The Causally Ambiguous Duration-Sorting (CADS) effect is a
scientifically observed phenomenon where the number of photons detected before
a decision is made appears to follow patterns connected to that future
decision. A one-year experiment involving light detection and randomized trial
lengths revealed consistent and measurable links between early photon behavior
and outcomes chosen later. These findings challenge the conventional view of
causality and suggest that time and light may behave in ways that align with
retrocausal or time-symmetric interpretations of quantum physics.
What the CADS Effect Describes
The CADS effect shows that measurements taken before a
future choice reflect that upcoming choice. In the experiment, photons were
counted during three initial intervals. Then, a random decision was made about
whether to continue or stop the experiment. The number of photons detected
before that decision often varied depending on the future choice, suggesting
that present events may contain information about what is yet to happen.
How Retrocausality May Explain the Effect
Retrocausality is the idea that future events may influence
what happens now. This concept does not appear in daily experience, but some
theories in quantum physics suggest time may operate in both directions. In the
CADS experiment, photon behavior recorded before the decision appeared to
correlate with what was chosen afterward. This does not mean the future
directly changes the past, but that some conditions may link them in a
non-traditional way.
How the Experiment Was Designed and Repeated
- A red
LED produced light in the form of photons, which entered a sealed
detection system.
- Each
experiment began with three 11-second windows where photon counts were
recorded.
- After
the third interval, a physical random number generator chose how many
additional intervals the experiment would continue: 0, 20, 30, or 60.
- This
generator worked using light-based randomness and was not connected to the
photon counter in any way.
- The
system ran automatically every day for one full year, with a short pause
between runs.
This design ensured isolation between the random decision
and the early measurements, making any connection between them scientifically
unusual.
How the Data Were Processed and Understood
- Only
photon data from the first three intervals were analyzed.
- A
high-pass filter was used to remove long-term trends and highlight
short-term patterns.
- A
method called Fourier transform was applied to detect repeating signal
patterns.
- Data
were grouped into six-hour blocks to observe consistent cycles across
time.
- Statistical
tools compared photon counts in each block to the duration chosen later.
These methods helped determine whether early measurements
could predict the outcome of a future random choice.
What the Results Indicated About Photon Behavior
- Photon
counts recorded before the random decision showed consistent differences
based on the final outcome.
- These
patterns repeated in regular cycles throughout the year.
- The
strength of the result was measured using a value called sigma, which
shows how likely an outcome is due to chance. A sigma of 4.7 or higher is
considered strong.
- In the
CADS experiment, sigma often exceeded 4.7, making the pattern unlikely to
be random.
- The
effect held across all conditions and time blocks.
These findings suggest a potential time-based relationship
where present measurements reflect future decisions, even when those decisions
are unknown at the time.
How the CADS Equation Predicts Signal Strength
A formula was developed to predict how strong the early
photon signal would be based on how long the experiment would last.
Signal strength = Constant – Coefficient × Cycles per run
- Cycles
per run refers to how many full signal patterns fit into the total
duration of the experiment.
- Coefficient
is a value that reduces the signal as the number of cycles increases.
The result showed that the longer the experiment was going
to run, the weaker the early photon signal appeared. This relationship formed a
reliable model that may help analyze similar effects in other systems.
Why the Moon’s Phase May Affect Photon Counts
In addition to the main findings, photon behavior appeared
to follow the lunar cycle:
- Counts
were higher during the waning gibbous and first quarter moon phases.
- Counts
dropped near the new moon.
- This
pattern repeated every month, even though the experiment was sealed and
shielded from outside light.
The cause of this effect is unknown. It may involve changes
in gravity, electromagnetic fields, or other environmental influences. Further
investigation is required to understand this pattern fully.
How the CADS Effect Fits with Quantum Theory
The CADS effect aligns with quantum models where time does
not move in only one direction. These include:
- Two-state
vector formalism, which suggests the present is shaped by both the past
and the future.
- Transactional
interpretation, which allows for time-symmetric exchanges between
particles.
- All-at-once
models, which treat time as a complete structure rather than a flowing
sequence.
The CADS experiment is different from most, which follow a
“prepare–choose–measure” pattern. In CADS, the flow is
“prepare–measure–choose–measure,” where the system is observed before the
outcome is even selected. This timing makes the results unusual and worth
further study.
What Remains Unclear About the CADS Effect
- The
experiment has not yet been repeated by independent research groups.
- The
reason for the observed link between early measurements and later choices
is not yet understood.
- No
method has been found to use the effect for real-time communication with
the future.
- The
lunar influence, while consistent, remains unexplained.
These open questions suggest that the CADS effect may
involve new physics, unknown environmental variables, or both. Continued
research is needed to determine the cause.
What the CADS Effect May Be Useful For
If the CADS effect is confirmed through further experiments,
it may have value in several fields:
- Quantum
computing, where light-based systems require accurate timing and behavior
prediction.
- Precision
measurement (metrology), especially in systems where time-related light
behavior matters.
- Foundational
physics, where models of time, cause, and effect are still evolving.
The ability to detect patterns in the present that relate to
the future may also help improve tools for forecasting, diagnostics, or system
control in advanced technologies.
Conclusion
The Causally Ambiguous Duration-Sorting effect suggests that photon measurements made before a decision may reflect the result of that future decision. This challenges the common belief that only the past influences the present and supports interpretations of time where past and future are linked. The CADS equation helps describe this relationship, while the consistent lunar effect adds further mystery. These findings may reveal a deeper structure in how light and time interact, opening new possibilities in science, technology, and the study of causality.