Sunday, 24 August 2025

More lesser known nebulae

Provocatio est invenire obiecta caelestia innominata.

 Nebula in Cygnus in the region of the star HIP100144 

1 hour 30 minutes worth of 5 min exposures captured of an un-named nebula in the region of the star HIP100144 in Cygnus by AstroDMx Capture through an Altair 60mm ED refractor with an 0.8 flattener/reducer and an Altair Ha/OIII dualband filter. Stacked and partly processed in PixInsight , further processed in GraXpert, SetiAstroSuite and Gimp.

    


Nebula in Lacerta in the region of the star HD213976

1 hour 30 minutes worth of 5 min exposures captured of an un-named nebula in the region of the star HD213976 in Lacerta by AstroDMx Capture through an Altair 60mm ED refractor with an 0.8 flattener/reducer and an Altair Ha/OIII dualband filter. Stacked and partly processed in PixInsight , further processed in GraXpert, SetiAstroSuite, Gimp, G'MIC and Starnet++


Nebula in Cygnus in the region of the star HD203663 SH2-114, LBN-347 The Flying dragon nebula.

1 hour 30 minutes worth of 5 min exposures captured of an un-named nebula in the region of the star HD203663 in Cygnus by AstroDMx Capture through an Altair 60mm ED refractor with an 0.8 flattener/reducer and an Altair Ha/OIII dualband filter. Stacked and partly processed in PixInsight , further processed in GraXpert and Gimp.


Nebula in Scutum in the region of the star HD168112

1 hour 30 minutes worth of 5 min exposures captured of an un-named nebula in the region of the star HD168112 in Scutum by AstroDMx Capture through an Altair 60mm ED refractor with an 0.8 flattener/reducer and an Altair Ha/OIII dualband filter. Stacked and partly processed in PixInsight , further processed in GraXpert, SetiAstroSuite, Gimp, G'MIC and Starnet++


The lesser known nebulae are varied and beautiful objects  that make a diverting challenge while Nicola continues with the porting of AstroDMx Capture over to Qt6 along with the concomitant refactoring.

Monday, 18 August 2025

Imaging some lesser-known objects in Cygnus.

Pulchritudo inveniri potest in rebus ignotis.

The Constellation of Cygnus is replete with deep sky objects and the brighter, iconic objects such as the North America nebula, the Pelican nebula, the Crescent nebula and the Veil nebulae are frequent targets for astrophotographers. 

There are however, many lesser known deep sky objects in Cygnus. More than 400 of the Cygnus objects are catalogued in the Lynds' Catalogue of Bright Nebulae, which covers far more than just Cygnus. The catalogue was compiled in the 1960s by the late Beverly Turner Lynds, an American astronomer. The well-known objects are contained in the catalogue, but so are a large number of lesser known objects. In Cygnus there are also nebulae which seem to be nameless, but which are easy to find.

In this imaging session we imaged two nebulae, one from the Lynds'  Catalogue of Bright Nebulae and the other, which appears to be nameless.

The equipment used


AstroDMx Capture was used with an SV605CC OSC colour CMOS camera and an Altair Starwave ASCENT 60ED doublet refractor with 0.8 reducer/flattener and a Pegasus Focuscube v2. An Altair 2” magnetic filter holder version 2 containing an Altair 6nm Ha/OIII dualband filter was placed in the optical train.

The equipment was mounted on a Celestron AVX GOTO mount. An SVBONY SV165 guide-scope fitted with a QHY-5II-M guide camera was mounted on the imaging scope. An INDI server was running on the imaging Linux computer indoors. The guide camera was connected by USB to another Linux computer indoors running PHD2 autoguiding software via the INDI server. Both the imaging scope and the guide scope were fitted with dew heater strips set on the low setting because of the summer nights. The mount and the focuser were controlled by AstroDMx Capture via the INDI server.

AstroDMx Capture slewed the scope to the star Altair and plate-solved to centre it. A Bahtinov mask was used to enable Altair to be brought into sharp focus.

Altair being focused using a Bahtinov mask


Then AstroDMx Capture plate-solved and sent the scope to LBN 182, a region of nebulosity in the vicinity of 27 Cyg, 28 Cyg and 29 Cyg

AstroDMx Capture was used to capture 3 hours 25 minutes worth of 5-minute RAW exposures of LBN 182 with an assisted meridian flip after 1 hour of capturing.

