Sung Hak Lim, Mihoko Nojiri (JHEP, 2018) Amit - - PowerPoint PPT Presentation
Sung Hak Lim, Mihoko Nojiri (JHEP, 2018) Amit Chakraborty, Sung Hak Lim, Mihoko Nojiri ( arXiv 1904.02092 ) 1 LHC
Sung Hak Lim, Mihoko Nojiri (JHEP, 2018) Amit Chakraborty, Sung Hak Lim, Mihoko Nojiri ( arXiv 1904.02092 )
1
統計は今後も増える（発見の物理から精密測定の物理へ） 標準模型のプロセスのずれを探す。 より厳しいカットをかけて、面白いイベントを探す 過去の成功体験（LEP) vs QCD 特有の難しさ
high pT objects (Events in Tail) soft object, mono something
Physics outputs effective operator, top partner dark matter..
hadronization, PDF, parton shower modeling, …
*システマティクスへの理解が重要 ** 解析のスピードアップの必要
馬鹿でかい統計
2
機械学習を使って イベントの再構成の効率化、高速化 ジェットやイベントの特性を学習させて、top W H などのを取り出す 目的 ルミノシティに見合ったアウトプット QCD の不定性を実データを使って迂回（理解しないで済 ます）あるいはソフトはプロセスの理解の促進
3
Jet physics : QCDの中ではソフトな物理を含むにも変わらず比較的成功し ている (Theoretical understanding + computation) 理論と現象論パートンシャワーー＞ハドロン 実験データとの比較 Jet reconstruction algorithm ( kT, CA, antikT) IRC safe アルゴリズム numerical progress (fastjet N^3-> Nlog N) Jet substructure (mass drop), Hi,,s W, Top reconstruction (BSM search) minimal Validation Analysis (leaving optimization to algorithm) →機械学習
theory and computational developments
4
x1 · · · xn wi1 · · · win bi P ϕ(·) ϕ(wijxj + bi) inputs weights bias activation
x y Rectified Linear Unit(ReLU) ϕReLU(x) = xθ(x)
x1 x2 x3 ˆ y
1 − 1 jetη
φ
[arb. unit]
i T,p
pixel ∈ iΣ b g Higgs jet MG5+PY8+Delphes
1 − 1 jetη
φ
[arb. unit]
i T,p
pixel ∈ iΣ g QCD jet MG5+PY8+Delphes
Input:Jet images QCD Higgs
φ: source of non linearity
rearrangement
wij, bi
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Input layer Hidden layer 1 Hidden layer 2 Output layer Bias nodes Calorimeter image
Arbitrary Units
QCD Jet
O
W’→ WZ event Convolutions Convolved Feature Layers Max-Pooling Repeat
Repeat
CNN correct nearby image first 1511.05190
DNN (all bins in a line) Recursive(Taoli Cheng 1711.02633) “ Maybe we do not know physics behind, architecture do the job for you”??
Almeida et al 1501.05968 6
7
0.1 0.2 0.3 0.4 0.5 Signal efficiency
4 −10
3 −10
2 −10
1 −10 1 10 Background efficiency
(13 TeV)
CMS
Simulation Preliminary
DeepAK8 DeepAK8-MD ImageTop ImageTop-MD 32 τ + SD m + b 32 τ + SD m HOTVRTop Tagger performance in MC
probability for t quarks by up to ∼ 50% (depends on pT).
algorithms, particularly in the low pT range.
information to τ32. In the low-pT region, the gain is mainly due to the use of larger-cone jets (i.e. jets clustered with R = 1.5).
in this regime.
ImageTop and DeepAK8 algorithms.
better
top vs QCD
Significant improvement from the new developments
0.1 0.2 0.3 0.4 0.5 0.6 0.7 Signal efficiency
4 −10
3 −10
2 −10
1 −10 1 10 Background efficiency
(13 TeV)
CMS
Simulation Preliminary
DeepAK8 DeepAK8-MD ImageTop ImageTop-MD 32 τ + SD m + b 32 τ + SD m BEST HOTVR7/22/19
15
Narain, Boost 2019
やったことのまとめ
のある量 (~40) (jet spectrum) の DNNをやった
るかみる。 They are Equivalent!
