AstroFOV — Why and How I Built a Browser-Based Field of View (FOV) Calculator for Astrophotography

TL;DR — Choosing the right telescope focal length for a deep-sky object is a geometry problem: the object’s angular size, the sensor dimensions, and the focal length together determine how much of the frame the object fills. AstroFOV is a free, browser-based tool that solves this instantly for all 3,145 objects in Gary Imm’s Deep Sky Compendium, across 74 cameras and any focal length — no installation required. This article explains the problem, the four formulas behind the tool, and how it was built in a dialogue between a domain expert and Anthropic Claude Sonnet 4.6.

Live tool: https://wllfrd.gitlab.io/astro-fov/

1. Motivation

Daytime photography is a wonderful thing — whether landscapes, portraits, or wildlife. With the right focal length and aperture, and a bit of practice, you can separate your subject from the background (depth of field) and capture it within the appropriate field of view (FOV) of an APS-C or full-frame sensor. In post-processing — with tools like DxO, for example — you correct lens distortions and fine-tune the image to your liking. Done.

But what happens when you want to photograph stars? A glance at a clear night sky reveals breathtaking views, sometimes with a sea of stars, constellations like the Big Dipper, or the full moon in all its glory. Look closer through a telescope, and you discover remarkable structures like the Orion Nebula (M42). These structures deserve to be captured too, right?

Figure 1: Image of M42 (Orion Nebula) taken by the author with Seestar S50 and post-processed in Siril [6].

Thanks to enormous advances in astrophotography — particularly smart telescopes — the barriers to entry have become very low. No more complicated star-hopping across the sky. You tell the smart telescope what you want to photograph. It finds the target autonomously, tracks it through the night, and captures many individual frames, which are then stacked and processed into a final image — either on the device itself or afterward with dedicated software like Siril [6].

Good YouTube channels [14] and literature on astrophotography will support you well in this hobby: deep-sky objects are thoroughly cataloged (see [1] for theMessier objects). A solid introduction to astronomy is provided by [2]. Astrophotography with DSLR and mirrorless cameras is covered in [3], and telescope-based imaging in [4]. More in-depth reference works, such as [5], are also readily available. And for figuring out which objects are well-placed for your location and time of year, dedicated planning tools exist [7].

Once you have taken your first steps with a smart telescope, it is very likely that the desire to see “more” quickly arises. The market responds energetically: astro-cameras and mono cameras with filter wheels and filters, telescopes (Newtonians, refractors, and more), mounts, automation software such as N.I.N.A. [8] or PINS [9], and so on. Before long, several thousand — if not 15,000+ euros or more — have been invested for a proper rig.

At the core, you need a camera and a telescope with a suitable focal length. But which camera and focal length to choose? Both are what this article is about to help you make a well-informed decision.

The camera has a sensor built from pixels of a defined size, arranged across a physical area. The telescope has a focal length and aperture. Together, they determine what you can actually photograph in the sky. There is no single telescope that can capture everything — the size differences between objects are too vast. For nebulae, a focal length of around 500 mm is often appropriate. For smaller galaxies, 1,000 mm and beyond may be needed. Planets demand even more.

In this article, we focus on one specific question: how large does an object appear on the sensor at a given focal length? The AstroFOV tool might help substantially in checking expectations when, e.g., considering investments in the camera and the telescope.

2. The Problem: Matching Telescope and Camera to the Sky

One of the first things a new astrophotographer like me learns is that there is no universal telescope. The reason is simple: the objects you want to photograph differ in apparent size by several orders of magnitude. Understanding this mismatch — and planning around it — is the essential skill that separates a satisfying image from a frustrating evening at the eyepiece.

Angular Size: The Key Quantity

In astronomy, the size of an object is not measured in meters or kilometers but in angular degrees, arcminutes (′), and arcseconds (″). One degree equals 60 arcminutes; one arcminute equals 60 arcseconds. The full moon, for reference, spans roughly 30 arcminutes — half a degree — across the sky.

