KOTO's computing experience on the OpenScience poolChieh Lin (Jay)chiehlin@uchicago.ed
University of Chicagou 2023 Jul 12

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KOTO: search for the very rare decay KL → π ννObserved Universe: MATTER >> ANTI-MATTER

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Target: Search for the KL → π νν decay→ Only happens once every 33 billion kaon
decays in the theory.
→ A golden mode that may further hint how
the nature creates the matter-dominatedUniverse.

2Why OSG?

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KL → π νν SIGNAL= 2γ + nothing else.

KOTO simulates copious events that canmimic signals using Monte Carlo method.

• Quantitatively evaluate the threat of eachpossible source• The accuracy relies on the number ofsimulated events• OSG is a perfect platform for KOTO toperform the Monte Carlo simulation.

γ300 0KL → π π BACKGROUND= Do not detect 2γγ1γ200 0
KL → π π.

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γ43History of OSG at KOTO
Initiated the OSG project

Deployed the stable version

Simulated the first KOTO event
using OSG machines.

Confirmed the results produced from
OSG were expected.

2022/082022/05

2023/02
Built the KOTO libraryStarted installing the full KOTO simulation
library at OSG.

2023/03

Mass production
Kept job submissions for KOTO.

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Strategy to overcome the KOTO database problem✓ KOTO implements a huge database (external les 20TB) to perform the simulation✓ Introduce “drawers” in database; only select the les to be used.

Database selector

Process a random
procedure to select the“drawer” in database.

Download external lesSimulate KOTO events

Running time
Minimize the external le size and maximize #events
to reduce the overhead time.

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Strategy to overcome the disk space problem✓ KOTO’s standard output le is huge (20 GB)✓ Hope to keep the overhead time small✓ Produce 10% of events rst and only keep events in interest✓ Repeat this procedure 10 iterations.
Simulate KOTO events
Generate 10% events

Post-selection

Merge all output lesRunning time

10 iterations.

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Workflow for OSG production
Required time ~ 12 hr

OSG pool

Database selector

A random process is involved
to select a “drawer” in theKOTO database.

Download database les(Stash Cache)

Only download the les that
this job needs to minimize
the overhead time.

OSG access node

When production
is completed.

OSG disk node

Speed = ~10 MB/s

Simulate events

Final MC@KEKCCKEKCC is where the KOTO real
data is stored.

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Production rate comparison
Use the output le’s epoch (unix time) to evaluate the simulation production time.
Run out the disk space.

OSG (with iterations)
OSG (no iteration)- Average production rate (one account
9KEKCC=3x10/ 10 hour
- OSG = 12 x 109 / 10 hour (no iteration).

KEKCC(where KOTO performs analysis)= 24 x 109 / 10 hour (with iterations)[ Speed x 8 ].

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Achievements
OSG contributes to the new data set.

• Produce more training samples for theneural network to enhance its
performance• Further estimate the probability forsome of the background mechanismsthat were hard to study before.

Cumulative protons on target

Latest publication(2016-2018 data)

In progress(2019-2021 data).

9KOTO future with OSG
More data is coming in the future
OSG is needed to reach the sensitivity.

More physics topics may be studied at KOTO
with the OSG resources.

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10Summary• KOTO introduced the Monte Carlo simulation using OSG since 2022 and began thecontinuous job submission since March 2023

• OSG has a major contribution to KOTO’s latest data set and will play a crucial role inthe future.

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Content of a KOTO docker image

https://hub.docker.com/r/chiehlin0212/koto-dev

13Preliminary KOTO docker image construction
Library construction command lines

Build a KOTO docker image supporting• ROOT (v6.22.02• GEANT4 (v10.05.01• e14lib (201605v6.2)
Translate into docker image
Docker images

Local workspace

Image size

Update the working image toOSG repository ))Remote repository: can be seen by grid computers

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Run a job to OSG pool
A job submission requires a con guration script.
A script that generates 1000
KL0 → 3π 0 events.

Build KOTO container

The00rst KL → 3π MCle.

Transfer GEANT4 setting le.

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