August 30 2022

The Transit of HAT-P-3b

The Transit of HAT-P-3b
By: Lucas Ahn
Date: Wed, Jul 27, 2022

As someone who is interested in astronomy but never had access to complex astronomical equipment, I was happy to have the opportunity to detect an exoplanet using the RASC Robotic Telescope situated in California. The goal of this project was to detect an exoplanet using the transit method, an exoplanet detection method used by astronomers that involves monitoring the host star’s brightness over a long period of time and comparing the star’s brightness to other stars. The star likely hosts an exoplanet if the star’s brightness dips in a consistent pattern. The project was done in 4 stages which included selection, imaging, processing, and analysis.

I first selected an exoplanet using Swarthmore College’s Exoplanet Transit Finder. I originally worked with Jenna Hinds to select two different exoplanets to study. I was given a new outreach coordinator named Samantha Jewett who told me that my original two selections didn’t work. After two more failed selections, they told me they were able to capture data for HAT-P-3b. The data included the original images along with bias, dark, and flat frames. The original data drifted, there were bright patches that made stars appear brighter than they should, and a meridian flip occurred later in the image sequence due to the telescope being flipped 180 degrees to continue tracking the star.

After receiving the raw data, I downloaded AstrolmageJ and was ready for the processing phase of the project. Since I was inexperienced with the tool, I closely watched the video tutorials so I didn’t mess up. I first created a master bias frame using the data reduction facility in AstrolmageJ and then used it to remove the bias from the flat frames. I noticed that white lines appeared in my processed flat frames.

In need of clarification, I asked Samantha if this will affect my data and she said that it’s most likely hot and dead pixels and will be removed by the dark frames. I took a break for a few months to focus on school and when I revisited the project earlier this summer, I noticed AstrolmageJ froze when I imported data. My coordinator sent me different solutions to try out and in the end, I reinstalled AstrolmageJ. With the issue fixed, I created a master flat frame and a master dark frame and applied them to my original data to equalize the brightness in each frame so I could compare stars to one another.

Next, I fixed the meridian flip by identifying the frame the flip took place in, then placing the frame and all the other frames after it into one folder. I then placed the image sequence in AstrolmageJ, flipped the sequence 180 degrees, and saved the images as .fits into a new folder. I then imported the unflipped images and saved them as .fits so that every image file was the same file type. Lastly, I stabilize my images using the “align stack with WCS or apertures” button. There was only one aperture present, but after some time, I clicked a button with a red circle and two other apertures appeared, allowing me to stabilize my images. Once they were stabilized, I saved them into a new folder.

Lastly, I moved on to analysis. I performed multi-aperture photometry which is the act of tracking the host star’s light over the entire image sequence and comparing the host star’s brightness to other stars of similar size. Some of the stars had abnormalities in their brightness, and thus I had to restart the process a few times to choose the right stars. Once the right stars were picked, I plotted each star. I noticed that my target star had a significant dip in its brightness which indicated an exoplanet. I wanted more data, so I added a line of best fit. The line of best fit gave new information including the planet’s size (0.97 Jupiter radii), its transit duration (1hr 42mins 34sec), and its orbital inclination (87.9 degrees). Its transit duration is a bit off compared to the data found in the Swarthmore College’s Exoplanet Transit Finder which is around 2hrs 5mins based on the ingress and egress times, though its calculated size is comparable to the data found by NASA which places the planet at around 0.94 Jupiter radii. Other notable information included the star’s density which is around 4.96036g/cm^3.

The Robotic Telescope Project was one of the most interesting projects I’ve done outside school. After completing this project, I learned more about exoplanet detection, developed problem-solving skills, and did interesting scientific research that will benefit my university application. Though at times the project was a bit frustrating and confusing, the amount of aid given, whether that be from outreach coordinators or video tutorials, making it a truly memorable and fun experience. And though the data I found was a bit off compared to the data found by NASA and other professional astronomers, I’m very grateful to have participated in this project, and I highly recommend this project to those who’re interested in astronomy either as a hobby or as a possible career path.

HAT-P-16b-finder-map-1-e1607620389657-360x360

December 10 2020

Finding HAT-P-16b

Jenna Hinds Blog 0

HAT's off to teens using robotic telescope

Two astronomy students from John Abbott College talk about their hands-on opportunity detecting an exoplanet using The RASC’s Robotic Telescope.

Imaging the Jellyfish

Jenna Hinds Blog 0

Choosing the Target

It seems that the Moon and the weather rarely cooperate, and when it’s clear, the Moon is often too bright to capture RGB targets. On one such evening last month I went in search of a good narrowband target.

As a marine biologist by training, of course I am always drawn to aquatic-themed targets. The Jellyfish is an interesting one as well. The light coming from it is almost entirely outside the visible range. That means that if you look through a telescope at it, it’s extremely difficult to see, if you can see it at all.

However, it makes for a great narrowband imaging target! It has lots of light coming from it in the Hydrogen-Alpha and Suphur-II range. Perfect for a night with lots of light pollution from the Moon.

