research28 min studyLesson 920 in Physics

A coincidence becomes evidence through a specified search

Distinguish measured arrival delays, false-alarm probabilities and posterior source odds.

On this page

What you’ll understand

  • Separate arrival-time data from intrinsic source and propagation delays.
  • Calculate a local Poisson chance probability and a specified trials correction.
  • Distinguish false-alarm probability from Bayesian association odds and physical source parameters.

Two messengers close in time and direction can provide compelling evidence for a shared event, but the strength of that evidence depends on how the search was defined. Different detectors measure different quantities and apply different selection cuts. A careful inference starts with the recorded events and their response models, then asks which source hypotheses predict them. It does not begin by treating a preferred emission model as an additional measurement.

Date observations and separate their interpretation

For the event on 17 August 2017, the LIGO–Virgo, Fermi Gamma-ray Burst Monitor and INTEGRAL joint paper published in October 2017 reported GRB 170817A arriving 1.74±0.05 seconds after GW170817. This is a dated result from that primary analysis, not a universal merger-to-gamma emission interval. The joint directional and temporal evidence supports a common event and shows that binary neutron-star mergers can produce short gamma-ray bursts. It does not establish that every short burst has an identical central engine, viewing angle or intrinsic delay.

An arrival delay can contain intrinsic source emission delay, geometry within the source and a difference in propagation times. Cosmological time stretching multiplies a host-frame intrinsic interval by one plus redshift. A bound on different messenger speeds therefore requires assumptions or constraints on the intrinsic contribution and the propagation model. Multiplying an arrival delay by c does not uniquely measure an engine radius. For a relativistic outflow, differing emission locations and photon paths can contribute even if all relevant signals propagate at their standard speeds.

Δtobs=(1+z)Δtsrc+Δtprop,OS/B(d)=BS/B(d) OS/Bprior\Delta t_{\rm obs}=(1+z)\Delta t_{\rm src}+\Delta t_{\rm prop},\qquad \mathcal O_{S/B}(d)=\mathcal B_{S/B}(d)\,\mathcal O_{S/B}^{\rm prior}
The first relation groups host-frame intrinsic and source-geometric effects into Δtsrc and propagation effects into the observer-frame Δtprop. The second is Bayesian posterior odds for specified shared-source S and background B hypotheses, with Bayes factor ℬ and declared prior odds. It is not a speed measurement by itself.↔ Scroll wide equations horizontally.

A chance probability depends on the actual search

Suppose a prespecified time-and-direction window has expected background count λ. Under a Poisson background model, the chance of at least one event is one minus exp(−λ). This is a probability of data under a background model, rather than the probability that the background hypothesis is true. Selecting a window after inspecting an event, scanning many sources or trying many energy cuts changes the relevant search space. A reported local significance cannot be carried into a population claim without accounting for those trials.

For M independent equal background searches, the probability of one or more windows producing an event is one minus exp(−Mλ). Real windows can overlap and share candidates, making the independence assumption false; calibrated simulations or time shifts can then estimate the full search statistic. Detector duty cycles, localization errors and event-quality selection also enter. A low background rate supports association only relative to the signal model and its prior plausibility. Neither a p-value nor a likelihood ratio alone supplies a complete posterior source probability.

PB(K≥1)=1−e−λ,PB,global=1−(1−PB,local)M=1−e−Mλ(independent equal searches)P_{B}(K\ge1)=1-e^{-\lambda},\qquad P_{B,\rm global}=1-(1-P_{B,\rm local})^M=1-e^{-M\lambda}\quad(\text{independent equal searches})
λ is the expected selected background count in one prespecified window and M is an integer number of independent equal trials. These probabilities refer to this toy Poisson search statistic; they are not the actual analysis probabilities of GW170817.↔ Scroll wide equations horizontally.

