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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →Stacking several registered deep-sky exposures reduces random noise by combining measurements of the same sky. It cannot recover detail that was never recorded, and it cannot rescue badly calibrated or misaligned frames. The reliable path is to organize the data, calibrate where appropriate, register the light frames, reject or down-weight poor inputs, integrate them with a suitable method, and then stretch and correct the resulting linear image.
Siril’s documentation describes the idealized signal-to-noise improvement of average stacking with rejection as proportional to √N, where N is the number of images. That is a statistical rule, not a promise that every dataset will look √N better to your eyes.
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What stacking changes—and what it cannot change
Every exposure contains two broad kinds of information: scene signal that is repeated from frame to frame, and variation that changes between frames. In a deep-sky sequence, the nebula, galaxy or star field is the repeated signal. Read noise, shot noise, hot pixels, satellites, aircraft lights, wind shake and other defects vary in location or intensity. Once the frames are aligned, averaging lets the repeated signal reinforce itself while much of the random variation partially cancels.
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Siril’s stacking documentation summarizes the idealized result as “the increase in SNR is proportional to √N.” If four comparable images are combined, the theoretical signal-to-noise ratio is twice that of one image; sixteen comparable images would be four times the single-frame value. Those are mathematical expectations under similar conditions, not guaranteed visual gains. Correlated noise, gradients, tracking errors, changing sky brightness and imperfect calibration all reduce the benefit.
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Stacking also does not create a star, filament or color that was below the information captured by the camera. It gives later processing a cleaner measurement to work with. A faint structure may become visible after a careful histogram stretch or color calibration because the integrated data has a better noise floor, but enhancement is revealing retained signal, not manufacturing it.
Separate the workflow into decisions
A dependable deep-sky result comes from distinct stages. Treating “stacking” as one button hides problems that are easier to fix earlier.
- Organization: keep the original light frames and calibration frames identifiable and separate.
- Calibration: correct repeatable sensor and optical patterns with suitable bias, dark and flat data when your capture workflow supports them.
- Registration: align stars or other stable scene features and account for geometric differences between frames.
- Quality review: identify frames with poor focus, trails, cloud, excessive background, bad tracking or other defects.
- Integration: combine the registered frames with an estimator and any rejection, normalization or weighting that suits the sequence.
- Enhancement: work on the linear integrated image with color calibration, gradient treatment, stretching and other controlled operations.
An excellent integration setting cannot compensate for a light frame that was never aligned, a flat that does not match the optical train, or a sequence contaminated by passing cloud. Diagnose each stage separately.
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Organize lights and calibration files before opening the stacker
Keep the originals untouched
Make a working copy of the session and keep the camera files read-only or otherwise protected from accidental overwrites. Save calibrated, registered and integrated outputs in separate folders. This gives you a way back if a calibration master is wrong or an integration setting removes too much real signal.
Use clear groups
Siril’s tutorial organizes a session into folders for lights, biases, darks and flats. That arrangement is practical because it prevents a calibration frame from being treated as a light frame and makes it obvious which files belong to the same capture setup. You can use another folder layout, but label the camera, filter, binning, gain or ISO, temperature and session date when those differences affect compatibility.
Do not mix exposures that require different calibration unless you understand the consequences. A dark made at a different temperature or exposure can leave a pattern rather than remove one. Flats must match the optical configuration that produced the lights: camera orientation, filter, focus position and any accessories in the light path matter.
Decide what is actually available
Not every session contains every calibration type. Some cameras or workflows provide bias frames; others use dark-flat frames or a different offset strategy. A session may lack flats, as the Siril tutorial notes. That does not make the lights unusable, but it means uneven illumination and dust shadows may remain and need careful handling later. Do not promise that missing frames can always be recreated after the fact.
A flat-field panel can make capturing flats more convenient, but it is optional capture equipment. It is not a stacking requirement and it is not itself a noise-reduction treatment.
