8 research outputs found
Resilient VAE: Unsupervised Anomaly Detection at the SLAC Linac Coherent Light Source
Significant advances in utilizing deep learning for anomaly detection have
been made in recent years. However, these methods largely assume the existence
of a normal training set (i.e., uncontaminated by anomalies) or even a
completely labeled training set. In many complex engineering systems, such as
particle accelerators, labels are sparse and expensive; in order to perform
anomaly detection in these cases, we must drop these assumptions and utilize a
completely unsupervised method. This paper introduces the Resilient Variational
Autoencoder (ResVAE), a deep generative model specifically designed for anomaly
detection. ResVAE exhibits resilience to anomalies present in the training data
and provides feature-level anomaly attribution. During the training process,
ResVAE learns the anomaly probability for each sample as well as each
individual feature, utilizing these probabilities to effectively disregard
anomalous examples in the training data. We apply our proposed method to detect
anomalies in the accelerator status at the SLAC Linac Coherent Light Source
(LCLS). By utilizing shot-to-shot data from the beam position monitoring
system, we demonstrate the exceptional capability of ResVAE in identifying
various types of anomalies that are visible in the accelerator
Multipoint-BAX: A New Approach for Efficiently Tuning Particle Accelerator Emittance via Virtual Objectives
Although beam emittance is critical for the performance of high-brightness
accelerators, optimization is often time limited as emittance calculations,
commonly done via quadrupole scans, are typically slow. Such calculations are a
type of , i.e. each query requires multiple
secondary measurements. Traditional black-box optimizers such as Bayesian
optimization are slow and inefficient when dealing with such objectives as they
must acquire the full series of measurements, but return only the emittance,
with each query. We propose applying Bayesian Algorithm Execution (BAX) to
instead query and model individual beam-size measurements. BAX avoids the slow
multi-point query on the accelerator by acquiring points through a
, i.e. calculating the emittance objective from a
fast learned model rather than directly from the accelerator. Here, we use BAX
to minimize emittance at the Linac Coherent Light Source (LCLS) and the
Facility for Advanced Accelerator Experimental Tests II (FACET-II). In
simulation, BAX is 20 faster and more robust to noise compared to
existing methods. In live LCLS and FACET-II tests, BAX performed the first
automated emittance tuning, matching the hand-tuned emittance at FACET-II and
achieving a 24% lower emittance at LCLS. Our method represents a conceptual
shift for optimizing multi-point queries, and we anticipate that it can be
readily adapted to similar problems in particle accelerators and other
scientific instruments
Validation of PEP-II Resonantly Excited Turn-by-Turn BPM Data ∗
For optics measurement and modeling of the PEP-II electron (HER) and position (LER) storage rings, we have been doing well with MIA [1] which requires analyzing turn-by-turn Beam Position Monitor (BPM) data that are resonantly excited at the horizontal, vertical, and longitudinal tunes. However, in anticipation that certain BPM buttons and even pins in the PEP-II IR region would be missing for the run starting in January 2007, we had been developing a data validation process to reduce the effect due to the reduced BPM data accuracy on PEP-II optics measurement and modeling. Besides the routine process for ranking BPM noise level through data correlation among BPMs with a singular-value decomposition (SVD), we could also check BPM data symplecticity by comparing the invariant ratios. Results from PEP-II measurement will be presented
Resilient VAE: Unsupervised Anomaly Detection at the SLAC Linac Coherent Light Source
Significant advances in utilizing deep learning for anomaly detection have been made in recent years. However, these methods largely assume the existence of a normal training set (i.e., uncontaminated by anomalies) or even a completely labeled training set. In many complex engineering systems, such as particle accelerators, labels are sparse and expensive; in order to perform anomaly detection in these cases, we must drop these assumptions and utilize a completely unsupervised method. This paper introduces the Resilient Variational Autoencoder (ResVAE), a deep generative model specifically designed for anomaly detection. ResVAE exhibits resilience to anomalies present in the training data and provides feature-level anomaly attribution. During the training process, ResVAE learns the anomaly probability for each sample as well as each individual feature, utilizing these probabilities to effectively disregard anomalous examples in the training data. We apply our proposed method to detect anomalies in the accelerator status at the SLAC Linac Coherent Light Source (LCLS). By utilizing shot-to-shot data from the beam position monitoring system, we demonstrate the exceptional capability of ResVAE in identifying various types of anomalies that are visible in the accelerator
Beam-based RF Station Fault Identification at the Linac Coherent Light Source
Accelerators produce too many signals for a small operations team to monitor
in real time. In addition, many of these signals are only interpretable by
subject matter experts with years of experience. As a result, changes in
accelerator performance can require time-intensive consultations with experts
to identify the underlying problem. Herein, we focus on a particular anomaly
detection task for radio-frequency (RF) stations at the Linac Coherent Light
Source (LCLS). The existing RF station diagnostics are bandwidth limited,
resulting in slow, unreliable signals. As a result, anomaly detection is
currently a manual process. We propose a beam-based method, identifying changes
in the accelerator status using shot-to-shot data from the beam position
monitoring system; by comparing the beam-based anomalies to data from RF
stations, we identify the source of the change. We find that our proposed
method can be fully automated while identifying more events with fewer false
positives than the RF station diagnostics alone. Our automated fault
identification system has been used to create a new data set for investigating
the interaction between the RF stations and accelerator performance
The fluctuation–dissipation measurement instrument at the Linac Coherent Light Source
The development of new modes at x-ray free electron lasers has inspired novel methods for studying fluctuations at different energies and timescales. For closely spaced x-ray pulses that can be varied on ultrafast time scales, we have constructed a pair of advanced instruments to conduct studies targeting quantum materials. We first describe a prototype instrument built to test the proof-of-principle of resonant magnetic scattering using ultrafast pulse pairs. This is followed by a description of a new endstation, the so-called fluctuation–dissipation measurement instrument, which was used to carry out studies with a fast area detector. In addition, we describe various types of diagnostics for single-shot contrast measurements, which can be used to normalize data on a pulse-by-pulse basis and calibrate pulse amplitude ratios, both of which are important for the study of fluctuations in materials. Furthermore, we present some new results using the instrument that demonstrates access to higher momentum resolution
On Ultrafast X-ray Methods for Magnetism
With the introduction of x-ray free electron laser sources around the world,
new scientific approaches for visualizing matter at fundamental length and
time-scales have become possible. As it relates to magnetism and
"magnetic-type" systems, advanced methods are being developed for studying
ultrafast magnetic responses on the time-scales at which they occur. We
describe three capabilities which have the potential to seed new directions in
this area and present original results from each: pump-probe x-ray scattering
with low energy excitation, x-ray photon fluctuation spectroscopy, and
ultrafast diffuse x-ray scattering. By combining these experimental techniques
with advanced modeling together with machine learning, we describe how the
combination of these domains allows for a new understanding in the field of
magnetism. Finally, we give an outlook for future areas of investigation and
the newly developed instruments which will take us there