Rare Kaon Decays
A little bit of history
Today: the CP violating rare kaon decay

From Neural Net Deep Learning, Fourier Transform ….
…. to photon momentum and pico-second timing
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A little bit of history:
First dedicated symmetry breaking experiment:
Mid-1956 to Jan-1957, Wu et. al. measured σ ⋅ p e , a parameterof “odd” parity of polarized Co60 β decay.

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History continued:
Idea: Early spring 1956 (with Lee)
Decision to go ahead
March 1956

Two major difficulties
On the long road of planning:
May-September 1956

Genuine Asymmetry Effect:
December 1956
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History continued:
C. N. Yang:“The physicist was like a man in a darkroom groping for a way out. He is awareof the fact that in some direction there
must be a door which will lead him out ofhis predicament. But in which direction?”
After the parity violation observation:

J. R. Oppenheimer: “ Walked through door….”
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Other ‘Symmetry-odd’ parameter?
An example:

K →π µ ν+0PT =8/14/2019+σ µ ⋅ ( pπ × p µ )( pπ × p µ )T-odd5KEK PS-246 and J-PARC E36

All about systematics

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Another ‘famous’ example:
Electric Dipole Moment of any particle with spin

Classical Electrodynamics 3rd edition, chapter 11-11: BMT (Bargmann, Michel, Telegdi) Equation
How spin behave (rotate) in a classical electro-magnetic field

Experimentally, look for dXE or d.E (angle) effects
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Quantum number counting:Linear combinations of K and K forms K1 (CP even) andK2 (CP odd), also known as the short and long lived kaons.
00CP(π ) = -1, CP(2π) = +1, CP(3π)=-1, ….
KS → 2πKL →3πIntroduction to High Energy Physics, 4th edition, D. Perkins, chapter 7.15-7.16
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1964 Cronin, Fitch, Christenson, and Turley:

Note : size of the apparatus,
and the manpower
8/14/2019~50 K2 →2π events
Br ~ 2x10-3

9Rare Kaon Decays=Strong suppression from CKM, ~10-11 for
New Physics if deviate
Small theoretical uncertainties ~ 2%
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Small Theoretical Uncertainty:±• The hadronic matrix element issubstitute with known measurement.
• Uncertainty in the Standard Model
prediction almost entirely comes fromthe CKM parameters, the top and W
mass.

8/14/2019= (3.00 ± 0.30) × 10-11

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The experimental signature is very weak:Nothing comes in, two photons go out
Tiny branching ratio
Typical comments:
Impossible …
Moth flying into fire ….
Find something better to do …..
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The Moths

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In Real:

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Pipeline digitization of all detector pulses
CsI pulse (top)Filtered pulse (bottom)

Gaussian Filter circuit:
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KOTO DAQ System

Pipeline digitizers:(1) 125MHz 14-bit with Gaussian filter(~4000 channels)(2) 500MHz 12-bit no filter (~150 channels)• No delay cables - yeah
• Signals are 100 ohm differential (CAT6cables) to keep fidelity high
• VME64 is used for house keeping andtesting purposes (and inexpensive)• Data transfer between digitizers andtrigger controls done with optical fibers

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CsI Calorimeter Photon cluster trigger:
Small block: >22MeV
Large block: >44MeVTurns counting parallelalgorithm> 99% efficient

15 clusters

8/14/20193MeV: 11 clusters

22MeV: 5 clusters

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The Photon Cluster Processor

Counting total (+ and -) corners onCsI blocks
4 corners for a complete cluster
Number of clusters = Total number ofcorners/4

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The calculation took 20 clocks (160 nsec) to finish, with negligible deadtime21

Chicago Pipeline Electronics System

125MHz
ADC

500MHz
ADC

LVDS
I/O

Fiber Optics
I/O

edg.uchicago.edu/~bogdan
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KOTO DAQ livetime:

KOTO collects 35TB per day
1 Peta-Byte per monthand the computing related
problems we encountered….

Run 6XRun 6X3+ years ago

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Run74-78
2+ year ago

Run 79~1 year ago

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The Kaon Beam

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Blind Analysis:
1.Blind off signal region
2.Do we understand allfeatures outside thebox?
3. Open the box when
we do
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Blind Analysis ?

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First look at data: surprise to say the least

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Applications of Neural Net to reject background
Types of neural nets used so far:A. Root TMVA – SNN (‘high level’ variables inputs)
B. GPU Deep Learning – with/without convolution layers (‘low level’ pixel inputs)
C. GPU XGBoost (‘low level’ pixel inputs)
Run 6X ( A. Root TMVA SNN with ‘high level’ inputs- photon energy, angles, energy slopes, timing…)~1500 neutron reduction with
90% photon acceptance

NeutronPhoton

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With run 7X neutron data :No neutron related cuts

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With all other neutron cuts except neural net cut

After TMVA SNN

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B. GPU Deep Learning

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With Run 7X neutron data

With no neutron related cuts

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With Neural Net Deep Learning Convolution

With 2015 veto cuts33

Another background example:

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Before Neural Net DL:

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After Neural Net DL:

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Results of 2015 data (published late 2018)8/14/201936

Here we go, blind analysis opening the box:

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Box opened:

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BR < 3x10-9 (90% confidence level)38

Japan tsunami, March 2011
~1 year
J-PARC radiation accident, 2013~1.5 year
Within a month, ~ 4x10-10 result
The goal is to reach “StandardModel” within next few year.
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With so many veto detectors, wouldn’t the ‘accidentals’veto good events? The “Masking” effectFor example, one of the veto channels:

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Identify double pulses with as narrow timing window aspossible:

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Performance comparison:

Efficiency

90% of double pulses can be identified, with lessthan 0.1% mid-id.

