From af4047b6c4f030ee1ce8d8d99cb571a8fec430c7 Mon Sep 17 00:00:00 2001
From: Chad Hanna <chad.hanna@ligo.org>
Date: Mon, 29 Jul 2019 10:39:49 -0400
Subject: [PATCH] inspiral_lr.py: tighten default snr / chisq distribution for
 signals

---
 gstlal-inspiral/python/stats/inspiral_lr.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/gstlal-inspiral/python/stats/inspiral_lr.py b/gstlal-inspiral/python/stats/inspiral_lr.py
index 5b23760bf0..ce46e2aaeb 100644
--- a/gstlal-inspiral/python/stats/inspiral_lr.py
+++ b/gstlal-inspiral/python/stats/inspiral_lr.py
@@ -417,7 +417,7 @@ class LnSignalDensity(LnLRDensity):
 		vtdict = self.horizon_history.functional_integral_dict(window.shift(float(gps)), lambda D: D**3.)
 		return dict((instrument, (vt / t)**(1./3.)) for instrument, vt in vtdict.items())
 
-	def add_signal_model(self, prefactors_range = (0.01, 0.03), df = 100, inv_snr_pow = 4.):
+	def add_signal_model(self, prefactors_range = (0.001, 0.01), df = 400, inv_snr_pow = 4.):
 		# normalize to 10 *mi*llion signals.  this count makes the
 		# density estimation code choose a suitable kernel size
 		inspiral_extrinsics.NumeratorSNRCHIPDF.add_signal_model(self.densities["snr_chi"], 10000000., prefactors_range, df, inv_snr_pow = inv_snr_pow, snr_min = self.snr_min)
-- 
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