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Calibrate the light frames where it is appropriate
Calibration removes repeatable patterns before alignment and integration. A master bias can model the camera’s readout offset; a master dark can model thermal signal and hot pixels for a matching exposure and temperature; a master flat can model vignetting and dust shadows. The exact combination depends on the sensor and how the data was captured.
Build masters from compatible frames
Combine the calibration frames into masters using the same general principles as light integration: consistent capture conditions, inspection for outliers and a method that does not let one defective frame dominate. Keep notes about exposure, temperature and gain or ISO. A master made for one camera setting should not be assumed valid for another.
Apply calibration before registration
Calibrating first gives the registration step cleaner stars and backgrounds to analyze. If a hot pixel or dust donut is mistaken for a star, alignment can fail or choose the wrong correspondence. In Siril’s documented workflow, calibration produces corrected light files that proceed to registration and then to sequence processing.
Check the result instead of trusting the process
Open several calibrated frames at the same display stretch and inspect the corners, background and bright stars. Look for residual dust rings, over-correction, clipped areas or new gradients. A calibration error repeated across every frame will survive stacking and can become harder to remove later.
Register the lights so the same stars occupy the same pixels
Registration estimates how each exposure must be shifted, rotated or otherwise transformed so corresponding stars line up. Siril’s workflow also corrects distortion and creates aligned image files for the next sequence stage. Other applications use different labels and algorithms, but the purpose is the same.
- Use the calibrated light sequence as the registration input.
- Choose star detection or feature settings that can find enough reliable stars without selecting hot pixels or noise.
- Run registration and review the generated aligned files or quality preview.
- Inspect the edges and several bright and faint stars at high magnification. Confirm that stars are compact rather than doubled or stretched.
- Remove or set aside frames that fail registration instead of forcing them into the integration.
Faint fields, heavy nebulosity, changing framing and large rotations can challenge star matching. A sequence can also contain individually sharp frames that do not share enough overlap with the reference. If alignment looks wrong, revisit calibration, detection settings and frame selection before changing the integration algorithm.
Review quality and decide whether to weight frames
Registration is not a quality guarantee. Examine the sequence for clouds, poor focus, trailing, guiding jumps, satellite streaks, aircraft lights, severe gradients and unusual background levels. A single bad frame can add more visible damage than its nominal exposure time is worth.
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Siril’s tutorial quality graph includes FWHM, roundness, weighted FWHM and background value. FWHM can indicate star size and therefore focus or seeing; roundness can reveal tracking or optical problems; background value can expose clouds or light-pollution changes. Weighted FWHM combines star size with another quality consideration, but it should still be checked against the image itself.
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Use a metric to find candidates for inspection, then view the frame. A low background does not prove that a frame has good transparency, and a high star count does not prove that stars are sharp. Different defects can produce similar scores.
Weighting options
Weighting gives selected frames more influence than others instead of treating every input identically. Siril lists star count, weighted FWHM, measured noise and integration time as possible weighting bases. None is universally best:
- Star count can favor frames with many detected stars, but detection depends on focus, sky brightness and the chosen threshold.
- Weighted FWHM can favor sharper stars while accounting for the distribution of star sizes.
- Measured noise can reduce the influence of noisier frames, although a low-noise frame with a cloud or gradient is not automatically useful.
- Integration time can reflect how much exposure a frame represents, but a longer exposure is not inherently cleaner if tracking or transparency deteriorated.
For a stable sequence, unweighted integration is a useful baseline. Compare it with a weighted result while watching faint structure, star shape and background texture. Keep the version that preserves the information you value, not simply the one with the most aggressive numerical ranking.