Screenshot of AstroDMx Capture saving RAW FITS files. Live stacking was used to improve the preview of this faint object

A negative preview helping to visualise the nebula



A normal preview


The data were Stacked and partly processed in PixInsight, further processed in GraXpert, Gimp, G'MIC and Starnet++

LBN 182


Annotated image



Then AstroDMx Capture plate-solved and slewed the mount/scope to 68 Cyg which is surrounded by un-named nebulosity and is located a little to the east of NGC7000. 1.5 hours worth of 5-minute RAW exposures were captured by AstroDMx Capture through the same Altair 60mm ED refractor with an 0.8 flattener/reducer and an Altair Ha/OIII dualband filter. 

Screenshot of AstroDMx Capture saving RAW FITS files. Live stacking was used to improve the preview of this faint object

A negative preview helping to visualise the nebula


A normal preview



The data were Stacked and partly processed in PixInsight, further processed in GraXpert, Gimp, G'MIC and Starnet++

68 Cyg nebulosity Containing the dark nebulae LDN 947, 948, 951, 952, 953; Lynds'  Dark Nebulae.

The dark nebula can be seen in the left hand part of the image

Annotated image


The lesser known nebulae in Cygnus and elsewhere will be an interesting challenge for AstroDMx Capture whilst the onerous task of migrating AstroDMx Capture to Qt6 to enable Wayland compatibility along with considerable code refactoring, increasing efficiency that is being done by Nicola.

Monday, 11 August 2025

The use of AI (Artificial Intelligence) as part of the workflow in Astronomical image processing.

 Intelligentia artificiali tantum utere si exemplar in datis astronomicis eruditum est.


The use of AI (Artificial Intelligence) as part of the workflow in Astronomical image processing.


It has become increasingly common for astronomers to use Artificial Intelligence systems to aid in the processing of their image data. This is done, often with  little thought to whether the results, however pleasing may be less than authentic in the final analysis.

There are AI systems in many modern image editing programs which often form the main basis of the advertising of the software. This is frequently because the programs have the ability to produce generatively, absolutely fake content to augment the original image. Whilst there may be some artistic merit in this, this kind of capability has no place in the processing of astronomical images. These programs have AI models that are trained on vast amounts of general photographic images rather than specifically on astronomical data. In my opinion such software should be avoided when using AI, and should be used only for general, traditional, mathematical processes that can be used for processes such as adjusting levels, curves, saturation, sharpness etc. Even then, as any experienced astronomer knows, even these processes should be used judiciously to avoid the introduction of artefacts into the image.

AI models used for in astronomical image processing

There are five areas in which AI models are used.

  • Star removal.
  • Background gradient removal.
  • Noise reduction.
  • Deconvolution... sharpening of stellar and non-stellar components in an image.
  • Re-scaling.

All of these processes have traditional counterparts that do not involve AI and these mathematical processes should not be forgotten in one’s enthusiasm to get on board with AI!

An example is G’MIC Qt which can variously be run as a stand alone application or as a plugin for GIMP and other programs. This software contains a huge selection of procedures for the mathematical approach to different aspects of image processing. In this software it is possible to appreciate the different results produced by different mathematical ways of revealing details in any given image. This fact alone should caution a user against the over zealous use of any given procedure.

The programs with which G’MIC Qt can be used as a plugin are:

Adobe Photoshop

GIMP 2 and 3

Affinity Photo

PaintShop Pro 

PhotoLine

XnView 

IrfanView 

And other 8bf-compatible hosts. An 8bf-compatible host is any graphics application that implements Adobe’s Photoshop filter API and can load filter plugins with the  extension. When you drop a  file into the host’s plugin folder, the application recognizes it as an additional filter option in its menu system.

Similarly, a user must guard against the over zealous use of AI procedures that they may use in their astronomical image processing. Many of these AI procedures have sliders that allow the user to determine the aggressiveness of the application of the AI technique to their data. There can be undesirable consequences from over aggressive application of an AI process.

