Contains the files corresponding to the LCLS Cu injector NN surrogate model and example notebooks illustrating how to load and use the model. Although not required, using LUME-model is recommended.
The model was trained by Auralee to predict beam properties at OTR2 using injector PVs for LCLS. As the model was trained with normalized data, input and output transformations have to be applied to use it on simulation data. Another layer of transformations is required for using it with EPICS data. See provided examples for more information.
conda env create -f environment.yml- Load and print model information
- Load as torch model
- Load as LUME-model
- Load as LUME-model for use with EPICS data
The default value for QE01:b1_gradient in the simulation variable specification has been noticed to lie outside the given value range (likewise for QUAD:IN20:425:BACT in the PV variable specification). Thus, a new value was determined by minimizing the model prediction of the transverse beam size within the valid range (documented in this notebook).
The surrogate model was trained on predictions for OTR2. However, the OTR2 screen has been broken for a number of months and is currently unavailable for measurements. OTR3 can be used instead as in theory, there shouldn't be too much difference between the two. There are a number of quadrupoles between the two but generally these aren't changed and should stay relatively consistent. The PV for OTR3 replaces 571 with 621 to become OTRS:IN20:621:XRMS etc.
We don't yet have a confirmed time for when OTR2 will be back in action.
Some of the input features used as features of the model are not available in EPICS. These include:
This is the pulse length within the simulation. There has been some discussion about creating a PV to record the pulse length but for now, a reference value of 1.8550514181818183 (PV units) or 3.06083484 (sim units) is used by default.
As demonstrated by the train input min and max values in model.json, this value was treated as a constant when training the surrogate model. However in reality, it's PV value ACCL:IN20:400:L0B_ADES shows a distribution of values. If it was to be used in the model for predictions, the error would increase dramatically and therefore any measured values from EPICS are overwritten by the value seen during training, scaled to PV units.
As above, the charge value was constant in the training dataset but it's PV value FBCK:BCI0:1:CHRG_S shows a distribution of values. Measured values from EPICS are overwritten by the value seen during training, scaled to PV units.
The value for the beam size (r_dist) is not measured directly in EPICS but we do measure the XRMS and YRMS value of the beam. We use these PVs (CAMR:IN20:186:XRMS and CAMR:IN20:186:YRMS) to calculate a value for the beam size using the formula:
r_dist = np.sqrt(data["CAMR:IN20:186:XRMS"].values ** 2 + data["CAMR:IN20:186:YRMS"].values ** 2)We call this computed PV CAMR:IN20:186:R_DIST. Therefore, when pulling data from the archive, this step needs to be completed in any data processing.