8
Monte Carlo →Parton splitting + hadronization
(p, R, z) describe parton shower spritting p2
1 + p2 2 = p2[(1 − z)2 + z2
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<latexit sha1_base64="QuRWz4oW8k4hvyGTCzGqUjp7PI=">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</latexit><latexit sha1_base64="SA5WSHWxz+lFuhu/ulJx9xifMk=">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</latexit><latexit sha1_base64="SA5WSHWxz+lFuhu/ulJx9xifMk=">ACeXichVHLSsNAFD2Nr1pf9bEQ3ERFqYrlpi4UQSi6cemrKvgoSZxqaJqEJC20xR9wLbhwo4KI+hlu/AEX/QRxqeBCF96mBVFR7zAzZ87c+fMjOaYhucTVUJSQ2NTc0u4NdLW3tHZFe3uWfsvKuLlG6btrupqZ4wDUukfM3xabjCjWnmWJDy5U9zcKwvUM21rzi47Yyan7lpExdNVnKh3tk520wj0xJzu7CbkUyZLY+noMUpCPknUOpgODm0PXFcSRaX7OgVtrEHGzryEHAgs/YhAqP2xYUEBzmdlBmzmVkBPsCh4iwNs9ZgjNUZrM87vNq85avK7W9AK1zqeY3F1WyhihB7qmZ7qnW3qk919rlYMaVS9FnrWaVjprqP+1d/VTmefRx8qv707CODmcCrwd6dgKneQq/pC6WT59XZlZHyKF3QE/s/pwrd8Q2swot+uSxWThHhD1C+P/dPsJ6IKxRXlvkn5lGLMAYwhBi/9zSWMQSUnxuCWe4xk3oTRqUYtJ4LVUK1TW9+BLS1Afh9JLb</latexit><latexit sha1_base64="E85+X0N0M56T9+5/8l5WE/CNGRA=">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</latexit>sum over pairs of jet constituents proportional to momenta of particles to be IRC safe (C-correlator R
R’
p(1-z)y p(1-z)(1-y)
p1 = pz
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S2(R, ∆R) = P
ij pT ipT j for R < Rij < R + ∆R
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for R = 0, 0.1, 0.2 …
9
10
The Lund Plane is the phase space of these emissions: it naturally factorises perturbative and non-perturbative effects, UE/MPI, etc. m ~ z*ΔR2
The jet mass is just one diagonal line in this space … So what if we could measure the whole thing?
25
] 2 ) ungroomed T / p soft drop [(m 10 / d log σ ) d resum σ (1 / 0.2 0.4 0.6 ATLASaround a hard core which represents the
core
最近の Lund Plane とも近い (Dreyer, Salam, Soyez, JHEP 12(2018)064) ( CA jet clustering のヒストリーを２次元面に射影） Jet Spectrum ~ Energy Energy correlation, C-correlator (1996 Tkachov) （IRC safe な要件で作った量 比較的起源の古い概念） Roloff Boost 2019
Z PT (~ R) = X
i∈J
pT,i (~ R ~ Ri),
hi = ˆ Ψi[PT ]
hi = w(0)
i
+ Z d~ R PT,a(~ R)w(1)
i,a(~
R) + 1 2! Z d~ R1d~ R2 PT,a(~ R1)PT,b(~ R2)w(2)
i,ab(~
R1, ~ R2) + · · · ,
+ 1 2! Z dR S2,ab(R) w(2)
i,ab(R) + · · ·
~ hi = w(0)
i
(~ xkin) + Z dR S2,A(R) w(2)
i,A(R; ~
xkin) 2 + 1 2 Z dR1dR2 S2,A(R1)S2,B(R2) w(4)
i,AB(R1, R2; ~
xkin) 12 + · · · .
jet = energy flow (+ …) *classifier using energy flow
If w only depends on R12
*classifier using Jet spectrum
calorimeter hit position
11
Jtrim = [
a
pT,Ja pT,J ≥ftrim
Ja .
X
∈
S2,trim(R; ∆R) = 1 ∆R X
i,j∈Jtrim
pT,i pT,j · I[R,R+∆R)(Rij),
S2,soft(R; ∆R) = S2(R; ∆R) S2,trim(R; ∆R).