Figure 2: Subdivision of astronomical angular measurements from degrees to arcseconds. Generated by Google Gemini (2026).

Deep-sky objects span an enormous range within this system. The Andromeda Galaxy (M31) stretches approximately 190 × 60 arcminutes [7] — more than six times the diameter of the full moon. At the other extreme, a compact planetary nebula like the Blue Snowball (NGC 7662) subtends barely 28 arcseconds [7], roughly 1/400th the angular size of M31. Between these two extremes lie thousands of nebulae, galaxies, and star clusters, each requiring a different optical configuration to be captured well.

Focal Length Sets the Scale

The focal length of a telescope determines how large an object appears on the sensor. A short focal length (say, 300–500 mm) produces a wide field of view, well suited to large, extended objects like the Orion Nebula (M42, ~65′) or the North America Nebula (NGC 7000, ~120′). A long focal length (1,000 mm and beyond) compresses the field, revealing fine detail in compact galaxies or small planetary nebulae — but at the cost of field width [4].

This trade-off is unavoidable. As discussed in [4], choosing the right focal length for a given target is not a matter of preference but of geometry: the object must fill a meaningful portion of the sensor to be imaged effectively, without being so large that it overflows the frame entirely.

A rough practical guide:

Table 1: Practical guide for focal length.

The Sensor Adds a Second Dimension

The focal length alone does not determine what fits in frame. The physical size of the camera sensor matters just as much. A full-frame sensor (36 × 24 mm) captures a wider field at any given focal length than an APS-C sensor (roughly 23 × 15 mm) or a small-format astronomy camera (e.g. 14 × 10 mm). Two photographers using the same telescope but different cameras will see fundamentally different sky crops [3].

This interaction between focal length and sensor size — expressed as the field of view — is the central planning quantity in astrophotography. Everything else follows from it.

Figure 3: Field of View (FOV). Generated by Google Gemini (2026).

The Practical Question

Before every imaging session, the astrophotographer faces a specific question: given my camera and my telescope, how large will a particular object appear on my sensor? Will it fill the frame comfortably, leaving some sky around it? Will it overflow the frame, requiring a multi-panel mosaic? Or will it appear as a tiny smudge, demanding a longer focal length?

Answering this question by hand — for 3,145 cataloged objects, across dozens of camera models and focal lengths — is tedious. That is the problem AstroFOV was built to solve, too.

3. The Math: Four Formulas That Drive Everything

AstroFOV is built on four formulas. They are not complicated — each is a single line of arithmetic — but together they answer every meaningful question about sensor coverage in astrophotography. Understanding where they come from makes the tool’s output far more intuitive to interpret.

Formula 1: Field of View

The field of view (FOV) is the angular extent of sky captured by a given camera-and-telescope combination. It derives directly from basic trigonometry: a sensor of physical size s mm placed at the focal plane of a telescope with focal length f mm subtends an angle given by:

The factor (180/π) converts radians to degrees; multiplying by 60 gives arcminutes. For small angles — which is always the case in telescope optics — the small-angle approximation holds, so the formula is exact for practical purposes. The formula is well established in the astrophotography community; Sky & Telescope publishes it in the equivalent form FOV (arcmin) = (chip width × 3438) / focal length, where 3438 = (180/π) × 60 [10].

This is calculated separately for the sensor’s width and height, yielding a rectangular FOV in arcminutes. For example, an APS-C sensor of 23.5 × 15.7 mm on a 500 mm refractor yields:

• FOV width: (23.5 / 500) × (180/π) × 60 ≈ 161′
• FOV height: (15.7 / 500) × (180/π) × 60 ≈ 108′

That rectangle of sky — 161 × 108 arcminutes — is exactly what the camera will record. From here, the next formula translates that framing into pixel detail.

