For astrophotographers, capturing the finest details in distant stars, nebulae, and galaxies is the ultimate goal. But one key metric underlies achieving sharpness and clarity: FWHM (Full Width at Half Maximum) and HFR (Falf-Flux Radius). If you’ve come across FWHM or HFR in software like NINA (Nighttime Imaging ‘N’ Astronomy) or WBPP (Weighted Batch Preprocessing) in PixInsight, or if you’re simply looking to improve focus in your images, understanding FWHM is essential. In this guide, we’ll dive into the technical details of FWHM, its role in focusing, how it impacts data integration, and why mastering this measurement can elevate your astrophotography.
What is FWHM (Full Width at Half Maximum)?
FWHM, or Full Width at Half Maximum, is a scientific measurement used to quantify the sharpness or “spread” of stars in an image. In practical terms, FWHM measures the width of a star’s image profile at half of its peak brightness. The narrower this width, the sharper the star, which means less atmospheric distortion, better focus, and ultimately, clearer images.
In astrophotography, FWHM is crucial for understanding how “tight” or “spread out” stars appear in an image. Lower FWHM values indicate smaller, sharper stars, while higher values suggest blur, either from focus issues, atmospheric conditions, or optical aberrations.
What is HFR (Half-Flux Radius)?
HFR, on the other hand, measures the radius containing half of a star’s total light. This approach evaluates how tightly a star’s light is concentrated, which can be advantageous for focus routines as it’s less sensitive to the peak brightness of a star and better suited to detecting minute focus changes across the frame.
Unlike FWHM, which depends on the star’s peak intensity, HFR considers the distribution of light across the star’s area. This stability allows NINA’s autofocus routines, like those with the Hocus Focus plugin, to consistently achieve precise focus across varying star fields and conditions, making it particularly effective for multi-star focusing.
Why is HFR and FWHM important?
In astrophotography, achieving sharp, detailed images requires careful attention to star sharpness and focus quality, two areas where HFR (Half-Flux Radius) and FWHM (Full Width at Half Maximum) are crucial. Both metrics offer insights into how well-focused and stable an image is. FWHM measures the spread of a star’s light profile at half its maximum intensity, making it useful for assessing atmospheric seeing conditions and overall sharpness. In contrast, HFR measures the radius containing half of a star’s total light, providing a reliable, brightness-independent measure ideal for maintaining precise focus during capture.
Together, these metrics help astrophotographers capture well-focused, high-quality data, enhancing the clarity and depth of their final images. HFR is frequently used in tools like NINA for autofocus routines, while FWHM is often analyzed in PixInsight to select the best frames for integration, helping to produce clear, detailed astrophotographs.
How HFR is Used in NINA
In NINA (Nighttime Imaging ‘N’ Astronomy) HFR plays a central role in focusing and monitoring star quality throughout a session.
1. Focusing with HFR in NINA
- Autofocus Routines: NINA’s autofocus system uses HFR as a primary indicator for achieving optimal focus. During focusing, NINA calculates HFR values across a range of focus points, identifying the point at which HFR is minimized (indicating the sharpest possible focus).
- Manual Focus Assistance: If you prefer manual focusing, NINA provides live HFR readings to help you adjust until you reach the lowest possible value, ensuring sharper images.
- Monitoring Changes: Environmental changes, such as temperature shifts, can affect focus. NINA’s focus monitoring with HFR alerts you to these changes, allowing automatic refocus when necessary.
Incorporating the NINA Hocus Focus add-on for NINA has significantly improved focusing at SadrAstro observatory by optimizing FWHM measurements and sharpening the focusing process. Hocus Focus brings several advancements:
- Enhanced Star Detection and Measurement: Hocus Focus refines how stars are detected and measured in the field of view. Unlike basic autofocus tools, which may struggle with misidentifying or inadequately measuring star shapes, Hocus Focus uses advanced algorithms to better recognize stars and measure their FWHM, even in varied or crowded star fields. This precision results in more reliable HFR data, directly impacting focus accuracy.