The Jellyfish Nebula, or IC 443, is a leftover cloud of dust from a supernova. Its star likely collapsed between 3,000 and 33,000 years ago. When stars collapse, they eject their outer layers of dust and gas into space as they do so, and the core collapses into either a neutron star or a black hole, depending on the size of the original star.

I used AstroImageJ to process the images initially (it’s free and open source), and Adobe Photoshop to combine the black-and-white images into a colour picture.

Setting up the Scope

With the target chosen, I set about programming CCD AutoPilot to do my bidding. With the amount of time I had, I took 3 exposures each of Hydrogen Alpha, Sulphur-II and Oxygen-III. Each exposure was 1800 seconds, or 30 minutes. Longer exposures capture more light from the object. We also have special software and hardware on the telescope that allows for tenth-of-a-second micro-adjustments to the direction of the light. This corrects a lot of the light fluctuations caused by the atmosphere.

Once morning rolled around, I had the scope take flats for Hydrogen Alpha. Unfortunately this is all I had time for, as the scope was also taking flats for other targets at dawn. I may do another run on another night in order to collect more light frames and flats for other filters.

Checking back in the morning, all was well and the images were ready for processing!

Test

Preparing the Light Frames

So out of that process there are now 9 images in 3 wavelengths showing the target itself, and 5 flat frames just for one of the filters. I wasn’t be able to create a fully processed final image, but I at least got started.

The only images I could truly fix errors on were the 3 Hydrogen-alpha frames. The other 6 frames (S-II and O-III) don’t have flat frames, so I removed the dark frames, and am waiting to remove the bias and flat together once I have the flat frames I need.

Take a look at the difference between a processed light frame and a raw light frame. Processing the image takes out broken pixels, hotspots, vignetting and electronic noise.

For my purposes, I stabilized all the images in AstroImageJ before moving over to Photoshop, because I find it easier.

Getting a Colour Image

Using Photoshop, I combined each filter’s three images into a single image. Doing this allows for individual errors in each image to be eliminated. You can actually do something similar with daytime photos with people in them. If you combine them using certain settings, the “odd pixels out” will be removed (like a person who is in one photo but not in the other two). In an astrophotographical sense, this makes for less noisy photos. The more frames you combine, the better the result will be! Stay tuned for a more detailed tutorial on this process (though hopefully made by someone with more experience than I).

It is at this point that artistic skill comes into play a bit. You can play with levels to make each black and white image pop the way you want it to. But it takes a creative hand and eye!

Now, with one individual frame for each colour filter, they can be applied to light channels, red, green and blue. Since the images are in black and white, when applied to colour channels, it just tells Photoshop to illuminate those coloured pixels accordingly. For example, a black and white image put into the red channel would turn into a red and white image. Same goes for the other two channels, and then when all are combined, you get a colourful image based on which colour pixels are lit up more or less than others. What was black in all three images will be black in an RGB image. What was black in the red and blue image but white in the green image will appear green in an RGB image.

: Jelly SII before stacking

: Jelly SII low contrast

: Jelly SII high contrast

What I was supposed to do

The Pillars of Creation, processed in the Hubble palette.

What I actually did

Whatever it was it definitely wasn’t right.

First Shot: Not Quite Accurate

Voila! A colour image! Sort of. I made a mistake on my first try, actually. The Jellyfish Nebula is not supposed to be pink, as pretty as that is.

I mean, really though, this is a false colour image as it is. If you looked at this with just a plain old telescope, it would look red. What we’re making is our own thing. We’re taking light from parts of the spectrum you can’t see and pushing it into parts of the spectrum you can.

Sulphur-II emits light at 672nm. That’s a deep deep red colour. Hydrogen-alpha emits light at 656nm. Also red, but slightly brighter. Oxygen-III emits light in the green-blue-turquoise-y part of the spectrum, at 498nm. This poses a problem. We have red (about 570nm), green (about 540nm) and blue (430nm) to work with. The wavelengths the telescope imaged don’t align with those specific colours.

Astrophotographers often work in something called the Hubble Palette. You may recognize this palette when you see it out in the wild. It just means we assign the colours based on the closest wavelengths to them. Starting with Sulphur-II, the deepest red of all, it gets assigned to the red channel. Hydrogen-alpha is the next one along, so it gets assigned to the next-reddest channel: green. Finally, Oxygen gets assigned to blue (it’s actually pretty close to this wavelength too).

I didn’t do that at first. I accidentally assigned Hydrogen to red (instead of green), Sulphur to blue (instead of red) and Oxygen to green (instead of blue). I got as much wrong as I could. It did make for a beautiful pink photo though, as red and blue combine to make that brilliant colour.

And really, does it matter? It’s not like we can actually see this nebula anyway. The colours aren’t represented as they are. If they were, the whole thing would be red. But there is an accepted way of doing things, and alas, I caved to the astronomical peer pressure.

Second Time's the Charm

Here’s the final image with the colours assigned to the Hubble palette. I played with the levels to get the brightness and colour you see here. You can see a bit of noise in the middle from the two filters I didn’t have the flats for. There is always room for improvement!

This was only my second shot at trying to process astrophotos. It takes a lot of work and a lot of problem solving, but is it ever satisfying at the end to have wrestled with the various programs and files and won!