Logging Your Journey Through the Cosmos

You finish an imaging session, look at the folder, and see a pile of files.

Some are individual frames. Some are stacked results. Some may have previews. The folder name made sense last night, but now you are trying to remember what happened at 11:40, why one subject has less useful time than expected, and whether the final image looks weak because of conditions, tracking, focus, or simply not enough data.

This is where many beginners get stuck. Planning the night is hard, but understanding the night afterward can be just as important.

AstroGuide’s session logging and analysis tools are meant to turn that folder into a story you can learn from.

Importing sessions gives the night a memory

Astrophotography creates a lot of evidence. The problem is that evidence often lives in places that are hard to read quickly: FITS headers, folder names, timestamps, stacked files, previews, and scattered notes.

AstroGuide’s Direct Scan flow helps by importing telescope or external-storage folders through the iOS Files picker. It scans supported FITS files, reads metadata, groups files into observation sessions, and imports session records once the metadata is ready.

In everyday terms, it is like turning a grocery receipt into a meal journal. The raw receipt has the facts, but the journal tells you what happened.

Direct Scan can also offer deeper analysis when the source files are still available. The app separates the quick import from optional heavier work, so you can finish the basic import or keep the folder connected for more analysis.

History turns sessions into nights

Once sessions are imported, History becomes the post-capture home.

History is organized around observing nights. It can show night images, target efficiency, session time slots, session lists, night details, and equipment summaries. On iOS, the History tab currently opens into the night-detail experience when completed sessions exist.

That matters because beginners often remember the night as a blur:

• “I tried two subjects.”
• “The Moon came up later.”
• “Something interrupted the run.”
• “One result looked better, but I am not sure why.”

History helps separate those memories into visible pieces: when sessions happened, how long they ran, how much integration they produced, what equipment and filters were involved, and which subjects were recognized.

Efficiency explains where the time went

Efficiency is one of the most practical review concepts in the app.

In simple terms, efficiency compares the time the session took with the useful imaging time it produced. If the telescope spent three hours outside but only produced ninety minutes of integrated exposure, that difference is worth understanding.

Low efficiency is not automatically bad. It can happen because of setup time, clouds, rejected frames, pauses, meridian behavior, refocusing, target changes, or interruptions. The point is not to feel judged by a number. The point is to see where the night went.

In AstroGuide, Target Efficiency helps compare elapsed capture time to integrated exposure by subject. That can reveal whether one subject got most of the useful time, whether a multi-subject night was too fragmented, or whether a future plan should be simpler.

Technical context: elapsed time is the wall-clock span of the session, while integration is the accumulated exposure time that contributes to the result. Sub length describes the duration of individual exposures. Efficiency relates those pieces so you can compare sessions more fairly than by clock time alone.

SNR is a clue about signal strength

SNR stands for signal-to-noise ratio.

That sounds technical, but the practical idea is familiar. Imagine trying to hear a quiet song while a fan is running. The song is the signal. The fan is the noise. If the song is much louder than the fan, it is easier to hear. If the fan is nearly as loud as the song, the details get lost.

In astrophotography, the signal is the light from the subject. Noise can come from the camera, sky brightness, processing limits, and the randomness that appears when you are collecting very faint light.

AstroGuide can use stacked-image metrics when analysis is available to help compare image quality. SNR should not be treated as a single magic score, but it can help you notice whether one session produced cleaner data than another.

Technical context: AstroGuide’s image metric path can inspect channel-level measurements and derive composite SNR-like values for comparison. Those numbers are most useful when compared across related sessions, similar equipment, and similar processing assumptions.

Coverage explains how much of the field is supported

Coverage is about how completely the usable image area is supported by data.

A simple analogy is painting a wall. If you only paint the center, the wall technically has paint on it, but the edges are thin or missing. In imaging, coverage helps describe whether the field has enough data across the frame or whether parts of the image are less supported.

Coverage can be affected by drift, framing changes, stacking behavior, mosaics, rejected frames, and how the subject moves through the field over time.

For beginners, coverage is useful because it explains why an image can look strong in one area and weaker elsewhere. It can also help you understand why composition and tracking stability matter.

Technical context: AstroGuide can represent coverage estimates from analyzed stacked image metrics, including per-channel values where available. Like SNR, coverage is best read as context, not a verdict.

Drift analysis shows whether the field stayed put

Drift is movement over time.

If you place a sticky note on a window and watch the scene behind it slide slowly away, that is the basic feeling of drift. In an imaging session, the subject may slowly shift across the frame because of tracking behavior, alignment, mount limits, field rotation, or interruptions and recovery events.

Small drift may be normal. Larger drift can reduce coverage, complicate stacking, or explain why the final image has weaker edges. If the session involved a lightweight setup, wind, or imperfect tracking, drift can be the missing clue.

AstroGuide supports observed drift analysis when the needed source files and local analysis are available. In Session Detail, diagnostics can show drift or a fallback diagnostics summary, and some analysis can load lazily after the screen opens.

Technical context: drift analysis looks for movement patterns across frames or derived analysis assets. The app treats this as deeper local analysis rather than something every imported session will instantly have.

Review connects back to planning

The best reason to log your journey is not nostalgia. It is learning.

When imported sessions connect to Target Detail Results, History, and Goals & Objectives, the past becomes part of the next plan. You can see whether a subject already has useful data, whether an objective moved forward, whether a setup is less efficient than expected, or whether a future imaging session should use a different window.

This is where AstroGuide’s planning loop becomes more complete:

• plan the night
• image the subject
• import the session
• review the evidence
• adjust the next plan

You do not need to become a data scientist to benefit from that loop. You only need enough feedback to answer a better question after each night:

What did this imaging session teach me?