First light for a Macbook Neo laptop for astronomical imaging

The Macbook Neo laptop (Running AstroDMx Capture)

The Apple MacBook Neo 13-inch Laptop with A18 Pro chip has a Liquid Retina Display, 8GB of Unified Memory, 256GB SSD Storage

The Apple A18 is a 3nm (TSMC N3E) 6-core System on a Chip. It features 2 performance cores (4.04--4.05 GHz) and 4 efficiency cores, a 5-core GPU, and a 16-core Neural Engine capable of 35 Trillion Operations Per Second (TOPS). It supports 8GB of LPDDR5X RAM on-package with 17% more memory bandwidth than previous generations, offers high energy efficiency (3-4W sustained), and is designed for on-device AI.

It has the same CPU performance as the A16 Bionic while consuming 30% less power. Due to its power efficiency the Macbook Neo has an extremely long battery life before requiring re-charging.

Single-Core Performance:

The A18 Pro often outperforming the most powerful x86 desktop processors. In Geekbench 6 tests, it achieves single-core scores around 3,400–3,500. This puts it ahead of top-tier desktop CPUs like the Intel Core i9-14900KS and the AMD Ryzen 9 9950X as well as The snapdragon ARM SOC in single-threaded tasks .

Multi-Core Performance:

Due to its 6-core architecture (2 performance, 4 efficiency cores), the A18 Pro falls behind high-end x86 desktop and laptop processors as well as the Snapdragon SOC in heavy multi-threaded workloads. With a multi-core score of approximately 8,500–9,100, its performance is comparable to mid-range mobile or older desktop x86 CPUs:

The Macbook Neo is the lowest cost laptop that Apple have ever released, but it retains the usual Apple build quality.

All of these factors make the Macbook Neo very suitable for astronomical imaging.

The Macbook Neo was used with two telescopes and two cameras to make initial tests of different aspects of astronomical imaging.

For deep sky imaging we used an Askar 71F quadruplet apochromatic astrograph refractor paired with an SVBONY SV405C OSC camera. The scope was fitted with an Altair V2 magnetic 2” filter holder containing an IR/UV cut filter.

For lunar imaging we used a Skymax 127 Maksutov Cassegrain paired with an SVBONY SV505C OSC camera fitted with an IR/UV cut filter.

Deep Sky imaging

The equipment used

Screenshot of AstroDMx Capture capturing FITS images of M5

The data were debayered, stacked and part processed in PixInsight and further processed in GraXpert, SetiAstroSuitePro and Gimp3. This was the procedure followed for each of the three deep sky objects tested: M5, M3 and the Leo Triplet.

M5

M3

Screenshot of AstroDMx Capture capturing FITS images of The Leo Triplet

The Leo Triplet

Lunar imaging with a Skymax 127 Maksutov Cassegrain paired with an SV505C OSC camera.

1000 frame RAW 8 bit SER files were captured of 9 overlapping panes of the Moon.

AstroDMx Capture capturing RAW lunar SER files

An eight pane mosaic of the 68.5% waxing Moon was captured (we later found that only 8 panes were necessary). Each pane was a 1000 frame SER file captured by AstroDMx Capture running on the Macbook Neo through a Skymax 127 Maksutov using an SV705C OSC camera fitted with an IR/UV cut filter. Initially the SER files were transferred to another computer for stacking and processing. Each pane was a stack of the best 90% of the frames in the SER, stacked in Autostakkert!3. The 9 panes were stitched in MS ICE, wavelet processed in Registax6 and finished in Gimp3.

The final mosaic of the Moon

We then decided to find out how much of the data processing could have been done directly on the Macbook Neo. These were important tests, but it is normally our practice to transfer data to a desktop mini computer for processing and posting.

Planet Stacker X

Planet Stacker X is a native Apple silicon stacking and processing program.

Screenshot of a SER file loaded into Planet Stacker X

Stacking

Screenshot showing the stacking is completed and the opportunity to take the stacked image directly into the processing part of the program or be save to be loaded into this software later

Screenshot of the processing part of the software doing wavelet sharpening

The processed image exported as a 16 bit TIFF file

Planet Stacker X is a modern, native macOS successor to PlanetarySystemStacker (PSS), and was developed by Rain City Astro.

