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Tutorial: Basic Detector Optimization

This tutorial shows how to use the Scheduler library for optimizing detector parameters. We'll create a simple objective function that evaluates detector performance based on field strength, detector length, and detector radius.

Prerequisites

  • Scheduler library installed (see Installation)
  • Basic understanding of Bayesian optimization with Ax
  • eic-shell container and a own version of epic detector geometry

Step 1: Import Required Libraries

import numpy as np
from ax.service.ax_client import AxClient
from scheduler import AxScheduler, JobLibRunner

Step 2: Define Your Objective Function

Write the holistic example here

Next Steps

  • Try modifying the objective function to include more realistic detector physics
  • Experiment with different parameter ranges and constraints
  • Check out the Slurm Execution Tutorial to scale up your optimization