Formula 2: Plate Scale

The plate scale (also called image scale) describes how many arcseconds of sky correspond to one pixel on the sensor. It connects the pixel size p in micrometers to the focal length f in millimeters:

The /1000 converts µm to mm; multiplying by 3600 converts degrees to arcseconds. This simplifies to the compact form widely used in the community [11].

The constant 206,265 is the number of arcseconds in one radian — a fundamental conversion factor that appears throughout spherical astronomy [11].

For example, a ZWO ASI2600MC with 3.76 µm pixels on a 500 mm telescope gives a plate scale of approximately 1.55 ″/px — meaning each pixel captures 1.55 arcseconds of sky. This number is important for linking optical resolution to atmospheric seeing conditions, but for FOV planning purposes, it serves mainly as a reference value displayed in the detail panel. With both framing and sampling defined, the next formula shows how fully an object uses the frame.

Formula 3: FOV Fill Percentage

The fill percentage expresses how much of the sensor’s shorter edge is occupied by a given object. It follows directly from Formula 1 by dividing the object’s angular diameter by the smaller FOV dimension:

Using the shorter sensor edge as the reference means the fill percentage reflects the more constraining dimension — the object will fit within the frame only if Fill% ≤ 100% on both axes. Objects are treated as circles with a diameter equal to their cataloged angular size; for elliptical objects this gives a conservative worst-case estimate.

A fill of around 40–75% is generally regarded in the astrophotography community as ideal for composition: the object is large enough to show detail, but there is enough surrounding sky to give context and allow for slight framing adjustments. If the object exceeds the frame, the next formula determines how to tile it.

Formula 4: Mosaic Tiling

When an object is larger than the FOV (Fill% > 100%), a single frame cannot capture it. The number of mosaic tiles required along each axis is the smallest whole number that, with 15% overlap between adjacent panels, covers the object fully:

The 15% overlap is a widely adopted practice in astrophotography mosaic planning: it ensures that each panel shares enough common stars with its neighbors to enable reliable registration in stitching software [12]. A 2×2 mosaic therefore requires four exposures and covers roughly 1.7× the single-frame FOV per axis, since each added panel contributes about 0.85 of its width or height.

AstroFOV computes this automatically and displays the result as “N×M mosaic” directly in the results table.

4. The Data Foundation: 3,145 Objects from Gary Imm’s Compendium

A FOV calculator is only as useful as its object database. A tool that covers Messier’s 110 showpiece objects is a start; one that spans 3,145 cataloged deep-sky objects across 19 catalogs is a genuine planning instrument. AstroFOV uses the latter.

The Source: Gary Imm’s Deep Sky Compendium

The object data in AstroFOV comes from Gary Imm’s Deep Sky Compendium 2026, 6th Edition [7] — a freely available, thoroughly compiled reference covering thousands of deep-sky objects with consistent, structured data. Imm is also the author of The Complete Messier Catalog [1], and the Compendium reflects the same standard of careful curation.

The full dataset is available at www.garyimm.com/compendium and is freely redistributable with attribution. AstroFOV’s objects.json (313 KB) is derived directly from this source.

Data Structure

Each object in the AstroFOV database carries the following fields:

Table 2: Data structure

The size_arcmin field is the critical value for FOV calculations. It represents the largest angular dimension of the object. For planning purposes, AstroFOV treats all objects as circles with this diameter — a conservative approximation that guarantees the object fits within the computed frame, even for elongated or irregular shapes.

19 Catalogs

The database spans 19 major catalogs, covering the full range of objects a serious deep-sky imager would pursue:

• General catalogs: NGC, IC, Messier [1], Caldwell
• Nebula catalogs: Sharpless (Sh2), Barnard (dark nebulae), van den Bergh (vdB), LBN, LDN, RCW, Gum
• Galaxy catalogs: Arp (peculiar galaxies), Hickson (compact groups), Abell (galaxy clusters)
• Planetary nebulae: Abell PN
• Supernova remnants: SNR catalog
• Selected lists: Herschel 400

This breadth means the tool is equally useful for a beginner working through the Messier list [1] and an experienced imager hunting Sharpless hydrogen-alpha targets.

