Intro

Setup

The script bootstrap.sh should get you up and running

chmod u+x bootstrap.sh
/bootstrap.sh

Structure

• do_global_corrections.py will run a full set of simulations go into the file and check it out
• pairs.py, q1_errors.py q2_errors.py and magnet_errors.py define helpful classes for the setup of magnet errors
• analysis_pairing.py does the statistics on many simulations
• job*.madx are various job files to create the madx lattice and produce model and meas output
• the folder macros contains our omc3 macros for lattice and model creation
• some folders are created by bootstrap.sh

Quick links

• History gives an overview over studies already done
• FiDeL homepage
• Magnet sorting Decision
• MADX Lattice info
• Magnet sorting studies for LHC

Current progress

• defined steps to take with Massimo
• Error distribution defines how the errors are created
• Run many simulations, correct with full response and compare how correctability and bbeat improves

Many simulations result

• some correlation visible for high bbeat
• low bbeat at Q2a + Q2b less significant

Global corrections sims

• shows good improvement for bbeat by sorting and pairing
• shows also good improvement for corr
• both seem correlated (luckily, now we can fix both in one go)

run simulations with 'idealised' corrections:

• i.e. calc corrections by hand / using mad-x
• plug I into transfer functions
• repeat tracking and measure betabeat

History

• Jaime: study on correctability, long time ago. showed that a tiny deviation from optimal could already be a problem.
• Global correction: not good for triplet (global factor?)
• Hectors work on magnet sorting: first talk, second talk (with ML), third talk (more ML),

Possible Tasks

• create a global corrections script, taking a global factor into account
• corrections from twiss
• add parameter to FR? YES
• check which correction method is the best (SbS, global+factor, APJ?, ML?)
• decide if sorting is needed after all

Parameters

• measured magnet strengths
• aperture?
• beta beating
• required correction strengths (pc limits?, K limits?)

Immediate tasks:

• check corrections.py - can we get corrections from twiss? A: YES
• start some simulations, get overview of beta beating A: 6-10%

result from meeting with massimo

Constraints

• probably only Q2 pairing possible,
• possibly Q1, Q2, Q3 sorting between different IPs same side

Notation

Pairing: A - B Sorting: Q1IP1 - Q1IP5

Merit

• sorting: could improve beta beating

• pairing: could improve correctability

  Aperture / tolerances: try simulating sorting for aperture

Work Details

Error distribution

The error distributions are created and managed by subclasses of Pairs.

Example:

q1_errors = Q1Pairs(real_error=10, meas_error=2, stage=1)
q1_errors.write_errors_to_file("model/errors_Q1.madx")

This creates a set of error distributions q1_errors and writes the errors of the initial distribution to a file model/errors_Q1.madx.

Now we can perform sorting:

q1_errors.sort_sum()
q1_errors.write_errors_to_file("model/errors_Q1_sorted.madx")

Or define our own sorting function:

def sort_fn(pairs):
    return np.sum([
        (errors.get_pair(i)[0].real_error - errors.get_pair(i)[1].real_error)**2
        for i in range(errors.get_pair_count())])

q1_errors.sort(sort_fn)
q1_errors.write_errors_to_file("model/errors_Q1_sorted.madx")

MADX lattice

root: acc-models-lhc

Triplet

Q1ab, Q2a, Q2b, Q3ab

defined in acc-models-lhc/hllhc_sequence.madx:420

Powering

task: please check if Q2a/b might be powered individually