- Multi-Star Focusing: A standout feature of Hocus Focus is its multi-star focusing capability. Instead of relying on a single star to determine focus, which can be susceptible to local distortions or noise, Hocus Focus evaluates HFR across multiple stars within the field. This approach averages out irregularities caused by atmospheric seeing, guiding quality, or slight tilt in the optics, yielding a more stable, accurate focus across the entire frame.
- Automatic Focus Analysis and Graphing: Hocus Focus provides real-time graphs and analysis during focusing runs, allowing you to visually track HFR trends across different focus points. This visualization is invaluable, as it helps identify the precise point where HFR reaches its minimum, signaling optimal focus. Additionally, this feature makes it easier to identify and diagnose any irregularities in the focusing pattern, such as backlash or tilt, allowing you to make hardware adjustments if needed.
- Integration with Environmental Adjustments: By continuously monitoring and recalibrating focus throughout a session, Hocus Focus ensures that temperature shifts or mechanical drift are accounted for, helping to maintain a stable HFR over time. This automation means fewer interruptions and more time spent imaging.
By leveraging Hocus Focus add-on with NINA, we’ve improved HFR based focusing by providing more accurate star detection, multi-star averaging, real-time analysis, and environmental adaptability. These features make it a powerful addition to your observatory’s workflow, ensuring consistent, high-quality focusing and sharper astrophotography images. (I wish we could fix those pesky clouds!)
2. Analyzing Star Quality with HFR in NINA
- Frame Evaluation: HFR is used to assess star quality in individual frames, helping to ensure each exposure meets your sharpness criteria before moving to the next.
- Guiding Focus Across Frames: With live HFR monitoring, you can track fluctuations in sharpness across frames and decide if adjustments are needed, maintaining quality throughout your session.
We implement NINA’s Target Scheduler plugin which the Target Scheduler leverages image grading tools to analyze individual frames’ HFR values and rank them in terms of star sharpness and quality. By evaluating each frame’s HFR, you can configure the plugin to reject frames that exceed a certain HFR distribution, ensuring only the highest quality data is retained.
We’re continuously revising our target scheduler and image grading tools to try and optimize our data not only for image quality, but also for best use of observatory time and scheduling of targets when they’re in optimum sky conditions.
FWHM and Atmospheric Conditions
It’s important to note that FWHM is not just a reflection of your optical system; it also reflects atmospheric turbulence (seeing conditions). On nights with good seeing, you’ll experience lower FWHM values, leading to sharper images. However, if seeing conditions are poor, FWHM values will increase, even if your telescope is perfectly focused.
Tips for Managing FWHM under Various Conditions:
- Adjust Expectations: Accept that on nights with subpar seeing, FWHM may be higher, and tighter focus may be harder to achieve.
- Select Optimal Conditions: When possible, prioritize nights with stable atmospheric conditions, as they will naturally produce better FWHM values.
Unfortunately, some seasons just have poor seeing overall and FWHMs may be impacted. If the seeing conditions are poor and your FWHM values are higher than desired, using 2x drizzle combined with dithering during capture and integration can sometimes improve final image quality. Drizzle integration (especially 2x drizzle) is often used to increase the resolution of under sampled images, which can help enhance detail in situations with suboptimal seeing. However, it doesn’t actually lower FWHM; instead, it interpolates extra detail by increasing the image’s resolution, potentially making stars appear smaller and better defined when combined with dithering. When shooting nebula on modern sensors, we are usually under sampled so 2x drizzle integration is always recommended!
FWHM PixInsight Subframe Selector
The Subframe Selector tool in PixInsight is a powerful utility for evaluating and filtering individual frames based on various quality metrics, including FWHM, eccentricity, and signal-to-noise ratio (SNR). By analyzing each frame with these metrics, the Subframe Selector can help you identify the sharpest frames and exclude those affected by poor seeing, guiding errors, or other artifacts.
When preparing frames for integration in the Weighted Batch Preprocessing (WBPP) script, the Subframe Selector’s scoring system allows you to assign weights to each frame based on its quality. For instance, you might set FWHM thresholds to prioritize sharper frames, ensuring that only high-quality data contributes to the final image. This approach improves clarity and detail in the integrated image, resulting in a more refined and polished final product.