The Size Range — Why It Matters

The 3,145 objects span an angular size range that illustrates exactly why one telescope cannot do everything [7]:

Table 3: Typical sizes of different objects

A sensor that frames M31 beautifully at a 400 mm focal length will reduce M57 to a few-pixel dot. A setup optimized for M57 at 2,000 mm will show only the core of M31. Planning which objects are reachable with a given setup — without tedious manual calculation for each of 3,145 targets — is precisely what AstroFOV automates.

5. The Camera Database: 74 Cameras, One Custom Option

FOV depends equally on the object and the sensor. AstroFOV ships with a database of 74 cameras covering the most common dedicated astronomy cameras as well as several popular DSLRs and mirrorless bodies — all drawn from publicly available manufacturer specifications.

What the Database Contains

Each camera entry in cameras.json carries these fields:

Table 4: Camera database structure.

For FOV calculations, only sensor_w, sensor_h, and pixel_um are used. The remaining fields drive filtering (by brand, format, and sensor type) and the sensor preview display.

Manufacturers and Segments Covered

The 74 cameras span a broad range of the current dedicated astrophotography market:

Dedicated astronomy cameras (cooled and uncooled): ZWO — ASI series (ASI 2600, 6200, 294, 533, 183, 462, and others) plus the Seestar S50 and S30 Pro smart telescopes; Player One — Poseidon, Neptune, Uranus, and Ares series; QHY — QHY268, QHY600, QHY294, and others; Omegon — veTEC series; Atik — 383L+, 460EX, Horizon series; Altair — Hypercam series.

Consumer cameras adapted for astrophotography: Canon EOS Ra (astronomy-modified full-frame mirrorless), Nikon D810A (astronomy-modified DSLR), Sony A7S III (high-sensitivity full-frame mirrorless).

Smart telescopes (integrated camera + optics): Vaonis Stellina and Hyperia; Unistellar eVscope 2 and Equinox 2.

Mono vs. OSC — Irrelevant for FOV, Important for Context

The type field distinguishes mono (monochrome) cameras from OSC (One-Shot Color) cameras. This distinction matters greatly for image quality and workflow [5]: mono cameras require separate exposures through individual narrowband or broadband filters. In contrast, OSC cameras capture all colors in a single exposure via a Bayer matrix. However, for FOV calculation, the distinction is entirely irrelevant — only the physical sensor dimensions and pixel size determine what fits in frame.

AstroFOV’s camera filter lets users narrow the list by type (Mono / OSC) and sensor format (Full Frame / APS-C / MFT / Small Format), making it easy to compare options within a given camera category.

The Custom Option

Not every camera is in the database. For any sensor not listed — whether a newer model, a modified DSLR, or a future release — the Custom option allows manual entry of sensor width and height in mm and pixel size in µm. With these three values, AstroFOV computes the full FOV and plate scale identically to any listed camera — the sensor preview updates in real time, showing the physical dimensions to scale alongside the technical parameters.

6. AstroFOV in Action: One Mode, Powerful Filters

AstroFOV has a single working mode: Mode A — Object Search. All 3,145 objects from Gary Imm’s Deep Sky Compendium [7] are searchable in one unified view. The design decision to consolidate everything into one mode was deliberate. Rather than splitting functionality across multiple tabs, the sidebar filters and the FOV-Filter together cover every planning scenario a user would need.

Figure 4: AstroFOV at a glance. Figure generated by the author.

Setting Up: Camera and Focal Length

The sidebar begins with two inputs that define the optical system:

Camera selector — a cascading dropdown filtered by brand, then model. Chip-style filter buttons above the brand selector narrow the list by sensor type (Mono / OSC) and format (Full Frame / APS-C / MFT / Small Format). The model label shows the current filtered count, e.g. Model (10/74). Selecting a camera immediately updates the sensor preview — a scaled rectangle showing the physical sensor dimensions alongside key technical data (sensor size in mm, pixel size in µm, resolution in megapixels). The camera selection is smart in such a way that, when changing from Mono to OSC, the comparable model is selected, e.g., ZWO ASI2600MM Pro changes to ZWO ASI2600MC Pro.