The inherent dangers of using AI tools in astronomical image processing

Distortion of Data Integrity

Artificial intelligence tools often apply learned patterns to enhance images, but in doing so they can inadvertently change or erase genuine celestial features. Concerns have been raised that AI-driven star removal or deconvolution may misclassify faint nebular structures as artefacts and strip them away, compromising the integrity of the data

Introduction of Hallucinations and Artefacts

Because AI models “hallucinate” details to satisfy their training objectives, they can introduce spurious structures such as faint filaments, false stars, etc that never existed in the raw exposures. These artificial patterns risk being accepted as real structure and they compromise the data

Loss of Genuine Faint Signals

Advanced noise-reduction networks aim to distinguish signal from background noise, but overzealous filtering can remove legitimate low-contrast features. Small and very faint objects might be mistaken for noise and eliminated or simply vanish under aggressive smoothing.

Training Data Bias and Coverage Gaps

AI models rely on the datasets they’re trained on. For example if those datasets emphasize only bright objects, the models may under-perform or misbehave when confronted with a very dim object: Such bias can blind AI pipelines to anomalies that don’t fit the “expected” patterns. The big worry here is whether AI model training that has been done by individuals rather than huge organisations may have used training data that are not comprehensive enough to handle all contingencies encountered in an image

Overall, it is best to carefully use a combination of conventional and AI procedures for astronomical image processing, with constant reference to your original stacked image. Above all, one should not have blind faith in the results produced by Artificial Intelligence even if, in the end, they turn out to be acceptable.

As Darwin's 'bulldog', Thomas Huxley, the English biologist and anthropologist once said: “Skepticism is the highest duty and blind faith the one unpardonable sin.” 

Using AI for Deconvolution

We shall look here at the results of three Deconvolution programs that use AI, but will concentrate on a new one called Theia, and compare its results with the other two, which are Seti Astro’s Cosmic Clarity and The PixInsight Process Blur Xterminator by R C Astro (Russel Croman Astrophotography).

Deconvolution is used to sharpen images, reducing blur and improving image clarity. It is the computational process of reversing the blurring effects introduced by a telescope’s optics and the atmosphere. It aims to recover the original image from a blurred or distorted version that has been convolved with a point spread function (PSF) which describes the blur, and corrupted by noise.

Estimating the PSF means reconstructing how a telescope and atmosphere smear a point source, the PSF. Some software uses adaptive PSF. Instead of assuming one static PSF, an adaptive model is built that adjusts to each region of the image. Stars are sampled at various locations to get PSF snapshots at multiple positions. Parameters are interpolated (size, ellipticity, halo fraction) between those samples. The locally tailored PSF is fed into the deconvolution routines.

The data we are going to use here are some unremarkable data on M17 captured with a Seestar S50. 458 x 10s exposures captured in EQ mode. The data were stacked in Pixinsight, Spectrophotometrically corrected, Automatic Dynamic background corrected and stretched in GIMP.

The original image untouched by AI


The image was then NoiseXterminator (AI) denoised and saved as a 32bit Fits file. The image was stretched in Gimp.

The Image at this stage


This linear 32 bit Fits file was deconvoluted in PixInsight with the AI BlurXterminator and finished in Gimp.

The BlurXterminator image


The 32 bit linear Fits file was deconvoluted in Seti Astro’s Cosmic Clarity in the SetiAstroSuite and finished in GIMP

The Cosmic Clarity image


Finally, the The 32 bit linear Fits file was deconvoluted in Theia

We determined that Theia, which is a Windows program would run perfectly in Wine with no delaying overheads. Theia is a standalone program, paid for but low cost. The GUI renders slightly different in Windows and in Fedora Linux.

Theia Windows version GUI


The Theia Linux (Wine) version GUI


The Theia Image


It will be noticed that each of the AI deconvoluted images of M17 is slightly different, and as Hamlet said ‘and therein lies the rub’. The soliloquy might have been ‘To AI or not to AI, that is the question”. For which, if any, of these AI renderings is correct? and are they more or less correct than say a conventional Richardson-Lucy deconvolution or a wavelet sharpening? Indeed, a Richardson-Lucy conventional deconvolution yields yet another variation on the theme:

Richardson-Lucy image


If we merge all three AI images with equal weight into an average AI image we get:

Average AI image


And, if we merge all three AI images and the Richardson-Lucy image with equal weight we get:

Three AIs plus Richardson-Lucy average image


Which image should we use?