FWHM in PixInsight’s WBPP for Integration
After your imaging session, (or downloading our astrophotography data) you may use Weighted Batch Preprocessing (WBPP) in PixInsight to integrate your data. Here, FWHM plays a pivotal role in ensuring high-quality data integration.
1. Subframe Selection Based on FWHM
- WBPP allows you to select subframes based on FWHM, discarding frames where FWHM values exceed a threshold (indicating blur or poor seeing). This process helps to ensure only the sharpest frames contribute to your final image, enhancing the detail and quality of your result.
2. FWHM Weighting for Better Image Quality
- WBPP uses FWHM in its weighting algorithms, giving more “weight” to frames with lower FWHM (sharper frames). This means frames with tighter stars will contribute more to the final integration, reducing noise and improving clarity in the final image.
- Balancing Sharpness and Noise: Lower FWHM frames provide more detail, but they may also have slightly higher noise. WBPP helps balance this by integrating these frames intelligently, blending sharpness with a smooth background.
3. Assessing FWHM Across Different Filters
- For multi-filter imaging (such as LRGB or SHO), you can analyze FWHM for each filter and adjust integration strategies accordingly. Some filters (e.g., Hydrogen-alpha) may naturally have higher FWHM due to narrowband limitations, so understanding this difference helps optimize your integration process. (this can vary by
Star Optimization in PixInsight
In PixInsight, the Morphological Transformation tool can help reduce star size and improve star shape after integration, especially for images impacted by seeing. Morphological Transformation applies erosion or dilation to stars, allowing you to reduce the prominence of bloated stars caused by high FWHM values. Using functions like erosion and morphological selection, you can tighten star profiles, giving the appearance of sharper stars without compromising the surrounding nebulosity. Careful application with a star mask helps isolate adjustments to only the stars, keeping the background and other image elements untouched.
Together, drizzle, dithering, and morphological adjustments can optimize image quality under poor seeing conditions by refining star shapes and enhancing perceived resolution. These techniques, combined, are effective strategies in PixInsight for managing atmospheric limitations.
Tips for Optimizing FWHM in Astrophotography
To get the best results, consider these practices for keeping FWHM low and improving image quality.
1. Temperature Compensation for Focus
- Temperature fluctuations can impact FWHM, as they cause slight expansions or contractions in the telescope’s optical path. Use temperature-compensated focusing, if available, to minimize the impact on FWHM as the night cools. At SadrAstro we use temperature changes and deviations to autofocus as needed.
2. Using Autofocus at Regular Intervals
- Even with stable temperatures, refocusing periodically during the night helps to keep FWHM values low, ensuring sharper images throughout your session.
3. Minimizing Telescope Vibration and Guiding Error
- Guiding issues can lead to star elongation, which increases FWHM. Ensure your mount is stable, and adjust your guiding parameters to maintain accurate tracking. Our telescope mounts are capable of guided and unguided tracking with a 350 point “pointing model” and full PEC correction for periodic error.
4. Optimizing Exposure Time
- Shorter exposures can reduce the effects of atmospheric seeing on FWHM, but they may also increase noise. Experiment with exposure times to find a balance between minimizing seeing effects and keeping noise manageable.
Understanding FWHM’s Limitations
While FWHM is a powerful metric for image sharpness, it’s essential to recognize its limitations. FWHM (and HFR) does not account for all aspects of image quality, such as color fidelity or contrast. It also does not directly measure detail beyond star size, so while it’s valuable for focus and integration, FWHM should be considered alongside other metrics (e.g., eccentricity or signal-to-noise ratio) for a complete picture of image quality.
Conclusion
In astrophotography, mastering FWHM opens up new possibilities for creating clear, detailed images. By understanding and utilizing FHR in software like NINA and FWHM in PixInsight’s WBPP, you can focus more accurately, filter out subpar frames, and enhance the final quality of your images. Whether you’re setting up a new focusing routine or optimizing data integration, knowing how to manage and interpret FWHM will help you get the most out of each imaging session and bring the universe’s sharpest details into view.
