The Linac Coherent Light Source changes configurations multiple times per day, necessitating fast tuning strategies to reduce setup time for successive experiments. To this end, we employ a Bayesian approach to transport optics tuning to optimize groups of quadrupole magnets. We use a Gaussian process to provide a probabilistic model of the machine response with respect to control parameters from a modest number of samples. Subsequent samples are selected during optimization using a statistical test combining the model prediction and uncertainty. The model parameters are fit from archived scans, and correlations between devices are added from a simple beam transport model. The result is a sample-efficient optimization routine, which we show significantly outperforms existing optimizers.

Acceleration of electrons using laser-driven dielectric microstructures is a promising technology for the miniaturization of particle accelerators. Achieving the desired GV m^-1 accelerating gradients is possible only with laser pulse durations shorter than ∼1  ps. In this Letter, we present, to the best of our knowledge, the first demonstration of acceleration of relativistic electrons at a dielectric microstructure driven by femtosecond duration laser pulses. Using this technique, an electron accelerating gradient of 690±100  MV m^-1 was measured-a record for dielectric laser accelerators.

Advances in ultrafast laser technology and nanofabrication have enabled a new class of particle accelerator based upon miniaturized laser-driven photonic structures. However, developing a useful accelerator based on this approach requires control of the particle dynamics at field intensities approaching the damage limit. We measure acceleration in a fused silica dielectric laser accelerator driven by fields of up to 9 GV m−1 and observe a record 1.8 GV m−1 in the accelerating mode. At these intensities the dielectric is driven beyond its linear response and self-phase modulation changes the phase velocity of the accelerating mode, reducing the average gradient to 850 MeV m−1. We show that free-space optics can be used to compensate this dephasing and demonstrate that tailoring the laser phase and amplitude can facilitate optimization of the beam dynamics. This could enable MeV scale energy gain in a single stage and pave the way towards applications in scientific, industrial, and medical fields.

The original "Seven Motifs" set forth a roadmap of essential methods for the field of scientific computing, where a motif is an algorithmic method that captures a pattern of computation and data movement. 1 We present the Nine Motifs of Simulation Intelligence, a roadmap for the development and integration of the essential algorithms necessary for a merger of scientific computing, scientific simulation, and artificial intelligence. We call this merger simulation intelligence (SI), for short. We argue the motifs of simulation intelligence are interconnected and interdependent, much like the components within the layers of an operating system. Using this metaphor, we explore the nature of each layer of the simulation intelligence "operating system" stack (SI-stack) and the motifs therein:1. Multi-physics and multi-scale modeling 2. Surrogate modeling and emulation 3. Simulation-based inference 4. Causal modeling and inference 5. Agent-based modeling 6. Probabilistic programming 7. Differentiable programming 8. Open-ended optimization Machine programmingWe believe coordinated efforts between motifs offers immense opportunity to accelerate scientific discovery, from solving inverse problems in synthetic biology and climate science, to directing nuclear energy experiments and predicting emergent behavior in socioeconomic settings. We elaborate on each layer of the SI-stack, detailing the state-of-art methods, presenting examples to highlight challenges and opportunities, and advocating for specific ways to advance the motifs and the synergies from their combinations. Advancing and integrating these technologies can enable a robust and efficient hypothesis-simulation-analysis type of scientific method, which we introduce with several use-cases for human-machine teaming and automated science.