1 − 1
jet
η
1 − 1
jet
φ
[arb. unit]
i T,
p
pixel ∈ i
Σ b g Higgs jet MG5+PY8+Delphes
momentum density plot
S2soft jet~ subjets(IRC safe) subjets above certain pt cut =>trimmed jet
= “soft-hard correlation” + “soft-soft” correlation =“hard-hard” correlation
S2trim
subjets
12 ほんとは R に寄った pt カットにした方が良かった
1 − 1
jet
η
1 − 1
jet
φ
[arb. unit]
i T,
p
pixel ∈ i
Σ b g Higgs jet MG5+PY8+Delphes
0.5 1 1.5 2
R
0.2 0.4 0.6 0.8
2 T,jet
p ;0.1) / R (
2
S 0.1 ;0.1) R (
2
S ;0.1) R (
tr 2,
S = 0.70
h
y = 0.24
σ
y = 308 GeV
T,jet
p = 287 GeV
tr T,jet,
p = 122 GeV
jet
m = 115 GeV
tr jet,
m Higgs jet MG5+PY8+Delphes
1 − 1
jet
η
1 − 1
jet
φ
[arb. unit]
i T,
p
pixel ∈ i
Σ g QCD jet MG5+PY8+Delphes
0.5 1 1.5 2
R
0.2 0.4 0.6 0.8
2 T,jet
p ;0.1) / R (
2
S 0.1 ;0.1) R (
2
S ;0.1) R (
tr 2,
S = 0.06
h
y = 0.13
σ
y = 312 GeV
T,jet
p = 252 GeV
tr T,jet,
p = 101 GeV
jet
m = 32 GeV
tr jet,
m QCD jet MG5+PY8+Delphes
QCD jet Empty region
weak second peak disappear after trimming
Higgs jet
13
Two point correlation pick up most important effects. (Or NN with jet image “find” two-point correlation by itself ( not proven) Chance to understand the distribution contributing to the decision?
Input Hidden1 Hidden2 · · · Hiddenn Output Activation: ReLU ReLU ReLU softmax pT,j mj bias nodes {S2} b b · · · b
The results only slightly worse than CNN CNN
for 300GeV<pT<400GeV and 100GeV<mj<150GeV
Jet spectrums
14
layer: Input Hidden1 Hidden2 Activation: ELU ELU pT,j mj bias nodes b b layer: Input {S2} Inner product Output softmax × × Σ
y = (1, 0)[Higgs] y = (0, 1)[jet]
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sha1_base64="lSIYasva8kXPlCDm90ItPk9atsU=">AD3ichZLahRBFIb/bi+J4yWtbgQ3g0OCgynRaI4qAbl7k4SWASQndZGdv0dLfdlYFx8AXcuXLhSmEQ8QHcieBGH8BFHkFcRnDjwr9rWlHSxVdapOfef8dQmzOCqMyK7j7t/4ODU9KHa4SNHj814x0+sFOlOrnRbpXGar4VBoeMo0W0TmVivZbkOemGsV8PtG6V/ta/zIkqTW2aQ6Y1e0E2irUgFhkup18YITS6iJBgSOsexwA5a4ABHqDGfg5X2c7Cx3kIzqFOqke/wR3u65Hr4CYjdFkLbJAaQdHzMytkfbKT5F1mNZaq0auZ/aflGx6DWmKLfVJw6+MBqykHrPsc5QKaXsMFUZ2NCOGa5g7VCOILOphzaR4RFKv7ZS1kVtDP6FLbZdznrVKultDJmYWnFLDFbTrKOWfkgL2RP3slL+Shf/xpraGOUWgYcwzGrs82Zh6eWv/yX+n6NP6h/ajbYwmWrtXzozK6Up1Bjvn/8d7ylaXZ4Zw8k0/U/1R25S1PkPQ/q9GiXnqCGh/A/26J42VC01fmv7ixUbrevU0ziNM/b7XEKLH2UBbSjHc+ada07LfeS+cl+7b8ZbXadiTuKX4r7/Bq2/qrA=</latexit><latexit sha1_base64="lSIYasva8kXPlCDm90ItPk9atsU=">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</latexit><latexit sha1_base64="lSIYasva8kXPlCDm90ItPk9atsU=">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</latexit><latexit sha1_base64="Y5iPgCMDp2CUfE8JK59CEx5+A=">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</latexit>w1 and w2 are trained to be MAX for Higgs events and MIN for QCD jet events
jet interior information