The relationship between the two is defined by their shared purpose and architectural evolution: The developer created Planet Stacker X out of frustration with running PSS on Apple Silicon (M1/M2/M3) Macs. Because PSS is a Python-based application, it requires complex dependency management and often relies on x64 emulation, which can be fragile and slow on newer Macs.

Planet Stacker X shares all the core features of PlanetarySystemStacker, and was built "from the ground up" specifically for macOS frameworks.

Unlike PSS, Planet Stacker X runs natively on Apple Silicon without emulation and utilises GPU acceleration and the Apple Neural Engine to significantly speed up analysis and stacking pipelines.

Planet Stacker X also includes image processing tools that are not found in PSS.

While PlanetarySystemStacker remains a powerful open-source tool for Windows, Linux, and older Macs, Planet Stacker X is a more user-friendly, high-performance alternative for the modern Mac Apple Silicon devices.

Panorama Stitcher

Panorama Stitcher was developed by Olga Kacher for macOS and iOS. It runs natively on Apple silicon. It is built on its own propitiatory engine and is not derived from other stitching software. It uses its own algorithms for automatic alignment and exposure levelling and it features fully automatic drag and drop for loading images.

Screenshot of Panorama Stitcher stitching two overlapping panes of lunar images

The images were stitched using planar motion and were saved as a 16 bit TIFF file.

All eight unprocessed panes were similarly stitched with Panorama Stitcher

The stitched image was processed in the Planet Stacker X image processor

Deep Sky processing with Apple silicon

We installed and tested various software that is native Apple silicon.

Deep sky stacking software

Affinity

Siril

ASI Studio

PixInsight will also run on Apple silicon but we did not install it here.

General image processing software

GIMP3

Pinta 

We shall continue to test the Macbook Neo for astronomical imaging, including solar imaging. However, so far it has performed perfectly. This article was written on the Macbook Neo using Libre Office Writer which is an open source alternative to Apple's Pages, and runs natively on Apple silicon.

Feature release of AstroDMx Capture Version: Version: 2.17.2 ( All platforms )

Nicola has released a new version of AstroDMx Capture

For Linux x86-64 • Linux ARM • macOS x-86 • Apple silicon • Windows 

Mutatis mutandis

• Added: QHY Filter wheel support (tested on the QHY Minicam)
• Added: sub-millisecond timestamp support for SER video files
• Added: New controllable 16-bit preview transform
• Fixed: Preview buffer sizing issues for Raspberry Pis and possibly other systems with low-powered GPUs
• Fixed: Touptek cameras incorrectly reporting failed controls
• Fixed: Problems with the iOpteron INDI focusers
• Improved: Sensor temperature readout in the UI
• Improved: QHY cooling functionality
• Removed: Message asking to change the capture mode from Video to Frame when entering long exposure mode
• Updated: PlayerOne SDK
• Other bug fixes and improvements

Although AstroDMx Capture Version 2 was previously described as being in maintenance mode, Nicola has decided to release this update as a stop-gap feature release while work continues on Version 3.

Development of Version 3 is progressing well, with a major focus on modernisation, improved UI design, and long-term maintainability. However, it is not quite ready for release yet, and Nicola wants to ensure it meets the standard expected before making it available.

In the meantime, Version 2 continues to receive occasional updates where appropriate.

Fitting an iOptron iEAF to an Askar 71F apochromatic astrograph refractor.

 

The iOptron iEAF attaches easily to the Askar 71F APO astrograph telescope with the supplied fitments. We chose to use this auto-focuser to test another make of EAF in AstroDMx Capture. The iEAF has INDI, INDIGO, Alpaca, and ASCOM drivers.

As is becoming more common, the iOptron iEAF has buttons to manually control the focusing, a useful feature for visual observers.

A user review, whilst praising the focuser, pointed out that the USB C port (which is actually a USB 2.0 device), is a weak point and can become damaged if the cable pulls up or down excessively. Mindful of this, a right-angled USB C (male) to USB A (female) adapter was plugged into the USB C female port on the iEAF and the adapter held rigidly to the iEAF with a bead of Sugru moldable glue. After about 48 hours the Sugru is set rock hard and prevents the connector from moving when a cable is attached.

