Cosmic Ray Finder

Application "Cosmic Ray Finder" is designed to detect traces of cosmic and terrestrial particles using a digital cam connected to the PC

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Windows-application "Cosmic Ray Finder" is designed to detect traces of cosmic and terrestrial particles using a digital cam connected to the PC.

When capturing the first frame of the application calculates the average distance to the color pixels of the frame, and if it exceeds the limit, it concludes, that the "eye" camera is not closed, the capture stops and a warning message is displayed.

The app allows you to set the limit value of the color channel (Limit, default 150) to detect "events" - the particle track. When registering the event pop-up message appears, and logging may be accompanied by an audible signal (Sound). In the event that the camera image of the particle track is stored in the image PNG-file in the current directory, or selected by the user, and in the log in the application window is logged time event detection and the values of the color channels for the brightest pixel.

To eliminate the effect of light (natural and artificial) necessary to cover the "eye" of the cam tape, the best in several layers.

When launched, the application is minimized to the tray with an star icon:

Application window:

To update the list of connected cams is a button list.

To control the capture of images from the selected cam are the start button start and stop button  stop .

After starting the capture application selects the highest resolution (Source), allows the cam to capture images.

"Worm" :

More about this method of detecting particles you can read here (in russian) - .

Archive with installation file

x-zip-compressed - 750.44 kB - 09/06/2020 at 06:25


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RichardCollins wrote 01/18/2021 at 16:24 point


Nice "worm" and tracks and project!

Are you counting, and displaying the counts to date by white level?  Or all three colors?  For an eight bit color  for RGB and Gray scale (R + G + B)/3 there are just 4*256 = 1024 counters.  If you are counting every frame (5 frames per second), then that is  256*5*86400 frames per day and 365.25 solar days per year, that is about 40.4 billion maximum.  In log2 terms that a counter of 36 bits.  A 64 bit counter would cover about 2^(64-36) 2^28 = 268,435,456 years of counts.

8 bytes per counter and 256 levels per color and 4 colors is 8*256*4 = 8192 bytes for storing counters.

You can pre-allocate your counters since you know how many there, initialize them to zero, get each pixel's RGBW values and just

var W = Math.floor((R +G  + B)/3);

RCounter[R] ++;

GCounter[G] ++;

BCounter[B] ++; 

If you have plenty of storage, keep lifetime counters, yearly counters, monthly counters, daily counters. For some projects counting pixels I keep counters for every frame, every second, every hour.  Many of the rare events can be sferics (lightning). I am running dark frames on cameras of many different kinds.  When I am set up to count several cameras for many days, I will see what rare events occur.  I plan to correlate the variations in time, using fast region or interest cameras to look for correlations with magnetic events (magnetometer arrays and nearby magnetometers), with power system events (monitor the frequency and voltage variations and spikes in the power supply), in gravitational events (a single camera or even a large network might not be able to see changes due to the sun and moon, but it is a large signal and easy to check.

Some of the cameras are capable of hundreds or thousands or tens of thousands of frames per second.  I have been looking for ones that can collect data over a million frames per second. With that time resolution you can determine from an array of darkened "noise" cameras where the noise comes from, and from the data, size and characteristics of the source or sources.  Electromagnetic noise from local AM and FM radios is possible.  I have not really started, but can exclude nothing yet.  Seismic noise can be checked with a local accelerometer or seismometer (largest events and noise), sound detectors.  But the global seismic array at is excellent, if a bit of  learning curve.

I am trying to find a way to read the raw pixels from Raspberry Pi, Ardunio, security, webcams (there are thousands of live cameras on the internet but they compress the signals and sometimes throw away rare events as noise), camera designers have their own sense of beauty and what is noise and what is not.  Read noise can sometimes be removed, and there are regions of operation where the camera is chaotic because it it driven too hard, or simply near a resonance in the system.  The thermal noise is easy to determine with a decent thermometer.  And some of what people call "thermal" or "kT" noise is from other sources.  

A Russian seismologist told me that she was seeing magnetic noise in her seismometer in northern latitudes.  And the broadband seismometers and superconducting gravimeters both picked up the direct gravitational acceleration pulses from the Japan earthquake. There is a group trying to make better gravimeters to do routine gravitational monitoring for earthquakes.  The speed of light and gravity are identical, except for dispersion. Moving masses (changing density in geometric voxels) changes the gravitational potential which diffuses outward at the speed of light. The gravimeters read the gradient as an acceleration. And for most practical purposes, the signal cannot be attenuated.  A three axis gravimeter in a deep cave should track the sun and moon with no change in the signal.  With three axes, you can minimize the sum of squares of the residuals, and solve for the best position and orientation of the sensor.  That gets more precise for permanent stations, and with high sensitivity and fast counters, should work on even a submarine or deep site.

I am trying to improve the cloud chamber for natural radiation.  CERN made a TimePix3 which separated the optical read layers (amplifiers, ADC and logic) from the sensor layer (photoelectric materials). They "bump bond" the sensor layer to the "optical" read layer.  I want to use that for a connector. A simple 1920x1080x30 by 12 bit read array in a camera is equivalent to a 1920x1080 = 2,073,600 parallel data channels.  If you could collect the same number of pixel samples at a million samples of pixels per second, that would be a 62 pixel regions, and the spatial resolution would be 4.8195 meters for correlation purposes.  The cameras are just semiconductor fabrications.  You could use larger pixels, and improve the sensitivity of the amplifiers and ADCs and reference voltages.  

If I had a way to design and build my own "noise cameras", I would start with existing radio astronomy arrays, or some of the big optical telescope sensors for stars that should be idle during the day.  Darken them and then look deep at the noise and look for correlation between stations around the world. The magnetic signals at low frequencies are equivalent in energy density to the gravitational energy density.  Since they are the same speed (gravity and magnetic signals) I now consider them the same field, just different spatial and temporal spectra and sources.

I am actually looking now for a way to fabricate my own cameras. But have no one to turn to for help finding how to get one build.  Using existing camera and equivalent sensors is the best I can do.  I went through all the electromagnetic interference groups, and Software Defined Radio groups because the SDRs can now get down to near zero frequency. For gravity and magnetism I use a log scale for frequencies and usually am looking from nanoHertz to GigaHertz events.  With the SDRs and enough computing capability, FFTS across that whole band allow for near continuous coverage. Earth based magnetic and gravitational sources are many, but it takes coordination and precision beyond what most people consider. Even LIGO still calls it "Newtonian noise" rather than treating it as just another signal to be monitored, correlated and studied in its own right.

Richard Collins, Director, The Internet Foundation

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