MPO Canopus v12 Workflow

Abstract

This comprehensive guide provides a step-by-step workflow for asteroid lightcurve photometry using MPO Canopus Version 12. It covers the complete pipeline from image loading and quality assessment through photometric measurement, period analysis, and ALCDEF export. Special emphasis is placed on data quality control, photometric accuracy, and troubleshooting common issues.

1. Introduction

MPO Canopus v12 is a specialized software package for asteroid photometry and lightcurve analysis. This guide assumes you have:

• Basic familiarity with astronomical imaging and FITS file format
• Calibrated images (bias/dark/flat corrected)
• A basic understanding of differential photometry principles

1.1 Workflow Overview

The complete workflow consists of six main phases:

1. Prerequisites & Quality Control — Validate image quality
2. Setup & Image Loading — Configure software and import data
3. Aperture Configuration — Set photometric parameters
4. Target & Star Selection — Identify asteroid and comparison stars
5. Measurement — Execute photometry
6. Analysis & Export — Determine period and generate outputs

2. Phase 0: Prerequisites & Image Quality Assessment

2.1 Required Image Preparation

Calibration

All images must be calibrated with appropriate bias/dark and flat frames. While Canopus can perform calibration, the standard v12 workflow assumes pre-calibrated data.

Plate Solving

Plate solving and WCS headers are highly recommended. Plate-solved images enable:

• Automatic catalog star identification
• Accurate coordinate transformations
• Faster processing
• HJD or BJD corrections

File Organization

Store all images for a single observing session in one dedicated folder with consistent naming convention.

2.2 Critical Quality Checks

Examine your images carefully before beginning photometry:

3. Phase 1: Setup & Image Loading

3.1 Launching the Asteroid Wizard

1. Launch MPO Canopus v12
2. Navigate to: Photometry → Multi-image Photometry → Asteroid Wizard
3. The wizard interface replaces the older manual photometry workflow used in earlier Canopus versions

3.2 Equipment Profile Selection

4. When prompted, select the equipment profile matching your telescope and camera setup
5. If no profile exists: File → Configuration to create one
6. Profile stores critical parameters:
   • Telescope aperture and focal length
   • Camera pixel size and gain
   • Default aperture settings
   • Observatory location (for HJD corrections)

3.3 Loading Images

7. Click the Select Images tab
8. Click Folder and navigate to your FITS directory
9. Select all files (Ctrl+A or Cmd+A)
10. Configure display options:
    • Review: Set to "No" for faster loading if you've pre-validated images
    • Scaling: "Auto" or "Compressed" to visualize faint stars
11. Click Load

4. Phase 2: Aperture & Photometry Configuration

4.1 Understanding Aperture Photometry

Canopus uses aperture photometry with three concentric regions:

1. Measuring Aperture — Circular region centered on the star, capturing stellar flux
2. Gap — Separation zone to avoid contamination
3. Sky Annulus — Ring region for background measurement (defined by inner and outer radii)

4.2 Setting Aperture Parameters

Access via: Configuration → Photometry Settings or directly in the wizard.

4.3 The Goldilocks Zone: Optimal Aperture Sizing

Choosing the correct aperture size is critical. Here's what happens with different choices:

• Too Small (r < 1.5×FWHM): Missing flux from star wings → systematic errors, especially in varying seeing
• Optimal (r = 1.5–2.5×FWHM): Captures >95% of star flux while minimizing sky noise
• Too Large (r > 3×FWHM): Excessive sky noise dominates → poor signal-to-noise ratio

4.4 Visual Verification

1. Use the Aperture Tool to display apertures overlaid on an image
2. Select a bright (but non-saturated) star
3. Verify:
   • Measuring aperture contains all visible star flux
   • Sky annulus is free from nearby stars
   • Gap prevents star wings from entering sky region
4. Examine the radial profile plot if available

5. Phase 3: Target & Comparison Star Selection

5.1 Locating the Asteroid

1. The wizard displays the first image in your sequence
2. Locate the asteroid. If difficult to find:
   • Use the Blink tool to animate frames
   • The moving object is the asteroid; background stars remain stationary
3. Click on the asteroid — a red circle appears
4. When prompted, locate and click the asteroid in the last image
5. Canopus calculates the motion vector and predicts positions for all intermediate frames

5.2 Session Metadata Entry

Accurate metadata is essential for ALCDEF export and scientific validity.

5.3 Comparison Star Selection

Canopus will automatically identify candidate comparison stars (marked with green or blue circles).

