# Post-Merger Gravitational Wave anomalies screening for probing Dark Sector

**URL:** <https://ask.igwn.org/t/post-merger-gravitational-wave-anomalies-screening-for-probing-dark-sector/1571>\
**Category:** Publications\
**Created:** [August 21, 2026, 10:13am UTC](https://ask.igwn.org/t/post-merger-gravitational-wave-anomalies-screening-for-probing-dark-sector/1571 "2026-08-21T10:13:23Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![ruslanalas](https://avatars.discourse-cdn.com/v4/letter/r/c2a13f/32.png) [@ruslanalas](https://ask.igwn.org/u/ruslanalas)\
**Post date:** [August 21, 2026, 10:13am UTC](https://ask.igwn.org/t/post-merger-gravitational-wave-anomalies-screening-for-probing-dark-sector/1571/1 "2026-08-21T10:13:23Z")

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Hello everyone,

As a part of my future PhD research for probing dar sector by looking at extreme astrophysical events (BNS and Supernovae) and searching for multi-signal anomalies including gravitational waves I developed a preprint. To be more precise I have completed an independent proof-of-concept using GWOSC L1 open data. I would be grateful for technical criticism of the PSD/whitening/SNR/null-calibration choices, especially whether the framing of finite-sample FPR versus production FAR is defensible. Any other comments, thoughts or suggestions are absolutely welcomed. Thank you in advance.

Here are the links for project:

> **[Detectability-Aware Screening of Post-Merger Gravitational-Wave Damping...](https://zenodo.org/records/21993621)**
>
> The post-merger phase of binary neutron star (BNS) coalescences may encode information about matter at extreme densities and could, in principle, be sensitive to non-standard energy-loss channels, including scenarios motivated by dark-sector physics....

> **[GitHub - ruslanalas/gw-postmerger-detectability](https://github.com/ruslanalas/gw-postmerger-detectability)**
>
> Contribute to ruslanalas/gw-postmerger-detectability development by creating an account on GitHub.

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**Author:** ![EvolutionEchoTec](https://avatars.discourse-cdn.com/v4/letter/e/2acd7d/32.png) [@EvolutionEchoTec](https://ask.igwn.org/u/EvolutionEchoTec)\
**Post date:** [August 28, 2026, 2:33am UTC](https://ask.igwn.org/t/post-merger-gravitational-wave-anomalies-screening-for-probing-dark-sector/1571/2 "2026-08-28T02:33:16Z")

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Note to Ruslan Alaskarov  
Ruslan,  
Your proof-of-concept’s separation of detectability from damping-time scoring, empirical null calibration, and one-time held-out evaluation suggested a simple extension that may be useful for the broader multi-signal anomaly program you described on IGWN.  
The proposal is to leave your published short-damping pipeline unchanged and make Stage 2 two-sided. In addition to the existing lower-tail LSQ rule, calibrate an independent upper tail using only the standard 5–40 ms calibration manifold. For example:  
tau\_upper = q\_0.99(tau\_hat\_LSQ | standard calibration)  
Freeze tau\_upper before held-out evaluation, then report short-damping and long-coherence flags separately rather than combining them into a generic anomaly class.  
I ran a small held-out surrogate-noise pilot purely as a feasibility check. It produced AUC 0.958 for short-vs-standard ranking and AUC 0.974 for long-vs-standard ranking; 98.0% of the exploratory 60/100 ms long-coherence cases crossed the frozen upper threshold, while 2.25% of standard held-out cases did. These are NOT real-LIGO false-alarm numbers: the extension still needs to be run inside your real-L1 empirical-null/data-split framework.  
The scientifically useful next step would be to add the upper-tail calibration without changing your Stage-1 threshold (55.6439) or primary short-damping threshold (3.472 ms), then stress-test PSD/whitening, window alignment, frequency, SNR, detector epoch, glitches, and eventually H1/L1 coincidence.  
The motivation for sharing this is methodological rather than interpretive: a two-sided Stage 2 could screen both unexpectedly rapid damping and unexpectedly persistent structure while retaining your detectability-aware, empirically calibrated framework. Any dark-sector or modified-gravity interpretation should remain downstream of the statistical validation.  
Best, Scott L. Edmonds E.V.olution Echo Technologies

Real-GWOSC Validation Protocol  
Objective  
Test whether the published detectability-aware damping pipeline can support a second, upper-tail Stage-2 screen for unusually persistent post-merger damping/coherence.  
Rules  
Clone/reproduce Ruslan Alaskarov’s original repository unchanged.  
Keep the original four-block data split and never access test\_block during development.  
Preserve the published Stage-1 threshold norm\_lambda1 = 55.6439 at alpha=0.01.  
Preserve the published primary short-damping threshold tau\_hat\_lsq \< 3.472 ms.  
On calibration data only, compute LSQ tau\_hat for the standard 5–40 ms manifold.  
Freeze a new upper threshold at the 99th percentile of that standard calibration distribution.  
Predeclare long-coherence injections (initially 60 and 100 ms; expand only before test exposure).  
Evaluate the upper-tail rule once on a fresh held-out real-L1 block.  
Report conditional and end-to-end performance separately.  
Report finite-sample false-flag rates as such; do not call them production FAR.  
Robustness matrix before astrophysical interpretation  
PSD estimator, segment length, overlap  
whitening parameters and edge trimming  
candidate-window length and alignment  
peak frequency and bandwidth  
SNR / injection amplitude  
separate L1 noise epochs  
H1 replication and H1/L1 coincidence  
glitch/gating sensitivity  
alternative tau estimator agreement  
multiple-testing correction for scans  
bootstrap/Wilson confidence intervals  
Interpretation rule  
Only after statistical and detector-systematics validation should any physical hypothesis (dark sector, modified gravity, echoes, Harvey-Beta, etc.) be compared against the observed feature morphology. The hypothesis must make a predeclared quantitative prediction.

