Followed instructions in 74681. Last done in 78782, 79988. Saved in /ligo/gitcommon/NoiseBudget/aligoNB/aligoNB/H1/couplings/ and pushed to git.
CHARD_P, CHARD_Y, MICH, PRCL, SRCL screenshots attached. The IFO had been in NLN for 4h15 when these were taken. The AS_A_YAW and PRCL offsets were turned off.
I modified the script used in 78969 to project PRCL noise to DARM through MICH and SRCL, and now added coupling through CHARD P + Y.
There is high coherence between PRCL and CHARD, but these active injections that Camilla did show that the main coupling of PRCL to DARM is not through CHARD.
This script is now in /ligo/gitcommon/NoiseBudget/simplepyNB/PRCL_excitation_projections.py (not actually a git repo)
Sheila, Vicky - we ran the noise budget using these updated couplings from Camilla.
Noise budget with squeezing - pdf and svg (and the quantum sub-budget with squeezing). Plots without unsqueezed DARM here: pdf and svg. Picked a time with high range, and good ~4.8dB squeezing.
Noise budget without squeezing - pdf and svg (and the quantum sub-budget without squeezing).
The remaining sub-budgets are for squeezed DARM: Laser, Jitter, LSC, ASC, Thermal, PUMDAC.
Noise below 25 Hz looks pretty well accounted for: 10-15 Hz ~ ASC (CHARD Y, then CSOFT P, CHARD P). 15-20 Hz ~ LSC (PRCL).
Some comments and caveats for these budget plots:
- OM2 is cold.
- Quantum noise parameters could be updated, pending a new SQZ data set. These budgets use parameters that matched the SQZ data set in May 8 2024 (lho77710). Since then, the SRCL detuning was zero'd on Sept 5 (see lho79929), then un-zero'd from commissioning changes. Based on the sensing function, the SRCL detuning now looks similar to May, so just used May parameters. To-do: re-zero the SRCL detuning, check filter cavity detuning, (get back to 5dB sqz), take another SQZ data set.
The code has been pushed to aligoNB repo, commit 95d3a88b. To make these noise budget plots:
- conda activate aligoNB
python /ligo/gitcommon/NoiseBudget/aligoNB/production_code/H1/lho_darm_noisebudget.py
Thanks to Erik von Reis for helping us update the aligoNB environment to newer python and scipy (etc) versions. Next to-do: have the budget code use median averaging to be more robust to glitches (which can be done now that the environment has an updated scipy!).
