siril.org

What is Siril?

Siril is a free, open-source astronomical image processing tool for turning raw telescope data into finished deep-sky images. It covers the full workflow: calibration (removing bias, dark and flat patterns), stacking many exposures to boost signal-to-noise, and enhancement such as colour calibration, deconvolution and histogram stretching. It also includes science-oriented tools for analysing star brightness variation and astrometry.

Siril

Who it suits

• Deep-sky imagers who want a scripted, repeatable pipeline rather than manual editing in a general-purpose photo editor.
• Lucky-imaging users working with SER videos or FITS cubes, since Siril processes these formats natively.
• Observers doing variable-star or astrometry work who need measurements, not just pretty pictures.

H3. How it fits a typical session You shoot a night of sub-exposures plus calibration frames, load them into Siril, and let a script handle calibration and registration before stacking. From there you can stretch and colour-correct the result, or branch into analysis tools. The trade-off is a learning curve and a processing-style interface: it rewards users who want control over each step, while those wanting one-click results may prefer a more automated alternative.

A practical next step: download a version for your platform, run one of the built-in scripts on a small existing dataset, then compare the stacked output with your current workflow before committing a full night's data to it.

How do I install Siril on my operating system?

Download Siril from its official site and choose the package that matches your OS: Siril. The page lists installers for Linux, Windows, and macOS, plus source code if you prefer to build it yourself.

H3 Platforms and package choices

• Linux: AppImage (portable, no installation), Flathub Flatpak (sandboxed, updates via your software center), or build from source.
• Windows: 64-bit UCRT64 installer or Portable UCRT64. The older MINGW64 builds are marked deprecated on the site.
• macOS: Separate downloads for Intel and Apple Silicon.

H3 Practical steps

1. Pick your OS and architecture (Windows 64-bit, macOS Intel vs. Apple Silicon, Linux AppImage vs. Flatpak).
2. Download the file from the official site, not a mirror.
3. Run the installer or make the AppImage executable and launch it.
4. Open Siril and check the version number in the interface or Help/About to confirm what you installed.

H3 Which format to choose

H3 A concrete example

If you're on Windows 11 with a modern CPU, download the 64-bit UCRT64 installer, run it, and accept the default install location. If you later want to test a different version without touching that install, grab the Portable UCRT64 build and run it from a folder.

Next step: after installing, open Siril once and note the version shown, then check the release notes on the site to see whether any known issues affect your platform before starting a processing session.

Can Siril process deep-sky lucky imaging videos?

Yes. Siril's page explicitly states that it can natively process SER videos and FITS cubes, and that its algorithms use all CPU cores, so it is positioned for deep-sky lucky imaging work where you capture thousands of short exposures.

What that means in practice

Lucky imaging with deep-sky targets usually means recording a video or image sequence, then keeping only the sharpest frames before aligning and stacking them. Siril's stated workflow covers that sequence:

• Preprocessing/calibration — building and applying master bias, dark and flat frames to remove unwanted signal and pattern.
• Registration — alignment with different methods: all stars, a single star, or the nebulae body. The page notes this flexibility makes alignment nearly never fail even when stars are faint.
• Stacking — combining registered frames to improve signal-to-noise, with outlier rejection to cut noise further.
• Enhancement — colour calibration, deconvolution, histogram stretching and similar steps on the stacked result.

Trade-offs to weigh

The claim of handling more than 60,000 frames "with no effort" is the vendor's own framing, not an independent measurement. In reality, throughput depends on your CPU core count, disk speed, frame size, and how many frames you keep after quality filtering. A realistic test is to run one short SER clip end to end — calibrate, register, stack — and time it on your own machine before committing to a long capture session.

One practical detail from the page: using SER or FITS-cube input means only one extra file is created per preprocessing step, and stacking methods run in parallel. That keeps intermediate storage manageable, which matters when a single lucky-imaging run can produce tens of thousands of frames.

A sensible next step

Download the current release for your platform, then try a small deep-sky SER capture first. Compare registration modes (all stars vs. nebulae body) on the same clip to see which gives you a cleaner stack, and only then scale up to longer sessions. If you want to compare tools, Astropy is a Python library rather than a full lucky-imaging GUI, so it suits scripting and analysis more than a click-through preprocessing pipeline.

What algorithms does Siril provide for image calibration and stacking?

Siril's calibration and stacking tools are built around standard astrophotography preprocessing, combining dedicated algorithms with scripted or manual control.