By careful comparison with the original image, (untouched by AI or any other process that might make structural changes) non of the resulting images seem to contain any hallucinations in their structure, so the decision becomes ultimately an aesthetic one.

Maybe the fairest approach is to combine the results from the most pleasing deconvolutions having satisfied ourselves that nothing deleterious has happened to the image during the processing, AI or not.

As a parting comment I will mention a bizarre example of an AI hallucination that occurred when I was researching the Foraax palette in 2024. I asked a large language model AI (which shall remain nameless) about the origin of the name Foraax. It came up with a completely believable explanation. It said that Foraax was derived from a combination of the first names of the palette’s developers: Forrest Tanaka and Axel Mellinger both of whom are real amateur astronomers. However, I wrote to Axel Mellinger to ask him to confirm what the AI had said. Here is a slightly redacted copy of his reply:

Stephen,

while I do pursue astrophotography and have been doing a fair amount of

narrow-band imaging ***** I have no connection

with any Foraax palette. AI must have gotten it wrong this time!

*****

To the best of my knowledge, the ForaxX palette (note the different

spelling) was invented by someone with the Discord name "ForaxX":

*****

It's actually been on my to-do list to try out for a while.

Best regards,

Axel

The AI had skilfully constructed an answer from the names of two real astronomers in its AI hallucination. When I questioned it about its answer later on, it denied all knowledge of its answer and wasn’t able to help. Incidentally, there have been considerable improvements in large language models since this incident, and when asked the same question today, it produced a very detailed and correct answer.

The take home message is that if an AI had been working on an astronomical image, it could have hallucinated details that were not part of the original image data. Moreover, these details could have seemed completely authentic. Caveat emptor!



Wednesday, 6 August 2025

Imaging SH2-91, filamentous nebulosity in Cygnus

Oportet ut imago rēs vērā repraesentet

AstroDMx Capture was used with an SV605CC OSC camera and an Altair Starwave ASCENT 60ED doublet refractor with 0.8 reducer/flattener and a Pegasus Focuscube v2. An Altair 2” magnetic filter holder version 2 containing an Altair 6nm Ha/OIII dualband filter was placed in the optical train.

The equipment used

The equipment was mounted on a Celestron AVX GOTO mount. An SVBONY SV165 guide-scope fitted with a QHY-5II-M guide camera was mounted on the imaging scope. An INDI server was running on the imaging Linux computer indoors. The guide camera was connected by USB to another Linux computer indoors running PHD2 autoguiding software via the INDI serever. Both the imaging scope and the guide scope were fitted with dew heater strips. The mount and the focuser were controlled by AstroDMx Capture via the INDI server.

Screenshot of the PHD2 autoguiding


AstroDMx Capture slewed the scope to Altair and plate solved to centre it. A Bahtinov mask was used to enable Altair to be brought into sharp focus.

Then AstroDMx Capture plate solved and sent the scope to SH2-91, a region of filamentous nebulosity in the vecinity of Albireo; a region containing 9 Cyg, 12 Cyg and the very red Campbell's Hydrogen star.

AstroDMx Capture was used to capture 3.5 hours worth of 5-minute RAW exposures of SH2-91 with an assisted meridian flip after 1 hour of capturing.

Screenshot of AstroDMx Capture saving RAW FITS files. Live stacking was used to improve the preview of this faint object



A negative preview helped show the live stacked view of SH2-91


The data were calibrated and part processed in PixInsight and further processed in GraXpert and Gimp.

SH2-91


Annotated image


Re-rendering of the SH2-91 image using the suggestions in "Dynamic narrowband combinations with PixelMath' June 9, 2020, THE COLDEST NIGHTS, for Dualband Ha/OIII data. The Pixelmath was done in Siril.


New, more challenging targets are being explored whilst Nicola is refactoring and re-writing the code-base of AstroDMx Capture, migrating from wxWidgets to Qt 6 which will allow the software to function properly in a Wayland desktop environment. It is a huge job and has delayed the introduction of new features in the road-map for the software's development. However, this is absolutely essential in view of the fact that many Linux distributions are dropping support for X11 during 2026.