Prognozist & SteelQuasar
Prognozist Prognozist
SteelQuasar SteelQuasar
Prognozist Prognozist
Hey, I've been crunching some data on solar flare patterns and how they might impact satellite orbital stability—looks like a perfect storm of numbers and space. Think you’d care to compare some predictive models?
SteelQuasar SteelQuasar
Sure thing. Throw the models over, I'll check the residuals and see if the flare signatures line up with the orbital decay data. Just keep the equations clean, no unnecessary variables.
Prognozist Prognozist
Alright, here’s the tidy version: for the orbital radius, use R(t)=R0‑a*t^b+c*sin(d*t+e) where R0 is the starting altitude, a and b capture the secular decay, and the sinusoid models the periodic flare‑induced perturbation. For the flare intensity, I(t)=I0*exp(-(t‑t0)^2/(2σ^2)) gives you a clean Gaussian pulse; tweak I0 and σ to match your flare catalog. Then just compute the residuals r(t)=R_obs(t)‑R(t) and run a correlation test against I(t) – if it’s significant you’ll see the flare signature line up. Happy crunching, but if you need a second set of eyes, you know where to find me.
SteelQuasar SteelQuasar
Nice set‑up. First, verify the units on a and c so the sinusoid stays in meters, not km, and make sure d is in radians per second. Then run the correlation, but keep an eye on the p‑value – a 0.05 threshold is standard, but with noisy orbital data you might need a stricter cutoff. If you spot a coherent lag between I(t) and the residuals, shift the Gaussian center by that lag and see if the fit improves. Let me know what you get; I’ll double‑check the math.
Prognozist Prognozist
I ran the model, made sure a and c are in meters by scaling R0 from km to m, d is 0.002 rad/s, and the residuals r(t) show a clear periodicity that aligns with I(t) when shifted by about 1.2 hours. The Pearson r is 0.73 with a p‑value of 0.001, well below 0.01. Shifting the Gaussian center by that lag improves the fit by 12 % in R². Looks like the flare signature is really driving the orbital decay—just the way the numbers wanted me to say.
SteelQuasar SteelQuasar
That’s solid evidence. A 1.2‑hour lag lines up with the transit time of the solar wind pulse. Next step—run the same filter on a neighboring satellite’s data to confirm the effect isn’t just local noise. If it holds, we’ll have a good predictive hook for future flare events. Good work.
Prognozist Prognozist
Sure thing—just pull up the neighboring satellite’s telemetry, apply the same Gaussian filter, and watch the residuals line up. If it matches, you’ll have a real lead time. Otherwise, it’s just a lucky coincidence, as usual. Happy hunting.
SteelQuasar SteelQuasar
I'll pull the telemetry, align the timestamps, and apply the same filter. If the residuals correlate with the flare pulse there, we get a repeatable lead time. If not, we’ll log it as a low‑probability event. Either way, the data will speak.
Prognozist Prognozist
Sounds good, just keep the equations tight. If the other satellite shows the same 1.2‑hour lag, we’re onto something. If not, we’ll just chalk it up to statistical noise. Either way, I’m sure the data will let you know.