Choose an integration method for deep-sky data
| Method | Documented behavior | Deep-sky implication |
|---|---|---|
| Average with rejection | Computes an average after excluding detected deviant pixel values; normalization can be used when frame levels differ. | Usually the relevant method to evaluate for a registered deep-sky sequence because it can reduce transient defects while retaining the average signal. |
| Median | Uses a different statistical estimator from an average with rejection. | Can behave differently with small stacks or weak signal. The available evidence does not support a universal frame-count threshold or a claim that it always wins. |
| Sum | In Siril’s documentation, lacks normalization and rejection and is recommended there only for planetary processing. | Do not use plain summing as the default deep-sky choice in that software. |
Average with rejection
Rejection compares pixel values across aligned frames and excludes values that look like outliers before calculating the average. This can suppress hot pixels, cosmic-ray-like events, satellite trails and other transient values, provided the algorithm can distinguish them from real signal. It is not a substitute for removing a frame with widespread cloud or severe trailing.
Normalization
Normalization scales or otherwise brings frames toward a common level before rejection or combination. Siril describes it as useful when level differences would make rejection less meaningful, such as sequences in which background brightness changes. Whether to normalize, and which mode to select, depends on the sequence and the implementation. Inspect the background and the rejection behavior rather than enabling it by habit.
Mean versus median
A median chooses the middle value at each pixel, while an average with rejection estimates a mean after discarding detected outliers. With only a few frames, a median can discard a real but weak signal contribution more readily; with many frames and strong outliers, either approach may be useful. The correct choice depends on frame count, defect pattern, signal strength and the implementation’s rejection behavior. Evaluate both only when your software exposes them and the data justify the comparison.
A Siril-centered sequence you can follow
Siril’s official documentation labels version 1.4.4, released June 17, 2026. Interface names can change, so use this as a process map rather than assuming that every application has identical menus or thresholds.
- Create a working session: place the lights and any available bias, dark and flat files in clearly identified folders. Preserve the camera originals separately.
- Make calibration masters: combine compatible calibration frames, then apply the masters to the light sequence. If your session has no flats or another calibration type, record that limitation instead of substituting an incompatible master.
- Register the corrected lights: run star-based alignment and distortion correction. Review the aligned outputs and set aside frames that fail or show doubled stars.
- Inspect sequence quality: use the quality graph and the image previews to identify poor FWHM, bad roundness, unusual background or other defects. Exclude clearly compromised frames.
- Select integration settings: for deep-sky work, start by evaluating average integration with an appropriate rejection method. Consider normalization if frame levels vary. Choose weighting only after deciding which quality measure reflects your sequence.
- Run the integration: save the integrated result as a new file so the registered inputs remain available for another pass.
- Inspect the rejection result: check the rejection rate and inspect any rejection maps or previews provided by the software. In the Siril tutorial example, the suggested range is 0.1–0.5%; that range is specific to the example and is not a universal quality rule.
- Save a linear master: keep an unstretched copy before applying strong enhancement. This is the reference for trying different stretches or color treatments later.
If the rejection rate is aberrant in the Siril tutorial’s sense, it advises changing the low and high sigma settings. Increasing a sigma factor lowers the rejection rate. Make small changes and inspect whether real star or nebula pixels are being rejected; a lower percentage is not automatically better.
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Inspect the linear integration before stretching
The integrated file may look nearly black or gray when opened with an ordinary display transform. That appearance is expected for linear astronomical data. First check for technical problems:
- Are stars round and aligned across the field?
- Are there bright residual columns, hot pixels, dust rings or dark calibration holes?
- Does the background show a severe gradient or abrupt seam?
- Are highlights clipped, or are faint structures absent even after a gentle temporary stretch?
- Do rejected-pixel artifacts appear around moving objects or bright stars?
Keep the linear master unchanged and make processing copies. This lets you compare a conservative stretch with a more aggressive version without losing the original integration.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Reveal faint detail with post-stack processing
Stacking lowers the noise floor; it does not finish the image. Siril’s official site lists color calibration, deconvolution and histogram stretching among post-stack enhancement operations. Gradient correction, background modeling and selective noise reduction may also be needed, depending on the data.
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Color calibration attempts to place the channels in a meaningful balance. A light-pollution gradient or uneven background can bias that calibration, so inspect and correct large-scale gradients before making fine color decisions. Avoid stretching a severe gradient until it dominates the frame.