h = X
k
Sk
2,trim wk 1 +
X
k
Sk
2,soft wk 2,
w X
w1 and w2
This simple classifier performs nearly as good as previous ones 15
Jet Image CNN
New one
16
inner product
0.0 0.2 0.4 0.6 0.8 1.0
R
QCD −0.4 −0.3 −0.2 −0.1 0.0 0.1 0.2 0.3 0.4 Higgs
f2(R)
Network name: SNN.TwoClass.hj.py8 (temporary label)
Higgs jet vs QCD jet
S2,trim(R) S2(R)−S2,trim(R)
1.0 1.2 1.4 1.6 1.8 2.0
R
QCD −4 −3 −2 −1 1 2 3 4 Higgs
f2(R) MG5+PY8+Delphes pT,J = 350 GeV mJ = 120 GeV
training
emission at R> 1.0 →QCD
trimmed jet emission at R~0.8→Higgs
QCD jet vs Higgs jet
for Ssoft
2
(R)
<latexit sha1_base64="TFSj09kyzWmca/5vCi9fJ5DBDCU=">ACl3ichVG7SgNRED2urxgfidoNmJUtAmzaXw0CoJYmsSo4CPsrjdxyWZ32b0JxOAP+AMKVgoi4j/YWOgPWPgJYqlgY+FksyAq6hR3Zs6dc2bmXt21TF8SPbYorW3tHZ2Rrmh3T29fLN4/sO47Fc8QOcOxHG9T13xhmbISVNaYtP1hFbWLbGhl5Ya9xtV4fmY6/Jmit2ylrRNgumoUmGnPgwCnDgYRTjyGIXdVxwVubcZ7wAiUPkGU2xn0IG0xjPxOUpMBGfwZqGCQ2qoTv8Q29ljOQIWlBWyWNWB4yY+tqC4DK2w40bi9hBveCm0aZW+EqwRUaoyU+i5xthajNeUPTD9gGd7GChQ5hQl6oCt6oXu6pid6/1WrHmg0Zqmx15tc4eZjR0PZt39ZfYS+5+sP2eW/Kizwawmz+4GSGMLo8mvHhy/ZOczE/VJOqdnv+MHumWN7Cr8ZFWmROEeUPUL8/989gPZVUKamU4nFufArIhjBGH+jihksYgWryHfE9zgDvfKsLKgLCsrzVKlJeQM4osp6Q8tuZNs</latexit><latexit sha1_base64="5sEkh+pC9oUagiNIG3nbO+H5Q4s=">ACl3ichVHLSsNQED3Gd31V3ShuirWimzIRwcdGQRCXtrUq+ChJvK3BNAnJbUGLP+APKLhSEBH/wY0L/QEXfoK4rODGhdM0IFrUWdyZOXfOmZl7dcyfUn03KQ0t7S2tXd0Rrq6e3r7ov0D675T8gyRNRzL8TZ1zReWaYusNKUlNl1PaEXdEhv6wVLtfqMsPN907DV56IqdolawzbxpaJIhJzqMPBx4iGEMGeyigivOipz7jOchcYwco1PsJ5DGJMZy0TglKbBY6CGQRyhrTrRa2xj+UMlFhawGZAxY0buJjCyoILmM73Ej9hJmcC+4aYS5Ja4SXKExesBngbOtELU5r2n6AdvgLlaw0DGvkKAnuqEqPdItvdDHr1qVQKM2yF7vc4Vbq7vZCjz/i+ryF5i/4v158ySH3U2mNXk2d0AqW1h1Pnlo9NqZj6dqIzTJb3y/Bf0TPe8gV1+M65SIn2OCH+A+vO5G4P1qaRKSTU1HV+cC7+iAyMY5W9UMYNFrGAVWe57hjs84FEZVhaUZWlXqo0hZxBfDMl9QkuWZNu</latexit><latexit sha1_base64="5sEkh+pC9oUagiNIG3nbO+H5Q4s=">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</latexit><latexit sha1_base64="TFSj09kyzWmca/5vCi9fJ5DBDCU=">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</latexit>h = X
k
Sk
2,trim wk 1 +
X
k
Sk
2,soft wk 2,
w X
coefficient w1 and w2 is instructed to depend on mass and momentum but not jet spectrum
interior
soft emission →QCD
17
0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2
R
QCD 0.4 0.3 0.2 0.1 0.0 0.1 0.2 0.3 0.4 Higgs
h S2(R)w(R) i [arb. scale] Higgs jet vs QCD jet MG5+PY8+Delphes
Higgs, hS2,trim(R)w1(R)i Higgs, hS2,soft(R)w2(R)i QCD, hS2,trim(R)w1(R)i QCD, hS2,soft(R)w2(R)i pT,J 2 [340, 360] GeV mJ 2 [110, 120] GeV
(1) (2) (3)
18
two hard substructure making peaks in S2trim
Large Hard-and-soft correlation soft emission near jet boundary
Higgs QCD
19