Combine messenger likelihoods with response and selection

A joint likelihood can combine independent detector data conditional on shared source parameters, while marginalizing uncertain distance, inclination, emission parameters and calibration. Conditional independence is a modeling claim, not automatic when instruments share backgrounds or selection information. For neutrinos, the expected count is the energy integral of number fluence times effective area, summed over flavors and evaluated for the source direction. A null result constrains that response-weighted fluence under an assumed spectrum and duration; it does not directly rule out all baryonic acceleration or fix bolometric source power.

The probability lab illustrates repeated opportunities for a false flag. Reset and set both controls to p=0.01,n=1, predicting zero flags with probability 0.99. Reset and set p=0.01,n=30, predicting zero flags with probability 0.99³⁰≈0.740, hence at least one with probability about 0.260. These are exact independent Bernoulli trials, not the Poisson windows or the actual multimessenger search. The lesson is to specify the trial family before interpreting an apparent coincidence; the instrument has no sky map, detector selection or astrophysical prior.

Calculate local and global background chances

A hypothetical prespecified coincidence window has expected selected background count λ=0.01. Find its chance of at least one background event. Then consider one hundred independent equal windows and calculate the probability of at least one such flag anywhere. These are toy search parameters, not GW170817 analysis values.

  1. For one Poisson window, Plocal=1−exp(−0.01)≈0.00995, about one percent. This conditions on the background hypothesis.

  2. The probability that all one hundred independent windows remain empty is [exp(−0.01)]¹⁰⁰=exp(−1)≈0.3679. Independence and a fixed trial family are essential.

  3. The global flag probability is 1−exp(−1)≈0.6321. This larger chance describes searching the full family, not a posterior probability that an identified astrophysical source is false.

The result

The toy local chance is about 0.995 percent, while the one-hundred-window global chance is about 63.2 percent.

Explore the model

Interactive lab

Interactive lab · probability

Count the opportunities for an independent false flag

Reset and set BOTH controls p=0.01,n=1: predict P(K=0)=0.99. Reset and set p=0.01,n=30: predict P(K=0)≈0.740, so P(K≥1)≈0.260. This exact binomial toy has no actual sky search, detector selection or source prior.

Count the opportunities for an independent false flagThese are exact binomial probabilities for independent trials with one fixed success probability. Playback tours the distribution; it does not resample outcomes. Current readings: Mean · np: 5; Variance · np(1−p): 2.5; Total probability: 1. All parameters are adjustable with the labeled controls below.03581000.070.150.220.3Successes kP(X = k)k = 0 · P = 9.77e-4
Exact probabilityHighlighted outcomeMean np
Mean · np
5
Variance · np(1−p)
2.5
Total probability
1
t = 0 s

These are exact binomial probabilities for independent trials with one fixed success probability. Playback tours the distribution; it does not resample outcomes. Parameter changes pause playback and recompute the model at the current elapsed time. Reset restores both parameters and returns time to zero. Playback starts paused. Adjust either parameter, then choose Play or Step.

Look forA fixed per-trial false-flag probability does not fix the chance of at least one flag over a larger search.

Check your understanding

A local background p-value of 0.01 most directly means what?

Try it yourself

For a hypothetical shared-source hypothesis S against a specified background B, take prior probability P(S)=0.01 and P(B)=0.99. A selection-aware analysis yields Bayes factor ℬS/B=100. Find posterior odds and posterior probability of S. Explain why a large Bayes factor need not imply near certainty.

Take a moment to make a prediction or work it out on paper. Open a hint when you need one.

Give me a hint

Multiply prior odds, not prior probability, by the Bayes factor. Convert odds O back to probability O/(1+O).

Compare with the solution

The prior odds are 0.01/0.99=1/99. Multiplying by 100 gives posterior odds 100/99. The posterior probability is 100/(100+99)=100/199≈0.503. The evidence strongly changes the odds, but the small prior probability balances much of that change. This value follows the two stated hypotheses and their selection-aware likelihoods; it is not an observed-event association probability.

Take it with you

  • Measured arrival delays require source and propagation models for physical interpretation.
  • A background chance probability is not a posterior association probability.
  • Joint source inference must include response, selection, trials and explicit hypotheses.

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