It is a fact that this may not be necessary if certain types of cable management are used, but we elected to use this precaution anyway.

We are looking forward to testing this setup with AstroDMx Capture.

Dealing with Walking Noise in deep sky images

Walking Noise stems from fixed pattern noise on the camera sensor, which is then exacerbated by imperfect polar alignment (drift) or lack of intentional shifting between exposures (dithering). It appears as faint, coloured "streaks" like "rain" across the background of the final, stacked image, often in the direction of declination drift. The camera's fixed noise moves slightly with each shot. When software stacks the images, these noisy pixels get averaged into lines. Some cameras are more susceptible to walking noise than others.

Dithering is the best preventative solution, which involves intentionally shifting the mount by a few pixels between exposures so noise does not stack on the same spot.

Using dark frames to subtract hot pixels helps mitigate walking noise as does active cooling of the sensor which reduces the thermal hot pixels that create noise. 

Walking noise is often invisible on single frames but appears after stacking and aggressive stretching of the image. 

However, we shall consider here how to deal with a situation where walking noise could not be or has not been avoided. This involves using a software solution to removing the walking noise.

There are a number of de-noising programs; some of them are AI, trained neural networks trained to detect noise and reduce or remove it from an image. They are all able to remove some walking noise but are really intended for the removal of more random noise, so often leave behind traces of the walking noise.

Frankin Marek’s SetiAstro contains a specific walking noise AI de-noiser in his Cosmic Clarity suite. This is trained to detect and remove walking noise specifically and so is likely to make a better job of the de-noising. This software is available for PixInsight and is fully integrated into SetiAstroSuitePro.

Paul Howat, a prominent imaging member of the Swansea Astronomical Society was king enough to provide me with a 16 bit stacked image with walking noise, unguided with a HAC125DX telescope and a QHY585C un-cooled camera. It is a good image but when aggressively stretched, walking noise is revealed.

Click on any image to get a closer view

The stretched image was loaded into SetiAstroSuitePro and Cosmic Clarity Walking noise de-noised using the maximum settings. The image has been zoomed to reveal a portion where the walking noise is clearly visible.

If this image is inspected closely (by clicking on it) the walking noise can be seen.

The de-noised image in SetiAstroSuitePro

If this image is inspected closely (by clicking on it) the walking noise can be seen to have been removed.

Animation blinking between the noisy and de-noised images

Click on the animation to get a closer view.

The Cosmic Clarity Walking noise de-noiser in SetiAstroSuitePro has done a good job of removing the walking noise from the image.

I then processed the de-noised image to show that it is a good image. Of course, image processing is always to the taste of the person doing the processing and the end result may not be what Paul would have produced. Nevertheless, it is an image, free of walking noise and showing the various regions of nebulosity in and between M42, M43 and the Running man nebula.

Processed image

In conclusion, SetiAstro's walking noise de-noiser did a good job of removing the walking noise from this un-guided, un-cooled image.

PlanetarySystemStacker (PSS)

 PlanetarySystemStacker (PSS)

PlanetarySystemStacker is a free, platform independent (runs on Linux, Windows, and macOS), open-source Python based program used in astrophotography to create high-quality, sharp images of planets, the Sun and Moon from sets of image files, AVI or SER files. It analyses and stacks the best frames to reduce noise and reduces distortions. It ranks frames by quality, aligns them globally, and computes a mean image. The workflow includes functionality for analyzing, editing alignment points (allowing manual adjustment), and "blinking" (reviewing) frames to remove poor quality or corrupted frames before final stacking or to compare a processed image with the original unprocessed, stacked image.

PlanetarySystemStacker is available to download via GitHub.

We chose an alternative way of running PSS. The Windows version runs perfectly in WINE in Linux, which made installation and running as easy as it is in Windows.

PSS is another of those programs that have largely escaped my attention and which merits serious consideration for inclusion in one’s aresenal of image stacking and processing software.

