ts
0
20,000
40,000
60,000
80,000
AEer Dilepton AEer ggF Merged
46770
67432
41513
77099
36469
66395
ggF 700ggF 1000ggF 2000
Signal points: ggF at 700, 1000, 2000 GeV Background: Z+jets, Top and Diboson
MC Samples
Number of leptons = 2 (ee or μμ) Number of fat jets >= 1
Preselection applied
Early: After dilepton selection Late: After full merged ggF selection
DNN trained at 2 points of the analysis flow
Background Samples
Even
ts1E+00
1E+02
1E+04
1E+06
AEer Dilepton AEer ggF Merged
212364
6168127
428
1341850695
308367
DibosonTopZ+jets
23 Nov. 2018
More detailed studiesCan look into which classes confuse the DNN most
Moved to multi-classification problem
K-Fold cross validation • Useful technique to
control overfitting
39
ggF 700 GeV ggF 1000 GeV
November 21, 2017
K-Fold cross validation
• Useful technique to check on performance and overfitting
• estimate variance of expected performance
• Computationally intense
• Split training set into K-folds
• Train on (K-1) folds, test on 1st-fold, then iterate
• Use average estimated performance on K-folds
11
November 21, 2017
K-Fold cross validation
• Useful technique to check on performance and overfitting
• estimate variance of expected performance
• Computationally intense
• Split training set into K-folds
• Train on (K-1) folds, test on 1st-fold, then iterate
• Use average estimated performance on K-folds
11