quark ->パートンシャワー -> たくさんの quark gluon (Qstart, Qhadron, αs) Herwig anglar ordering (より大きい角度から小さい角度に向かって放出が起こる。 メリット angular ording が保証されている Pythia, Sherpa pT ordering(よりkT の大きい放出から小さい放出が順次起こる）
パートンシャワーからハドロンへ( Qhadron) クラスター模型（Herwig とSherpa) parton shower の中のqqbar から近いものを見 つけてペアを作り崩壊させる。 String 模型（Pythia) color connection がちぎれてMeson ができる。 parton shower + hadronization のパラメーターがデータで調整されたものが、使われ ている現象論的な量. (Matrix element といった精度が評価できる量と異なる。 できるだけ、 resumして評価できるところは抑えて、その範疇でjet の物理をやりたい というのが今の流れ
20
21
0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2
) R Δ / R / d ln(
jets) dN
jets(1/N
ATLAS Preliminary
= 13 TeV, 139 fb s ) < 1.25 z 0.97 < ln(1/
Data Pythia 8.230 Powheg + Pythia 8.230 Sherpa 2.2.5 (Cluster Had.) Sherpa 2.2.5 (String Had.) Herwig 7.1.3 (Dipole Shower) Herwig 7.1.3 (Angular Shower)
0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 ) R Δ / R ln( 0.6 0.8 1 1.2 1.4
Ratio to Data
ences between d and fected egion
The Lund Jet Plane
1 2 3 4 5 6 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2
) z / d ln(1/
jets) dN
jets(1/N
ATLAS Preliminary
= 13 TeV, 139 fb s ) < 1.00 R Δ / R 0.67 < ln(
Data Pythia 8.230 Powheg + Pythia 8.230 Sherpa 2.2.5 (Cluster Had.) Sherpa 2.2.5 (String Had.) Herwig 7.1.3 (Dipole Shower) Herwig 7.1.3 (Angular Shower)
1 2 3 4 5 6 ) z ln(1/ 0.6 0.8 1 1.2 1.4
Ratio to Data
ent generators and unfolded fected by
different generators and unfolded data
wide-angle splitting is affected by parton shower
MPI (as expected)
MC は現象をうまく記述しているか?
Lund jet Plane (Jet core からの分布）
(zE , ΔR) from Boost 2019 talk by Jennifer Roloff
計算可能
計算がむずかしい
0.0 0.2 0.4 0.6 0.8 1.0
R
HW7 −0.4 −0.3 −0.2 −0.1 0.0 0.1 0.2 0.3 0.4 PY8
w(R) × 104 ˆ Rb¯
b
PY8 vs HW7
w1(R) w2(R)
1.0 1.2 1.4 1.6 1.8 2.0
R
HW7 −4 −3 −2 −1 1 2 3 4 PY8
w(R) × 104 Higgs jet
pT,J = 350 GeV mJ = 115 GeV
* 違うMC で同じプロセスを作って、機械学習で分類させてみる。 * ハードなサブジェットの分布は同じ。 * ソフト部分は違う。(そしてそれにかなり依存して分類をやっている。)
0.0 0.2 0.4 0.6 0.8 1.0
R
HW7 −0.4 −0.3 −0.2 −0.1 0.0 0.1 0.2 0.3 0.4 PY8
w(R) × 104 ˆ Rb¯
b
PY8 vs HW7
w1(R) w2(R)
1.0 1.2 1.4 1.6 1.8 2.0
R
HW7 −4 −3 −2 −1 1 2 3 4 PY8
w(R) × 104 sgluon jet
pT,J = 350 GeV mJ = 115 GeV
0.0 0.2 0.4 0.6 0.8 1.0
R
HW7 −0.4 −0.3 −0.2 −0.1 0.0 0.1 0.2 0.3 0.4 PY8
w(R) × 104 ˆ Rb¯
b
PY8 vs HW7
w1(R) w2(R)
1.0 1.2 1.4 1.6 1.8 2.0
R
HW7 −4 −3 −2 −1 1 2 3 4 PY8
w(R) × 104 QCD jet
pT,J = 350 GeV mJ = 115 GeV
Higgs boson Scalar gluon q or gluon
22
color singlet or octet vs QCD
23
singlet (Higgs )
機械学習が注目する特徴すら 同じでない場合
MC によらず機械学習が 注目している分布が同じ
ML H-QCD ジェット分類で[実際に参照されている分布]を同定 した。 ジェットイメージ NxN to スペクトル N (N~20 for our case.) とても簡単に収束するし、結果も安定。ジェットイメージを 使ってるCNNはかなりの場合ジェットスペクトルを発見するた めに時間を無駄にしてると思う。 機械学習はソフトな分布も使っている。MC と実データが違う ことからくる不定性がある。 自然がパートンシャワーで記述できるとは限らない。 トップはもっと複雑で計算中
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