PSS can debayer RAW data which means that data need only be 1/3 of the size of RGB data, if they are colour data.

This is good news for Seestar users who capture RAW AVIs of the Sun and Moon. Also, if RAW SER files are captured, the colour files captured can be captured quicker as well as being only 1/3 the size of an RGB image.

Clicking on any image will give an even closer view

We tested the software on Seestar S50 whole disk RAW lunar AVI and on Lunar and H-alpha surface data. Whole disks are best dealt with like planet data.

Screenshot showing the quality curve in relation to the % of frames selected for stacking

Seestar lunar RAW AVI debayered and the best 50% of frames stacked in PSS

Screenshot showing wavelet processing the stacked image in PSS

Final processed and cropped Seestar S50 lunar image

Stacking a surface image

Screenshot showing the lunar stacked surface image

Screenshot showing Wavelet processing the stacked image in PSS

Animation blinking between the original stacked image and the wavelet sharpened image.

The final processed lunar surface image

Processing H-alpha solar data.

The data were captured as two overlapping panels (RAW SERs) covering the whole solar disk using AstroDMx Capture and a Coronado Solarmax II, 60, BF15 H-alpha scope. Each panel SER was processed in PSS and then stitched together in MS Image Composite editor.

Screenshot showing the quality curve in relation to the % of frames selected for stacking

Screenshot showing frequency distribution local warp sizes of alignment points

PSS has gathered all the information it needs to stack the frames. First, at every AP it identifies the sharpest frames to be used for stacking. Since the seeing is a very local phenomenon, frame sets will be different for different APs. Then, for every AP and every contributing frame the local displacement relative to a reference frame is measured, and the shifted AP patch added to the stacking buffer. Clicking OK completes the process.

Screenshot showing Wavelet processing the stacked image in PSS

The stitched two panel stacked images

The image further processed in Gimp3 and colourised

There is not a huge learning curve for PSS, in fact, there is a totally automatic workflow where no user intervention is required. Our results indicated that PSS can do the jobs of software such as Autostakkert! and waveSharp, all in one program. We intend to study this software more closely, and will include PlanetarySystemStacker in our suite of regularly used image processing programs and in our workflows.

The Dwarf Mini smart telescope

The Dwarf Mini is a relative newcomer to the ranks of smart telescope. It’s immediate claim to fame is that is is the smallest and lightest, and possibly the lowest cost smart telescope on the market.

The only accessory it comes with is a solar filter. This is one of the reasons that it is such a low cost device and it is necessary to provide your own tripod. This is not such a bad thing because most people have a photographic tripod that can be used with the scope in AZ or EQ mode.

I purchased a very cheap tabletop v-logging tripod which proved to be very suitable and even came with a carry case.

The Dwarf Mini in equatorial mode

On a table in the observatory area to give it extra height

Like all smart scopes the Dwarf Mini is controlled by an app on a smartphone or tablet which can be either Android or iOS and displays in Landscape rather than Portrait.

The Dwarf app running on an iPad and displaying the accumulating live-stacked image

Over several nights we tested the scope on a number of objects with a variety of exposures. We found that even with 90s exposures, there were very few dropped frames. It can be seen in the top image of the Dwarf app showing the live-stacked image of C50 and the Rosette nebula with 90s exposures, 80 out of 84, 90s exposures had been stacked. With tracking this good, the appropriate exposure should be chosen for the specific object and which filter is to be used, the Duo-Band or the Astro filter. The Astro filter in the DWARF Mini is a general-purpose filter for reducing light pollution and enhancing stars, reflection nebulae, and galaxies. The Duo-band filter is a narrowband filter that selectively passes Hα (656.3 nm) and OIII (500.7 nm) wavelengths, making it ideal for high-contrast imaging of emission

Table of the imaging tests done to date

Results

Clicking on an image will get an even closer view

M3

M13

M100

Leo Triplet

Monkey-head nebula

Jellyfish nebula

Flaming Star nebula

Markarian's chain

M51

M37

C50-cluster Rosette nebula

M44

Further tests will be done with the Dwarf Mini smart scope but these initial tests have shown it to be a remarkable device with good optics and excellent tracking.