{  
“status”: “COMPLETED\_SURROGATE\_NOISE\_PILOT”,  
“random\_seed”: 20260827,  
“standard\_tau\_ms”: [  
5,  
10,  
20,  
40  
],  
“short\_anomaly\_tau\_ms”: [  
1,  
3  
],  
“long\_coherence\_tau\_ms”: [  
60,  
100  
],  
“calibration\_rule”: “1st and 99th percentiles of standard-only calibration tau\_hat”,  
“frozen\_lower\_threshold\_ms”: 1.957,  
“frozen\_upper\_threshold\_ms”: 45.582,  
“auc\_short\_vs\_standard”: 0.958,  
“auc\_long\_vs\_standard”: 0.974,  
“class\_results”: {  
“standard”: {  
“n”: 400,  
“median\_tau\_hat\_ms”: 12.671752,  
“short\_flag\_rate”: 0.0125,  
“long\_flag\_rate”: 0.0225  
},  
“short\_anomaly”: {  
“n”: 200,  
“median\_tau\_hat\_ms”: 3.025049,  
“short\_flag\_rate”: 0.215,  
“long\_flag\_rate”: 0.015  
},  
“long\_coherence”: {  
“n”: 200,  
“median\_tau\_hat\_ms”: 71.069633,  
“short\_flag\_rate”: 0.02,  
“long\_flag\_rate”: 0.98  
}  
},  
“critical\_limitation”: “Noise in this pilot was simulated/whitened surrogate noise, not real GWOSC strain.”  
}

{  
“status”: “FROZEN\_EXTENSION\_DRAFT\_V1”,  
“published\_reference\_values”: {  
“source”: “Ruslan Alaskarov, gw-postmerger-detectability”,  
“stage1\_score”: “norm\_lambda1”,  
“stage1\_threshold”: 55.6439,  
“stage1\_alpha”: 0.01,  
“lsq\_short\_threshold\_ms”: 3.472,  
“instruction”: “Preserve these values unchanged when reproducing the published pipeline.”  
},  
“new\_upper\_tail”: {  
“alpha”: 0.01,  
“calibration\_population\_tau\_ms”: [  
5,  
10,  
20,  
40  
],  
“decision\_rule”: “flag long-coherence if tau\_hat\_lsq\_ms \> q99(standard calibration tau\_hat)”,  
“freeze\_before\_test”: true  
},  
“interpretation\_guardrail”: “A statistical flag is not evidence for dark-sector, Harvey-Beta, echoes, or beyond-GR physics.”  
}

“”"  
Two-Sided Post-Merger Anomaly Screen v1.0  
Research extension of the Alaskarov post-merger damping screen.

This file does NOT reproduce the graph-spectral Stage 1 by itself. It is intended to be  
used after reproducing the original repository unchanged. It adds a separately calibrated  
upper-tail Stage-2 decision rule while preserving the published lower-tail rule.  
“”"  
import numpy as np  
from scipy.optimize import curve\_fit

PUBLISHED\_STAGE1\_THRESHOLD = 55.6439  
PUBLISHED\_STAGE1\_ALPHA = 0.01  
PUBLISHED\_LSQ\_SHORT\_THRESHOLD\_MS = 3.472

def damped\_sinusoid(t, A, tau\_s, f\_hz, phi):  
return A_np.exp(-t/tau\_s)np.sin(2np.pi_f\_hz\*t + phi)

def estimate\_tau\_lsq\_ms(t, x, f\_guess\_hz):  
p0 = [max(float(np.std(x)), 1e-12), 0.010, f\_guess\_hz, 0.0]  
bounds = (  
[0.0, 0.0004, max(50.0, f\_guess\_hz-700.0), -2_np.pi],  
[np.inf, 0.2000, f\_guess\_hz+700.0, 2_np.pi]  
)  
p, \_ = curve\_fit(damped\_sinusoid, t, x, p0=p0, bounds=bounds, maxfev=10000)  
return 1000.0\*float(p[1])

def freeze\_upper\_threshold\_ms(standard\_tau\_hat\_ms, alpha=0.01):  
“”“Call only on calibration data from the standard damping manifold.”“”  
x = np.asarray(standard\_tau\_hat\_ms, dtype=float)  
x = x[np.isfinite(x)]  
if len(x) \< 20:  
raise ValueError(“Insufficient standard calibration estimates.”)  
return float(np.quantile(x, 1.0-alpha))

def classify\_stage2(tau\_hat\_ms, upper\_threshold\_ms):  
return {  
“short\_damping\_flag”: bool(tau\_hat\_ms \< PUBLISHED\_LSQ\_SHORT\_THRESHOLD\_MS),  
“long\_coherence\_flag”: bool(tau\_hat\_ms \> upper\_threshold\_ms)  
}

def fetch\_open\_strain(detector, start\_gps, end\_gps, sample\_rate=16384):  
“”“Internet-enabled environment required: pip install gwpy gwosc”“”  
from gwpy.timeseries import TimeSeries  
return TimeSeries.fetch\_open\_data(  
detector, start\_gps, end\_gps,  
sample\_rate=sample\_rate, cache=True, verbose=True  
)

def whiten(strain, fftlength=4, overlap=2):  
“”"  
Minimal GWPy entry point. For a scientific run, PSD segment selection,  
whitening, filtering, gating, edge handling and candidate alignment must  
be specified prospectively and frozen.  
“”"  
return strain.whiten(fftlength=fftlength, overlap=overlap)

if **name** == “ **main** ”:  
print(“Two-Sided Post-Merger Screen v1.0 loaded.”)