Figure 5: The camera settings. Figure generated by the author.

Focal length — direct numeric input in mm, with preset buttons for common telescope focal lengths from 100mm to 3,000 mm. Changing either value instantly recalculates all 3,145 rows in the results table. Also, you may want to manually adjust the focal length beyond 3.000mm. This is possible. The slider jumps to the right but the entered focal length is valid.

Figure 6: The Focal length settings. Figure generated by the author.

The Results Table

The main table lists all objects matching any active filters, sorted by name by default (all columns are sortable). The most important columns:

Fit — an immediate visual verdict: Passt (the object fits within the sensor frame), Groß (the object is larger than the FOV on at least one axis), or N×M Mosaik (a mosaic of N columns × M rows is required).

FOV Fill % — a percentage bar showing how much of the shorter sensor edge the object occupies. A fill of 40–75% is generally considered compositionally ideal; the bar color indicates whether the object is underfilling, well matched, or overflowing the frame.

Size — the object’s angular diameter from the Compendium [7]. Type / Subtype / Constellation — classification fields, all filterable.

Figure 7: The results table. Figure generated by the author.

The FOV-Filter: The Central Planning Tool

The FOV-Filter is a min/max range slider in the sidebar that filters the results table by fill percentage. Setting it to, say, 40–80% shows only the objects that fill between 40% and 80% of the shorter sensor edge at the current camera-focal length combination. A live counter below the slider shows the result instantly — for example: “312 objects (9.9%) match this criterion”.

This is the core planning workflow: choose a camera, enter a focal length, set a target fill range, and read off which objects are well-matched to this setup tonight. Changing the focal length updates the count in real time, making it straightforward to find the focal length that maximizes the number of suitable targets.

Figure 8: FOV filter settings. Figure generated by the author.

Object Filters

Three additional filter layers narrow the results. The type filter covers Neb / Gal / Stars. The subtype filter appears contextually when a type is active, covering PN, Spiral, Em, SNR, GC, OC, Dark, and others. The catalog filter offers 19 catalogs selectable individually — Messier, Caldwell, NGC, IC, Herschel 400, Arp, Hickson, Sharpless, Barnard, vdB, Abell PN, Abell Galaxies, LBN, LDN, RCW, SNR, and Gum.

Catalog filters can be combined with type and FOV filters to create precise queries such as“all Sharpless emission nebulae that fill 30–70% of my sensor at 500 mm”.

Figure 9: Object filters. Figure generated by the author.

The Detail Panel

Clicking any row opens a detail panel with a sky preview via Aladin Lite (CDS Strasbourg) — a live DSS image of the object at the correct angular scale, with the sensor frame overlaid; catalog memberships — all catalog designations for the object, each clickable to filter the main table to that catalog; plate scale in arcseconds per pixel for the current setup; and mosaic details — if applicable: tile count, overlap, and total coverage in arcminutes. The detail panel is also horizontally and vertically resizable.

The sky preview is particularly useful for objects with irregular morphology, where the circular size approximation in the main table is too coarse — seeing the actual DSS image with the sensor frame overlaid gives an immediate sense of framing quality.

Figure 10: The detail panel. Figure generated by the author.

Bilingual Interface

The entire interface — all labels, tooltips, filter buttons, and the embedded README — is available in both German and English. A toggle in the header instantly switches the language without a page reload.

7. A Practical Example: Planning an M42 Session

Abstract formulas become concrete with a real example. Let us work through a typical planning session — choosing equipment for the Orion Nebula (M42), one of the most photographed objects in the winter sky.