Acceptance recovery!
~>30%!

Half width of time window (unit of clock count, 1 clk=8nsec)

8/14/2019Black squre: standard straight timing window
Blue triangle: standard straight timing window
with shape fit
Red star: FTT fit

Possible next step: Wavelet Transform(could be viewed as ‘time dependent
Fourier Transform’)42Thoughts: reduce background? increase sensitivity?
Usual: more beam power , smaller production angle
more run time
Unusual: use veto cylinder as detector to increase
acceptance ~X4 ,very much like collider
measure photon momentum to reconstruct pionmass,eliminate many backgrounds. But how?

New experiment: with sensitivity to collect 100 SM events
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Measure Re(Vtd)
BNL E787/E949
7 events
Br=(1.73+/-1.15)x10-10

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CERN NA62

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NA62 result on 2016 data (2% of total)
1 event
BR < 14x10-10 (95%CL)
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The overthrow of the Parity Law drives home once again the idea thatscience is not static but ever growing and dynamic. It involves not justthe addition of new information but the continuous revision of oldknowledge. It is the courage to doubt what has been long establishedand the incessant search for its verification and proof that pushes thewheel of science forward.
… share some of my excitement …. I often wonder, could it be that
excitement feeling in the scientific research has unfailingly kept us
scientists dedicating our lives to it contentedly.
C. S. Wu, April 1983, Nishina Memorial Foundation Publication No. 19
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Backups:
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Contribution of new physics
10-810-9

Direct limit
2.6x10-8(KEK E391a)

Indirect limit
1.5x10-9(Grossman-Nir)

10-10

10-11

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SM sensitivity
3.0x10-1151

Blind Analysis - III
A simple example: Data driven blind analysis for

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To understand hadroninteraction events
Scattered at the target
Core neutron
10 mm thick Al

Contribution byChonbuk Univ.
* Removed in physics run
Took Al-target data 70 hours> 15 times higher statics
than May 2013

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Reconstructed Z vertex(mm)

Hadron interaction

Gammas from π0Developing BG reduction with cluster shape54

New in-beam charged vetoMay 2013(24kW)

Huge gamma andneutron flux in beamApr 2015(24kW)
Hit rate

3-mm-thick plastic
scintillator
2.8-mm-thick gaswire chamber x3

10% signal loss

3.3MHz1.1MHz

1/3

30cm

3 layers of Thin Gap Chamber
Hit rate → 1/3.
Efficiency 99.6% / layer

8/14/201950um wires
Wire-spacing 1.8 mmWire-cathode gap 1.4mm
Gas : CF4+n-Pentane
Graphite coated cathodeon 50-um-thick Kapton
HV ~2.7kV(Mass :30mg/cm2 / layer)
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Activity from primary beam line
K1.8D1K1.1D1Q1B H18

Q1C

Work with beam line group

Primary beam side
Tuning of transportation magnet

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Before Tuning
May2013After Tuning

Defocused forthe dump

104
Module ID

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Rates from primary beam line
was reduced, but still remains

Want to add iron shield in Summer 201656

Time structure of Beam

BeamintensityWaveform

TimeTrigger from Target Monitor

Target Monitor trigger was issuedaccording to the instantaneous beam rate

Hit probability with Target Monitor trigger

Calculate hit probability

RF device in MR(Exciter) was On

Hit probability with Clock trigger
Exciter wastuned

Time structure of beam
seems to be improved
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1AprilJune

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Install Inner Barrel Photon Veto Winter2015
Add 5 X0 to 13.5 X0 of current Main Barrel−Suppress punch-through inefficiency by 1/50.

Gluing fiber to scintillator was mostly finished.
Module assembly will start soon.
Will install it in this winter+ Maintenance for existing broken channels in vacuum
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Application of Neural Net Deep Learning with no convoluton on CV-η backgroundη produced in material in front of CsI, 2015 data background: 0.03 +/- 0.02
Small but non-negligible

Note: Factor of ~6 reduction (Monte Carlo statistics limited)
Neural Net Deep Learning with Convolution ??
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KTeV Calorimeter → KOTO Calorimeter(Chicago)• New in KOTO: high rate, different energy range, vacuum,
higher timing requirement• CW base ->low power• Differential outputs to the125MHz waveform digitizer

14bit 125MHz FADC

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CsI calorimeter Total Energy Trigger:
Calculated with gain
corrected energies andsame 4-clocks time window
as the cluster counting,pipeline data with fiber
optics for the sum.

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2016-2018 DAQ Upgrades
LVL1 Veto based on individual channel energy (digital discriminator within timing window):
IB without veto
Online IB veto with energy sum

IB without veto
Online IB veto with individual channel• Factor of 3 reduction on physics trigger rates•8/14/2019

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