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Especially to those among us that do unguided imaging a new and free software-tool I recently created may be of interest: FITSalize. When doing unguided imaging stability of the set-up is of the essence. Question is how one can properly assess the stability of a tripod or an observatory and distinguish flexure in the imaging train from deformation of the basis of the set-up. From the images themselves it is impossible to assess whether elongated stars are the result of tracking errors or due to stability issues. FITSalize is a command-line tool that runs under JAVA and uses ASTAP to plate-solve the images taken and to determine their SQM values. It is built to analyse FITS-files from a stationary scope (i.e. a scope firmly attached to the pier or base of the mount and pointing in a fixed direction) and to convert the images to accurate alt/azi coordinates. Being taken with a stationary scope the alt/azi coordinates should remain unaltered during the imaging period. Any deviation may indicate issues with the stability of the set-up. As these plate-solves result in J2000 RA/DEC coordinates they cannot be directly used to analyse stability. FITSalize corrects for precession to produce mean JNOW RA/DEC coordinates from the J2000 RA/DEC, then corrects for the effect of nutation in longitude and obliquity of the ecliptic and for the effect of annual aberration to find the apparent JNOW RA/DEC. These are then converted to alt/azi and stored (with all intermediate results) in a .csv-file. The algorithms in FITSalize are based on J. Meeus, Astronomical Algorithms, (Richmond (VA), 2005) and have been compared to results from the SOFA-library (many thanks to Massimiliano Chersich for testing this, he also initiated this type of measurements last year at the 10Micron forum) and with the algorithms in Han Kleijn's ASTAP and Stellarium (only Stellarium does not apply annual aberration to the calculations). Using your favourite spreadsheet (examples are provided) graphs can be produced to visualise the stability: Above example shows the stability of InFINNity Deck (my observatory) over a period of approximately six hours. Deformation in altitude was less than 4 arc-seconds, in azimuth less than 2 arc-seconds. In addition to deformation measurements FITSalize can also be used to plot SQM-values against alt/azi. For this regular FITS that were acquired to image a target are analysed: In this example light pollution affects the SQM-values in the north-east of the observatory. From those same FITS the focuser gradient and intercept can be calculated: For more info and download of the software see the FITSalize-page on my web-site. Nicolàs

Just finished work on a sky quality meter with built in wifi. The device is based on the ambient light sensor TSL2591 and the wifi board ESP32. Communication between the two boards is through I2C. The device has a 40 degrees lens. The light sensor is programmable, which means you can set integration time (from 100 to 600 ms) and gain (from 1 to almost 5000, in 4 steps). I implemented automatic adjustment of these parameters to allow for the highest dynamic range (600M:1 accoring to the spec sheet). The device shows Sky readings as a web page. It is connected to a local wifi network, although it could also create its own access point. So far I haven't been able to calibrate the sqm yet, partly due to eternal cloud cover. But it should only require one parameter to be adjusted. The code is available on GitHub. Sky-Quality-Meter Here are som pictures. The components: The parts connected: The finished device: This is how output is presented:

Last year I was given a Unihedron SQM-L, the narrow field of view version of their gadget for measuring night-sky brightness. Since then, I’ve nipped outside to take zenith readings whenever I’ve been able, often a few times per night. As a result I now have 85 data-points, all from my back garden in Sunbury on Thames which rates a 19.04 on www.lightpollutionmap.info . As it turns out, this agrees well with the data I’ve collected. The darkest I’ve measured at this location has been 19.13, with 4 records better than 19.05 and 10 better than 19.00. Plotted against Moon altitude, it looks like: One thing I noticed very early on was that the reading generally gets darker and darker as the night goes on. The chart below suggests the data agrees, but how strongly I’m not adept enough yet with my statistics to work out. If anyone fancies doing this for me, I’d be grateful, I’ve attached the data .csv file I think to the end of this post. The data itself: each record contains date, time[GMT], SQM value, Moon phase, Moon altitude . For the purposes of my analysis, I’ve converted the time value into hoursafter6pm, which allows the intercept of the regression solution to be loosely considered as the “6pm starting point” for the darkness estimation, which is OK for this dataset as my data is all from this latest Autumn/Winter. I’ve done an “ordinary least-squares” regression with multiple input variables. At first glance it seems to me that the SQ vs altitude chart above should not behave well with that: there’s a clear kink, intuitively obvious I guess, at the point the Moon altitude goes negative. To cope with that, I divided my data into two and did three separate regressions: “Moon up” data, “Moon down”, and “All data” but treating phase and altitude as zero if the Moon is below -5 degrees (I chose -5 degrees arbitrarily). With Moon up, I decided the SQM value will depend on Time of Night, Moon Altitude and Phase. With Moon down, it only needs to depend on time of night. Thus my regression model is: SkyQual = a + b.timeafter6pm + c.phase + d.altitude + residual or rearranged residual = a + b.timeafter6pm + c.phase + d.altitude – SkyQual The analysis involves minimizing the sum of (the squares of the) residuals, by hunting around for the appropriate values of a, b, c & d which yields this minimum. I used MS Excel’s built-in Solver to do the “hunting around”. The following table summarizes the results: In words, using “Moon Up” as my subject, my Sky Quality, in magnitudes per arc-second, can be estimated as 19.28 mags/arc-sec plus 0.0314 /hour minus 0.864 /full-phase (or 0.216 /quarter) minus 0.0186 /degree above horizon (or 0.186 /10 degrees). This is a pretty simple analysis. I’m sure there’s theory and formulae available relating Moon-altitude and -phase to extra sky brightness, but I haven’t used any of that here. And the “error model” I’ve used implicitly assumes that the relationships between SQM-reading and the variables are linear. If anyone is curious and wishes to do their own analysis, my raw-ish data is available as a .csv file attachment at the end of this post. Cheers, Magnus A note about the data collection: each reading is an average of a few readings at a given time, with outliers rejected. For instance, often the first press yields an outlier, and over the following few seconds subsequent ones tend to settle down. So the series of readings 19.05 (me getting excited), 18.85, 18.86, 18.86 , which is a quite typical pattern, would cause me to record 18.86. My highest recorded reading, 19.13, was indeed where it settled down. Other “one-on-one” charts: SQMLdata201903.csv