The Setup

Suppose you are working with a ZWO ASI2600MC Pro (Sony IMX571 sensor, 23.5 × 15.7 mm, 3.76 µm pixels, APS-C format, OSC) and a 400 mm refractor. Select the camera from the ZWO dropdown (filter: OSC; APS-C narrows the list immediately), then enter 400 mm as the focal length. The sensor preview updates: 161 × 108 arcminutes FOV, plate scale 1.94 ″/px.

Searching for M42

Type “M42” in the search field. The Orion Nebula appears instantly:

Table 5: Information about M42.

A fill of 60% on the shorter sensor edge (108′) means M42 occupies a comfortable portion of the frame with sky context on all sides — close to the ideal compositional range of 40–75%. The object fits without a mosaic. Clicking the row opens the detail panel: the DSS preview shows the familiar arc-shaped nebula with the Trapezium at its heart, with the sensor frame overlaid at the correct scale.

What Changes at Different Focal Lengths?

Changing the focal length in the sidebar recalculates everything instantly:

Table 6: Different fits of M42 depending on focal length.

At 800 mm, AstroFOV shows “2×1 Mosaik” in the Fit column: two panels side by side, with 15% overlap, are needed to cover the full object.

Using the FOV-Filter for Session Planning

Set the FOV-Filter to 40–80% and keep the focal length at 400 mm. The live counter shows how many of the 3,145 objects fall into this ideal framing range for this specific setup. Add a catalog filter for Messier objects, and you have a curated shortlist of showpiece targets perfectly matched to your equipment — ready to hand off to your planning software or automation tool.

The Orion Nebula in Context

This example also illustrates the breadth of the database. Around M42 in the search results, you will find its neighbors: the Running Man Nebula (NGC 1977, ~20′), and de Mairan’s Nebula (M43, ~20′). All appear in the same table, with their fill percentages computed for your exact setup. A single AstroFOV planning session can map an entire region of the sky.

8. Under the Hood: How It’s Built

AstroFOV is deliberately simple software. There is no backend, no database server, no user accounts, and no data collection. Everything runs in the browser — a single HTML file, two JSON data files, and a CI/CD pipeline to publish updates.

A Single HTML File

The entire application — layout, logic, styling, and the bilingual interface — lives in index.html. No build step, no npm install, no framework. The two data files, cameras.json (74 camera entries) and objects.json (3,145 deep-sky objects, 313 KB), are fetched once on load and held in memory. All subsequent filtering, sorting, and calculation is purely in-memory JavaScript — which is why the results table updates instantly as you type or adjust a slider, with no network round-trip.

The tool works offline once loaded and can be self-hosted by anyone who wants a local copy.

GitLab Pages and CI/CD

The live version is hosted on GitLab Pages. The CI/CD pipeline is a concise YAML file that copies the four project files into public/ on every push to main. Within a minute or two, the live site is updated. Adding a camera to cameras.json, committing, and pushing is all it takes to make a new camera available to every user — no deployment script, no server restart.

The tool is accessible at: [https://wllfrd.gitlab.io/astro-fov/](https://wllfrd.gitlab.io/astro-fov/)

Aladin Lite for Sky Previews

The detail panel’s sky preview is powered by Aladin Lite v3 [13], the browser-based sky atlas developed by the Center de Données astronomiques de Strasbourg (CDS). Aladin Lite renders real DSS (Digitized Sky Survey) imagery at the correct angular scale for any object, with the sensor frame overlaid as a rectangle computed from the FOV dimensions and centered on the object’s coordinates.

Built with Anthropic Claude

AstroFOV was developed in an extended dialogue with Anthropic Claude Sonnet 4.6 via claude.ai. Claude wrote the application code, researched and compiled the camera specifications, and structured the data pipeline from Gary Imm’s Compendium [7] (~32 MB) into the objects.json format (~450 kB), using only the information we needed for FOV calculations.

The author elaborated the concept, astrophotography domain expertise, testing, and directions of development.

