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In the graph below, does each dot represent the passage of six hours? If so, does that mean the last dramatic decrease in solar wind particles, from ≈24 particles/sec to ≈10 particles/sec occurred over a six hour period. If not, is it possible to determine how quickly that drop occurred?

enter image description here

Source

Wikipedia's caption:

Plot showing a dramatic decrease in the rate of solar wind particle detection by Voyager 1 (October 2011 through October 2012)

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    $\begingroup$ You can find the underlying data and analysis background here: sd-www.jhuapl.edu/VOYAGER I suspect that will give you finer time resolution, at the cost of quite a bit of noise. $\endgroup$ – Bob Jacobsen Jun 7 at 19:09
  • $\begingroup$ @BobJacobsen. Thanks. I wish I knew how to read through all of that stuff. $\endgroup$ – Bob516 Jun 8 at 3:30
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    $\begingroup$ @BobJacobsen thanks for the link! Feel free to comment or improve on my answer $\endgroup$ – uhoh Jun 8 at 15:58
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Thanks to @BobJacobsen's comment the data and other related information can be found in these links:

I've downloaded the 1 day and 1 hour averaged data as well as the background-subtracted data sets for 2012:

v1_2012_ion_flux_1d.txt
v1_2012_ion_flux_1h.txt
v1_pl0s_1d_2012_bgcor.txt

The data is Voyager 1 LECP: Z>=1 Ion Intensities [ions/(cm^2 sec ster MeV)] and the eight traces represent the eight energy passbands (MeV per nucleon):

0.040 - 0.053
0.053 - 0.085
0.085 - 0.139
0.139 - 0.220
0.220 - 0.550
0.550 - 1.050
1.050 - 2.000
2.000 - 4.000

The 1 hour data (as BobJacobsen points out) is too noisy due to low counting statistics perhaps, but the 1 day data shows a fairly smooth fall-off over a few days. So I don't think there is anything happening that is more abrupt than what you see in the plot in your question.

file structure Voyager charged particle data

Voyager charged particle data 1 day

Voyager charged particle data 1 hour

Voyager charged particle data 1 day processed

Python script to read the data files and plot them:

import numpy as np
import matplotlib.pyplot as plt
import glob

fnames = glob.glob('v1_*2012*.txt')

linez, nums = [], []
for n, fname in zip((2, 2, 7), fnames):
    with open(fname, 'r') as infile:
        lines = infile.readlines()
    print fname, len(lines), len(lines[42].split())
    linez.append(lines)
    rows = []
    for line in lines[n:]:
        try:
            row = [float(x) for x in line.split()]
            if len(row)>18:
                rows.append(row)
        except:
            pass
    a = np.array(rows).T.copy()
    a[a<0] = np.nan
    nums.append(a)

print ([n.shape for n in nums])

if True:
    plt.figure(figsize=[9, 6])
    for thing in nums[0][4::2]:
        plt.plot(nums[0][1], thing)
    plt.yscale('log')
    plt.title('one day averages', fontsize=18)
    plt.xlabel('days in 2012', fontsize=16)
    plt.show()

if True:
    plt.figure(figsize=[9, 6])
    for thing in nums[1][5::2]:
        plt.plot(nums[1][4], thing)
    plt.yscale('log')
    plt.title('one hour averages', fontsize=18)
    plt.xlabel('days in 2012', fontsize=16)
    plt.show()

if True:
    plt.figure(figsize=[9, 6])
    for thing in nums[2][3::2]:
        plt.plot(nums[2][1], thing)
    plt.yscale('log')
    plt.title('one day averages, processed', fontsize=18)
    plt.xlabel('days in 2012', fontsize=16)
    plt.show()
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