eeFACT 2025 - 70th ICFA Advanced Beam Dynamics Workshop on High Luminosity Circular e+e– Colliders

Session

Machine learning and automatic tuning

WG13

6 Mar 2025, 16:10

300 (EPOCHAL)

199. Luminosity tuning with ML in FCCee (THB11)

Vaibhavi Gawas (European Organization for Nuclear Research)

06/03/2025, 16:10

Invited Oral Presentation

95. Using RL to maximize luminosity at BEPCII (THB12)

Jiaqi Fan (Chinese Academy of Sciences)

06/03/2025, 16:40

WG13 : Machine learning and automatic tuning

Invited Oral Presentation

BEPCII is a double-ring collider operates in the decay mode, as beam currents decrease over time, the beam orbits need to be continuously adjusted to maintain the optimum collision conditions. This job was primarily performed manually, with operators scan three offset knobs (x, y, y') based on the luminosity. There is a critical need to implement advanced automated control method. However, the...

96. AI for FCCee - what will it bring and what needs to be prepared (THB13)

Verena Kain (European Organization for Nuclear Research)

06/03/2025, 17:10

WG13 : Machine learning and automatic tuning

Invited Oral Presentation

The Future Circular Collider (FCC) study at CERN is developing designs for the next generation particle colliders to follow on from the Large Hadron Collider after its High-Luminosity phase. A new tunnel of about 90 km circumference would Initially house an electron-positron collider, the FCC-ee, with a research programme of 15 years followed by a hadron collider, the FCC-hh, with a programme...

202. Advanced ML at APS (FRB01)

Nikita Kuklev (Fermi National Accelerator Laboratory)

07/03/2025, 09:00

Invited Oral Presentation

99. FCCee Interaction Point optics tuning (FRB02)

Satya Sai Jagabathuni (European Organization for Nuclear Research)

07/03/2025, 09:30

WG13 : Machine learning and automatic tuning

Invited Oral Presentation

The FCC-ee collider requires strong focusing and small beam sizes at the interaction point (IP) to achieve its unprecedented luminosity. Magnet misalignments and gradient errors will perturb the optics at the IP, leading to beam size growth, and making it difficult to reach the collider’s luminosity goals. Therefore, tuning tools are essential for correcting these aberrations during operation....

204. DAMA design optimization for light sources and implications for FCCee (FRB03)

Daniel Ratner (SLAC National Accelerator Laboratory)

07/03/2025, 10:00

Invited Oral Presentation

101. Machine learning experience at SuperKEKB (FRB04)

Gaku Mitsuka (High Energy Accelerator Research Organization)

07/03/2025, 10:50

WG13 : Machine learning and automatic tuning

Invited Oral Presentation

The SuperKEKB accelerator has been studying accelerator operations using machine learning since 2022. Machine learning has been introduced in Linac to control the orbit of electron and positron beams to achieve highly efficient generation and transport of electron and positron beams downstream of Linac. Similarly, machine learning has been applied to orbit control to suppress emittance...

206. ML techniques for polarization improvements in RHIC and EIC (FRB05)

Eiad Hamwi (Cornell University)

07/03/2025, 11:20

Invited Oral Presentation

231. Status and limitations of SuperKEKB optics tuning (FRB06)

Yukiyoshi Ohnishi (High Energy Accelerator Research Organization)

07/03/2025, 11:50

WG13 : Machine learning and automatic tuning

Invited Oral Presentation

The nano-beam scheme allows the vertical beta function at the interaction point (IP) to be much smaller than the bunch length. The vertical beta function and the beam size at the collision point realized at SuperKEKB are the smallest in the world among colliders. As the result, the luminosity can be achieved higher than conventional colliders utilize a head-on collision or a smaller...