Calibration

Calibration removes unwanted signal and pattern noise from RAW frames before stacking. Siril handles master bias, dark, and flat files, applying them to light frames so that sensor artifacts, vignetting, and dust shadows are corrected first.

Stacking

Stacking combines many registered individual frames to improve the signal-to-noise ratio. Siril's registration can be adapted to the target — aligning on all stars, a single star, or the nebulae body — which helps alignment succeed even with faint stars. Outlier rejection during stacking further reduces noise. The engine is multi-core and parallelized, and because Siril processes SER videos and FITS cubes natively, it suits deep-sky lucky imaging with very large frame counts.

Practical fit

If you shoot deep-sky lucky imaging with thousands of short SER frames, Siril's native video handling and parallel stacking are the main draw. For standard DSLR or cooled-camera sessions, the bias/dark/flat calibration path covers the usual preprocessing chain. Scripts automate the sequence; manual mode keeps each step under your control.

A useful next step: try one of the bundled scripts on a small dataset first, then compare a manual run so you can see exactly which calibration and rejection choices affect your result. For broader context on compatible workflows, see Siril and PixInsight.

How does Siril compare to other astrophotography software like PixInsight or DeepSkyStacker?

Siril sits between DeepSkyStacker and PixInsight: it is a free, open-source astro image processor that handles the full workflow — calibration, registration, stacking, and post-processing — rather than only one stage. That makes it the natural upgrade for someone who has outgrown DeepSkyStacker but does not want PixInsight's cost or learning curve.

Where each tool fits

What Siril actually offers

The site describes the workflow in stages. Calibration removes unwanted signal and pattern from RAW frames and handles bias, dark and flat master files. Stacking combines many registered frames to raise signal-to-noise, with outlier rejection for extra noise reduction. Enhancement covers colour calibration, deconvolution and histogram stretching on the stacked result. There are also science tools for star-brightness analysis and astrometry.

Two practical advantages stand out from the page evidence. Siril processes SER videos natively, which suits deep-sky lucky imaging, and its algorithms use all CPU cores — the site claims over 60,000 frames can be processed without strain. Registration can be set to all stars, a single star, or the nebula body, which helps alignment succeed on faint-star fields. Automation is available through dedicated scripts, or you can run each step manually.

How to choose

• You only need to stack and prefer minimal input: DeepSkyStacker is still the shortest path.
• You want one free program for the whole chain, including video-based lucky imaging and scripting: Siril is the strongest fit.
• You need the deepest possible processing control and don't mind paying and climbing a learning curve: PixInsight.

A reasonable next step is to try Siril on a dataset you have already stacked elsewhere and compare the final image. If the enhancement stage gives you what you want, you can drop the extra program entirely; if you hit its limits on noise or gradient handling, that is your signal to consider PixInsight. Related free options worth knowing include Astropy for the scientific analysis side and GNU-style open-source tooling generally, though neither replaces an imaging workflow.

One caveat: the version listed on the page is recent, and the notes mention known issues with that release and deprecated Windows builds, so check the bug tracker before committing to a specific installer.

What scientific analysis features does Siril offer for astronomical images?

Siril's scientific analysis features center on measuring what is in an image rather than only making it look better. According to the site, it provides tools to analyze star brightness variation and an astrometry interaction that returns information about objects in the frame. In practice, that means you can go from a processed image to a light curve for a variable star, or from an unknown field to a plate-solved position and object identification, without leaving the same program you used for calibration and stacking.

Siril

What this looks like in a real workflow

A useful scenario is a small observatory or a patient amateur tracking a variable star or an exoplanet transit. You calibrate with bias, dark and flat masters, stack the frames to improve signal-to-noise, then use the science tools to measure how a star's brightness changes across the sequence. The astrometry side helps when you need to confirm which object you captured or match your field against a catalog.

How to decide whether it fits

• If your goal is measurement — brightness variation, object identification, astrometric position — these features are the reason to choose Siril over a purely cosmetic editor.
• If your goal is only a pleasing final picture, the calibration, stacking and enhancement stages will matter more to you than the science tools.
• If you already use another astrometry service, compare how much of the pipeline you can keep in one place; Siril's advantage is doing preprocessing and analysis together.

A practical first step: take one short sequence of a known variable or a well-identified field, run it through calibration and stacking, then try the brightness-variation and astrometry tools on the result. That tells you quickly whether the analysis output matches your expectations before you commit a full observing run to it.