Hi PyCBC team,
PyCBC supports custom waveform approximants via the pycbc.waveform module. I'm
exploring whether an algebraic waveform model (no NR calibration) could be registered
as an approximant for comparison studies.
The algebraic model:
import numpy as np
from scipy.signal import chirp
def qgd_waveform(M1, M2, distance, f_low=20, sample_rate=4096):
"""
QGD algebraic gravitational waveform.
No NR calibration — derived from first principles:
h(t) = h_inspiral + h_merger + h_ringdown
Merger condition: d = 4*r_Schwarzschild (from Sigma=1 horizon condition)
Ringdown freq: f_220 = 0.747*c³/(2π*G*Mf)
"""
G, c, M_sun = 6.674e-11, 3e8, 1.989e30
M1_kg, M2_kg = M1*M_sun, M2*M_sun
Mf = (M1 + M2) * 0.95 * M_sun # ~5% radiated
# Peak frequency from algebraic QNM
f_peak = 0.747 * c**3 / (2*np.pi * G * Mf)
tau = G * Mf / (0.178 * c**3)
t = np.arange(0, 0.5, 1/sample_rate)
t_merger = 0.35 # seconds
dt = t - t_merger
# Ringdown phase
h = np.zeros_like(t)
post = dt > 0
h[post] = np.exp(-dt[post]/tau) * np.cos(2*np.pi*f_peak*dt[post])
# Inspiral phase (Peters formula)
pre = dt < 0
Mc = (M1_kg * M2_kg)**0.6 / (M1_kg + M2_kg)**0.2
f_inst = f_peak * (1 + (-dt[pre])**(3/8) * 0.1)
h[pre] = np.sin(2*np.pi*np.cumsum(f_inst)/sample_rate)
A = (G*Mf/c**2) / (distance * 3.086e22) # amplitude
return t, h * A
# GW150914
t, h = qgd_waveform(36.2, 29.1, 410) # masses in M_sun, distance in Mpc
Validation result: Peak frequency ~250 Hz, ringdown τ ~3.6 ms — matches GW150914
LIGO observation without NR template fitting.
Proposal: Would PyCBC consider accepting a PR adding qgd_inspiral_ringdown as
a registered approximant? This would allow formal Bayes factor comparison vs SEOBNRv4,
IMRPhenomD etc. using real LIGO data.
Preprint: https://doi.org/10.5281/zenodo.18605058
Full validation code: https://github.com/matshaba/Quantum-Gravity-Dynamics
Quick References:
https://github.com/matshaba/Quantum-Gravity-Dynamics/blob/main/docs/summary.md
https://github.com/matshaba/Quantum-Gravity-Dynamics/blob/main/core/LIGO_ringdown.py
Hi PyCBC team,
PyCBC supports custom waveform approximants via the
pycbc.waveformmodule. I'mexploring whether an algebraic waveform model (no NR calibration) could be registered
as an approximant for comparison studies.
The algebraic model:
Validation result: Peak frequency ~250 Hz, ringdown τ ~3.6 ms — matches GW150914
LIGO observation without NR template fitting.
Proposal: Would PyCBC consider accepting a PR adding
qgd_inspiral_ringdownasa registered approximant? This would allow formal Bayes factor comparison vs SEOBNRv4,
IMRPhenomD etc. using real LIGO data.
Preprint: https://doi.org/10.5281/zenodo.18605058
Full validation code: https://github.com/matshaba/Quantum-Gravity-Dynamics
Quick References:
https://github.com/matshaba/Quantum-Gravity-Dynamics/blob/main/docs/summary.md
https://github.com/matshaba/Quantum-Gravity-Dynamics/blob/main/core/LIGO_ringdown.py