So, finally my DIY arduino SQM is finalized, it is not calibrated yet, but hang in there, i will do so in the near future... But first i want to point out that i didn't want any scientific grade SQM but just a simple tool that i can use to compare different photo-sites around my home-town here in Sweden Feel free to Use it as you want to, if you wan't to calibrate it, change the value at: const float A = 22.0; More pictures and code are available at SGL's Yahoo-site. The parts needed for this is: Arduino approx. 30USD Light to freq-sensor: TSL237 - electrical component shop 5 USD 0.1-0.01uF capacitator 20 degree lens - ebay 1USD UV/IR-cut filter between the lens and the sensor - ebay 1 USD Some kind of housing and cables/connectors The lens is mounted to a plastic cover with superglue, the UV/IR filter is mounted underneath it and underneath that, the sensor. The sensor has three connectors, one gnd, one vdd and one signal, so it quiet simple to connect, you should use a capacitator to stabilize the +5v. Here is a video of it how it works (not the best quality i'm afraid. And here is the code: The libraries you need is at the top of the code. // Author: Daniel Sundström and Ola Karlsson, Arvika, Sweden #include <FreqMeasure.h> #include <Math.h> #include <LiquidCrystal.h> float Msqm; const float A = 22.0; int buttonSQM = A2; int val = 0; int reading = 0; int percentage = 0; LiquidCrystal lcd (12, 11, 10, 9, 7, 6); void setup() { pinMode(buttonSQM, INPUT_PULLUP); digitalWrite(buttonSQM, HIGH); lcd.begin(16,2); Serial.begin(19200); } double sum=0; int count=0; void loop() { val = digitalRead(buttonSQM); if (val == LOW) { reading = 1; lcd.clear(); FreqMeasure.begin(); while(reading) { if (FreqMeasure.available()) { // average several reading together sum = sum + FreqMeasure.read(); count +=1; percentage = count/31.0*100.0; lcd.setCursor(0,0); lcd.print("Reading"); lcd.setCursor(8,0); lcd.print(percentage); lcd.setCursor(11,0); lcd.print("%"); if (count > 30) { double frequency = F_CPU / (sum / count); sum = 0; count = 0; Msqm = A - 2.5*log10(frequency); //Frequency to magnitudes/arcSecond2 formula lcd.clear(); lcd.setCursor(0,0); lcd.println("Mag/As2: "); lcd.setCursor(9,0); lcd.print(Msqm); delay(5000); lcd.clear(); reading = 0; FreqMeasure.end(); } } } } }

I used to use Needless-org for to get a rough idea how good or bad possible dark sites would be but since I've been taking my own sky measurements I've discovered that Needless is way out on its estimations. I know Needless is just a simulation but I used to use it a lot and travel up to an hours drive just to get somewhere that it said was good when I could of stayed a lot closer to home and had just as dark skies. My local dark site Needless says its LM 5.5 but its actual readings from NELM and SQM are more like 6.3! This winter I'II be saving a lot of money in fuel and staying closer to home! Anyone else compared actual on the ground readings to a LP map and had a pleasant surprise?