Building a Bortle-9 Imaging Index
A two-year structured observation toward a predictive model of urban imaging difficulty
Most deep‑sky photography begins with dark skies.
This project begins with the opposite.
Throughout 2026 & 2027, I’m imaging a curated set of targets from Bortle‑9 suburban Chicago—one hour at a time, under the glow of a city that never really gets dark. By tracking conditions, scoring results, and analyzing the patterns, I’m building a data‑driven model of what’s truly possible when light pollution is the rule, not the exception.
This is the Bortle‑9 Imaging Index:
a study in limits, discipline, and the quiet satisfaction of making something beautiful under imperfect skies.
Project Overview
The Bortle‑9 Imaging Index — An Experiment in Urban Astrophotography
Purpose
This project explores a simple question with a complicated answer:
What determines whether a deep‑sky object is realistically achievable under Bortle‑9 skies?
Throughout 2026 & 2027 I’ll image a curated list of targets ranging from “guaranteed success” to “probably impossible,” record the conditions of each session, and score the final results. The goal is to build a data‑driven model — the Bortle‑9 Imaging Index — that predicts how difficult a target will be from a severely light‑polluted location.
This is equal parts science experiment, creative challenge, and honest documentation of what’s possible when you refuse to give up on the night sky.
Why This Matters
Urban astrophotography is often dismissed as a compromise. This project argues the opposite.
By embracing constraints — limited integration time, heavy light pollution, narrowband filters, unpredictable seeing — we can learn which objects still shine through and why. The Index aims to give beginners and experienced imagers a realistic roadmap for choosing targets that match their conditions, gear, and expectations.
It’s not about perfection.
It’s about clarity, consistency, and craft.
The Plan
1. Collect
Image 25 deep‑sky objects across the full difficulty spectrum.
For each session, record variables such as:
Seeing and transparency
Moon phase and distance
Altitude
Guiding RMS and FWHM
Sky background
Filter choice
Integration efficiency
Final image quality score
2. Analyze
Use the dataset to explore:
Which variables correlate most strongly with image quality
How object type, size, and surface brightness affect difficulty
How filters perform under Bortle‑9 conditions
Which targets consistently exceed or fall short of expectations
3. Model
Develop a predictive formula — the Bortle‑9 Imaging Index — that estimates difficulty based on measurable variables. The model will evolve as more data is collected.
4. Publish
Share the results, insights, and methodology through:
Field Notes
Gallery entries
A dedicated Bortle‑9 Index page
Educational resources for urban imagers
The Target List
The 25 objects span five tiers (5 objects each):
Tier 5 — Easy Wins Bright nebulae, large galaxies, and iconic showpieces. M42, M31, North America Nebula...
Tier 4 — Should Work Well Filter-friendly emission regions and mid-brightness galaxies. Eastern Veil, Elephant Trunk, Dumbbell Nebula...
Tier 3 — Moderate Challenges Low surface brightness galaxies and dimmer nebulae. Whirlpool, Pinwheel, Rosette Nebula...
Tier 2 — Tough Targets Small galaxies, faint emission, and difficult broadband objects. Horsehead, Thor's Helmet, Phantom Galaxy...
Tier 1 — Probably Not Happening Extremely faint supernova remnants, distant galaxies, and ultra-low-surface-brightness structures. Cas A, Spaghetti Nebula, Barnard's Loop...
The list is intentionally diverse—a full year of seasonal opportunities and constraints.
Scoring
Each final image is rated on a 1–5 scale:
5 — Excellent
4 — Good
3 — Acceptable
2 — Marginal
1 — Poor
The score reflects structure, color, noise, and overall clarity given the conditions.
Tools & Workflow
This project uses a consistent, repeatable workflow:
One‑hour imaging sessions
Dual‑band filters for emission targets
Broadband for galaxies and reflection nebulae
Structured session logging
Spreadsheet and Qlik‑based analysis
AI‑assisted modeling and documentation
The goal is not to optimize every variable — it’s to keep the process honest and comparable.
What You Can Expect
As the year unfolds, I’ll publish:
Session notes
Raw observations
Final images
Analysis summaries
Insights into what worked and what didn’t
Updates to the Index as the model improves
By the end of 2027, the project will produce a practical, data‑driven guide for anyone imaging under heavy light pollution.
Follow the Project
New entries will appear in Field Notes, with galleries updated as each target is completed. The Index itself will evolve throughout the year and will eventually become a standalone resource.
Clear skies,
Pete