This workflow — using an AI assistant for technical implementation while a domain expert drives requirements, tests outputs, and corrects course — is itself an interesting subject. The camera database alone required cross-referencing dozens of manufacturer specification pages; the FOV filter and its live counter went through several iterations before the interaction model felt right.

9. What’s Next

AstroFOV covers the core planning question well: given a camera and focal length, how do any of 3,145 deep-sky objects fit on the sensor? But a tool built in dialogue between a domain expert and an AI assistant tends to accumulate ideas faster than it implements them. The backlog is honest about this.

Mobile layout — the current interface is designed for desktop use. A responsive layout with a collapsible sidebar and touch-friendly sliders would make AstroFOV usable at the telescope, not only at the desk.

Export — there is currently no way to save a filtered target list. A straightforward export to CSV or PDF would let users carry a planning session into their imaging software or print a paper list for the observing session.

Visibility windows — integrating rise/set times and object altitude for a given observer location and date would transform AstroFOV from a pure FOV tool into a full session planner. A lightweight version integrated with the FOV filter would be useful: “show me objects filling 40–80% of my sensor that are above 30° altitude tonight from my location”.

Bortle filter — filtering by object magnitude and surface brightness relative to a Bortle scale estimate for the observer’s site. Faint, low-surface-brightness targets may be unsuitable from light-polluted locations regardless of their FOV fit.

Elongation and position angle — for elliptical objects, the current worst-case circular approximation is conservative. Displaying the actual position angle and major/minor axis dimensions in the detail panel — and using them for a more precise fill calculation — would improve accuracy for galaxy imaging in particular [5].

Community Contributions

The camera database is the most immediately extensible part of the project. Adding a camera requires editing a single JSON file and opening a merge request on GitLab — a low barrier for anyone comfortable with a text editor.

If AstroFOV is useful to you: try it, break it, suggest a camera, or open an issue on GitLab. The tool is as good as the community that uses it.

Live tool: https://wllfrd.gitlab.io/astro-fov/ Object data: Gary Imm’s Deep Sky Compendium — https://garyimm.com/compendium [7]

References

[1] Gary Imm, The Complete Messier Catalog, 2025 [2] Frank Sackenheim, Astronomie, Rheinwerk, 2026 [3] Katja Seidel, Astrofotografie, Rheinwerk, 2020 [4] Thierry Legault, Astrofotografie, dpunkt Verlag, 3rd edition, 2024 [5] Charles Bracken, The Deep-Sky Imaging Primer, Third Edition, 2022 [6] Cyril Richard, Siril, Axilone, 2026 [7] Gary Imm, Deep Sky Compendium, https://garyimm.com/compendium [8] N.I.N.A., https://nighttime-imaging.eu/ [9] PINS, https://www.youtube.com/watch?v=wYG5OBc7rUE [10] Richard Wright, Framing Your Astro Images: Understanding Field of View and Pixel Scale, Sky & Telescope, 2018, https://skyandtelescope.org/astronomy-blogs/imaging-foundations-richard-wright/understanding-field-of-view-pixel-scale/ [11] Roger N. Clark, Image Plate Scale, clarkvision.com, 2012, https://clarkvision.com/articles/platescale/ [12] Sara Wager, My Guide to Image Capture — Creating a Mosaic in SGPro, swagastro.com, https://www.swagastro.com/sgpro---creating-a-mosaic-in-software.html [13] M. Baumann et al., Aladin Lite v3: Behind the Scenes of a Major Overhaul, ASPC, 532, 7, 2022, https://aladin.cds.unistra.fr/AladinLite/ [14] See, e.g., German-speaking channels like https://www.youtube.com/@astrophotocologne, https://www.youtube.com/@astromarso or English-speaking channels like https://www.youtube.com/@the_space_koala, https://www.youtube.com/@DeepSpaceAstro, https://www.youtube.com/@CuivTheLazyGeek, https://www.youtube.com/@edislatube