Stretch in controlled passes
Use several modest histogram or curve adjustments rather than one extreme move. After each pass, inspect the background, star cores and faint structures at both fit-to-screen and high magnification. An overly strong stretch can make noise look like nebulosity and can clip bright stars before you notice.
Sharpen only where the data support it
Deconvolution or sharpening can improve perceived star and nebula structure when focus, sampling and registration are sound. It cannot restore detail erased by motion blur or poor seeing. Apply it selectively and compare against the unstretched or lightly processed master.
Common failure modes and fixes
The stack is still noisy
Check whether the frames are truly aligned, whether calibration introduced a pattern, and whether the sequence contains enough comparable exposures. The √N relationship gives diminishing returns: adding the same number of frames produces a smaller relative improvement as the stack grows. More frames with clouds, changing gradients or inconsistent focus may worsen the result.
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Stars are doubled or elongated
Do not try to hide this with rejection settings. Re-run registration with suitable star detection, verify that the frames overlap, and exclude frames with tracking jumps or failed solutions. Inspect the aligned files before integrating again.
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Dark rings or uneven corners remain
Review flat compatibility and calibration order. A flat captured with a different filter, focus position, camera rotation or optical accessory can create errors. If flats were never captured, acknowledge that limitation and handle the gradient carefully during later processing rather than inventing a universal correction.
Real nebula disappears after rejection
Over-aggressive sigma settings can classify legitimate signal as an outlier, especially in a small stack or in areas where a faint feature covers only part of the frame. Increase the sigma factor, reduce other rejection aggressiveness, compare with an unweighted or less-rejected integration, and inspect rejection maps. The goal is to remove transient defects without removing consistent sky signal.
One frame dominates the background
Look for changing sky brightness, moonlight, haze or gradients. Normalization may make rejection more meaningful when frame levels differ, as described in Siril’s documentation. Also test whether excluding the compromised frame produces a cleaner, more consistent master.
The image looks blank after stacking
Do not judge a linear master with an unmodified display. Apply a temporary, gentle stretch or adjust the display transfer function, then inspect the histogram. If no structure appears, verify that the correct sequence was integrated and that calibration or registration did not create a blank or clipped output.
How many images should you stack?
There is no universal exposure count supplied by the workflow documentation, because the useful number depends on sky brightness, camera noise, exposure length, tracking, target surface brightness and the quality of the frames. Use the √N rule to set expectations rather than to chase a fixed number. Doubling the number of comparable frames improves idealized SNR by a factor of √2, not by a factor of two.
More integration time is valuable when the additional frames are consistent and add uncontaminated signal. A smaller set of sharp, well-calibrated exposures can outperform a larger set containing clouds, poor focus or tracking errors. Review the sequence continuously so that you can stop adding frames when quality declines.
A practical decision checklist
- Original lights and calibration files are preserved.
- Frames are grouped by compatible camera and capture settings.
- Available bias, dark and flat data have been checked for compatibility.
- Calibration artifacts are not visible in sample corrected frames.
- Registration produces compact, coincident stars across the field.
- Poor focus, trails, cloud and severe gradients have been excluded or flagged.
- Weighting is based on a metric that matches the defects you actually see.
- Average integration with rejection and, where justified, normalization has been evaluated for the deep-sky sequence.
- Plain sum has not been used as the default deep-sky method in Siril.
- Rejection settings have been checked against the image and rejection output, not just a target percentage.
- A linear master is saved before color calibration, gradient work or stretching.
- Final faint-detail judgments are made after an appropriate display stretch, not from the untouched linear preview.
Bottom line
Stacking works when the same sky signal is aligned across clean, compatible exposures and random variation is allowed to average down. The most dependable deep-sky workflow is therefore not “choose a stack button,” but calibrate where appropriate, register carefully, inspect and select the frames, integrate with rejection and sensible normalization or weighting, then stretch and color-correct the linear result. Treat the √N improvement as an ideal statistical guide, and let the quality of your data—not a universal frame count or rejection percentage—decide how far the final image can go.
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