Selection Criteria

Choose 3–5 comparison stars based on:

1. Brightness Match:
   • Similar magnitude to the asteroid (within ±2 mag ideally)
   • Ensures comparable signal-to-noise ratio
2. Non-Saturation:
   • Inspect star profiles — reject any with flat-topped peaks
   • Check pixel values if uncertain
3. Stability (Variable Star Check):
   • Cross-check against VSX (Variable Star Index) database
   • Canopus can perform this check if catalog access is configured
   • Avoid known variables at all costs
4. Spatial Distribution:
   • Spread stars across the field of view
   • Don't cluster all comparisons in one corner
   • Helps average out field-dependent systematics (vignetting, etc.)
5. Color Match (Advanced):
   • Ideally, select stars with similar spectral type to the asteroid
   • Reduces differential extinction and filter transformation errors
   • Often impractical; use similar brightness as primary criterion
6. Path Avoidance:
   • Ensure comparison stars don't lie in the asteroid's predicted path
   • Check motion vector overlay

5.4 Understanding Differential vs. Calibrated Photometry

Differential Photometry (Default)

Canopus measures the asteroid's brightness relative to comparison stars. This produces:

• Instrumental magnitudes (arbitrary zero-point)
• Excellent for period determination (only relative changes matter)
• Cannot be directly compared to catalog values

Calibrated Photometry (Optional)

For standard magnitudes:

1. Obtain catalog magnitudes for your comparison stars from:
   • APASS
   • Gaia DR3
   • 2MASS (for infrared)
   • Pan-STARRS
2. Enter catalog magnitudes in: Session Manager → Comparison Stars
3. Canopus will calculate standard magnitudes
4. Results can be compared to predicted asteroid magnitudes

6. Phase 4: Photometric Measurement

6.1 Executing the Measurement

1. Verify all settings (apertures, comp stars, metadata)
2. Click the Measure button
3. Canopus will iterate through all FITS files, measuring:
   • Target asteroid at predicted positions
   • All comparison stars at fixed positions
   • Sky background in annuli

6.2 Monitoring the Process

What to monitor:

• Red aperture stays centered on the asteroid
• No aperture overlaps (target and comp stars)
• Progress bar advances smoothly
• No error messages about saturated or failed measurements

If tracking fails:

1. Stop the measurement immediately (Escape or Stop button)
2. Return to target selection
3. Re-click asteroid in first/last frames more precisely
4. For long sessions, consider breaking into shorter blocks

Handling Non-Linear (Curved) Motion

For sessions exceeding 6–8 hours, asteroid motion often becomes non-linear, causing the fixed-path tracking to fail.

1. Data Segmentation: Break the session into 3–4 hour "blocks"
2. Measurement: Run the measurement wizard for the first block. Once complete, do not clear the session
3. Stitching: Load the next block of images. MPO Canopus will treat these as a continuation. Re-center the target on the first frame of the new block; the software will calculate a new motion vector while preserving the photometric zero-point from the previous stars

6.3 Automatic Session Saving

Upon completion, Canopus automatically saves:

• Photometric data to the MPO database (.MDB)
• Session parameters and metadata
• Comparison star information

Session name can be found in: Lightcurve Analysis → Sessions

7. Phase 5: Period Analysis with FALC

7.1 Loading Data into Analysis Mode

1. Close the Photometry Wizard (if still open)
2. Navigate to: Lightcurve Analysis (toolbar icon or Analysis menu)
3. In the Analysis window:
   • Click Sessions (top-left panel)
   • Check the box next to your session name
   • Click Load
4. Raw data points appear in the main graph (unphased, time-series plot)

7.2 Understanding FALC

FALC (Fourier Analysis of Light Curves) determines the rotation period by:

1. Testing a range of trial periods
2. For each period, folding the lightcurve (wrapping time onto phase 0.0–1.0)
3. Fitting a Fourier series to the phased data
4. Computing the RMS (Root Mean Square) residual
5. Identifying the period with minimum RMS as the best-fit rotation period

7.3 Period Search Strategy

Step 1: Coarse Search

1. Locate the FALC panel (typically right side of Analysis window)
2. Set search parameters:
   • Start: Lower period bound (e.g., 2.0 hours)
   • Stop: Upper period bound (e.g., 20.0 hours)
   • Step: Coarse step size (e.g., 0.1 hours)
   • Order: Start with 2 or 3
3. Click Find Period or Search
4. Examine the Period Spectrum (periodogram) that appears

Step 2: Identifying Candidate Periods

The period spectrum shows RMS error vs. trial period:

• Deep minima (low RMS) indicate good period candidates
• Multiple minima are common — requires further investigation
• Lowest global minimum is the primary candidate

How to Distinguish True Periods from Aliases:

1. True period: Deepest RMS minimum, produces clean double-peaked lightcurve
2. Daily aliases (12h, 24h): Artifacts of observing cadence. Test by:
   • Adding data from different nights (breaks the 24h pattern)
   • Checking if lightcurve morphology makes physical sense
3. Harmonics (2×P, 3×P): Integer multiples of true period. Usually shallower minima

Step 3: Fine Search

1. Note the period(s) with lowest RMS from coarse search
2. Set a narrow search range around the best candidate:
   • Start: Best period −0.5 hours
   • Stop: Best period +0.5 hours
   • Step: Fine step (0.01 or 0.001 hours)
3. Re-run FALC
4. The refined minimum gives your final period estimate

Step 4: Optimizing Fourier Order

1. With the best period selected, experiment with Fourier order:
   • Order = 2: Double-peaked, sinusoidal lightcurve
   • Order = 3–4: Asymmetric or complex shapes
   • Order = 5+: May overfit noise (use cautiously)
2. Choose the minimum order that adequately represents the lightcurve shape
3. Higher orders reduce RMS but risk fitting noise rather than signal

7.4 Validation and Quality Checks

Aliasing Detection

How to check:

• If your best period P ≈ 24 hours, test P/2 and 2P
• Compare RMS and visual appearance of phased lightcurves
• Multi-night observations break daily aliases

Lightcurve Morphology

After phasing with the best period, assess the lightcurve:

Statistical Indicators

• RMS (Root Mean Square): Measure of fit quality. Lower is better, but compare only within the same dataset
• Reduced χ²: If available, values near 1.0 indicate good fit. χ² ≫ 1 suggests underestimated errors or poor fit
• Error bars: Display measurement uncertainties. Scatter should be consistent with error bars

7.5 Period Uncertainty

Canopus may report formal period uncertainty, but consider:

• Statistical uncertainty: From the curvature of the RMS minimum
• Systematic uncertainty: From observing baseline
• Longer time baseline → better period precision
• Rule of thumb: σ_P ≈ P²/T where T is observing baseline

8. Phase 6: Data Export & Reporting

8.1 Exporting the Lightcurve

Canopus provides several export options:

Lightcurve Plot

Use Print/Export to save the phased lightcurve as:

• Image file (PNG, JPEG) for publications
• PDF for reports

Data Tables

Export raw photometry as:

• CSV or TXT for custom analysis
• Includes: time (HJD/JD), magnitude, error, phase

Period Spectrum

Save the period spectrum to document your period search

8.2 ALCDEF Format

What is ALCDEF?

ALCDEF (Asteroid Lightcurve Data Exchange Format) is the international standard for reporting asteroid photometry to the Minor Planet Center. It ensures:

• Consistent data format across observers
• Machine-readable for database ingestion
• Metadata completeness for scientific utility

Generating ALCDEF Files

1. In the Lightcurve Analysis window: File → Export → ALCDEF
2. Canopus auto-populates most fields from session metadata:
   • Object designation
   • Observer information
   • Filter
   • Comparison star details
   • Photometry parameters
   • Observation dates/times
3. Verify all required fields are complete
4. Save the .txt file

Required ALCDEF Metadata

Ensure these are correctly entered:

• Object: Proper MPC designation
• Observers: Names (first initial + last name)
• Filter: Must use standard notation (V, R, I, Clear, etc.)
• Magband: Photometric system (usually same as filter)
• Observatory: MPC code (if assigned) or coordinates
• Comparison stars: RA/Dec and catalog magnitudes
• Exposure time
• Cycle time: Total time including readout

8.3 Publication Checklist

When reporting lightcurve results (CBET, journal article, etc.), include:

1. Rotation Period: P = X.XXX ± 0.XXX hours
2. Amplitude: A = X.XX ± 0.XX magnitudes
3. Observing Dates: HJD or calendar dates of observations
4. Phase Coverage: Fraction of rotation period observed
5. Photometric System: Filter and comparison methodology
6. Instrumentation: Telescope aperture, camera model
7. Phased Lightcurve Plot
8. Period Spectrum: Demonstrates uniqueness of solution
9. Data Quality: RMS scatter, number of images, exposure times

9. Troubleshooting Common Issues

9.1 Noisy or Scattered Lightcurve

Symptom: Data points widely scattered around fitted curve; high RMS

Possible Causes & Solutions:

1. Variable comparison star:
   • Plot comp stars against each other (Canopus has this feature)
   • If one deviates, remove it from the ensemble
   • Re-measure with revised comp star set
2. Aperture too large:
   • Captures excessive sky noise
   • Reduce aperture to 1.5× FWHM
3. Aperture too small:
   • Loses flux, especially in varying seeing
   • Increase to 2.5× FWHM
4. Poor calibration:
   • Check dark/flat frame quality
   • Ensure flats are recent and match filter
5. Atmospheric variations:
   • Thin clouds, variable seeing
   • May be unavoidable; increase observing time
6. Image quality issues not caught during preprocessing:
   • Re-examine FWHM consistency (should vary <20%)
   • Check saturation levels and star trailing
   • Remove problematic frames and re-measure

9.2 Asteroid Not Tracking Properly

Symptom: Red aperture drifts off asteroid during measurement

Solutions:

1. Re-select endpoints:
   • Return to target selection
   • Click asteroid more precisely in first/last images
   • Use zoom feature for accuracy
2. Non-linear motion:
   • Long sessions (>6–8 hrs) may show curved paths
   • Break into 2–3 hour blocks
   • Measure separately, then combine lightcurves
3. Images out of time order:
   • Verify FITS headers have correct timestamps
   • Check file loading order in image list

9.3 No Clear Period Found

Symptom: Period spectrum shows no distinct minimum, or all RMS values similar

Possible Causes:

1. Insufficient data:
   • Need more images or longer time baseline
   • Aim for >1 complete rotation
2. Very slow rotator:
   • Period >24 hours requires multi-night observations
   • Extend search range to 48+ hours
3. Very fast rotator:
   • Period <2 hours (rare but possible for small asteroids)
   • Ensure time resolution is adequate
   • May require shorter exposures
4. Low amplitude:
   • Nearly spherical asteroids (A < 0.05 mag)
   • Signal buried in noise
   • Need higher photometric precision
5. Pole-on view:
   • Viewing asteroid down its rotation axis
   • Little to no brightness variation
   • Consider observing at different apparition

9.4 Comparison Stars Showing Variability

Symptom: When plotting comp stars against each other, one or more show trends or variations

Solutions:

1. Identify the variable star(s)
2. Remove from comparison ensemble
3. If possible, add new comparison stars to replace
4. Re-measure the session
5. Cross-check remaining comp stars against VSX catalog

9.5 Saturated Stars

Symptom: Canopus reports saturation warnings, or stars show flat-topped profiles

Solutions:

1. If asteroid is saturated:
   • Cannot be salvaged — must re-observe with shorter exposures
2. If only comp stars are saturated:
   • Select fainter comparison stars
   • Re-measure with new comp set
3. Prevention:
   • Pre-check saturation before long observing runs
   • Keep peak counts <60% of sensor full well

10. Advanced Topics

10.1 Multi-Night Observations

Combining data from multiple nights improves:

• Period precision (longer time baseline)
• Phase coverage
• Alias rejection (breaks daily cadence)

Procedure:

1. Create separate sessions for each night
2. Use identical comparison stars across nights (if possible)
3. In Lightcurve Analysis, load multiple sessions simultaneously
4. FALC will analyze combined dataset

Zero Point Shift Tool: Aligning Multi-Night Data

When combining observations from different nights, atmospheric conditions, instrument configuration, or comparison star availability may cause systematic magnitude offsets between datasets.

Using the Zero Point Shift Tool in Canopus:

1. Load multiple sessions into Lightcurve Analysis
2. Phase the data with your best-fit period
3. Navigate to: Analysis → Zero Point Adjustment (or similar menu item)
4. Canopus will display each session's data in different colors
5. Options for alignment:
   • Automatic: Canopus minimizes RMS by shifting each session vertically
   • Manual: You specify the offset for each session (in magnitudes)
   • Reference Session: Choose one session as the reference (zero shift); others are adjusted relative to it
6. Click Apply Shifts
7. Re-run FALC to generate combined fit

10.2 Composite Lightcurves

Combining data from different observers/telescopes:

• Each dataset must be internally consistent (same comp stars per dataset)
• Import data into Canopus as separate sessions
• Phase all data to common period
• May require magnitude offsets between datasets

10.3 Fast Rotators (Period < 2 hours)

Special considerations:

• Exposure time must be short (< 5% of period) to avoid rotational smearing
• Need many images per rotation (20+ recommended)
• Timing accuracy becomes critical
• Verify FITS timestamp precision (sub-second accuracy needed)

10.4 Sparse Data and Incomplete Coverage

If phase coverage is poor:

• Period determination becomes less certain
• May have multiple equally good solutions
• Amplitude measurement is unreliable
• Additional observations recommended before publication

10.5 Shape and Pole Modeling

High-quality lightcurves from multiple apparitions enable:

• Convex inversion (convex inversion methods)
• Spin pole determination
• This is beyond Canopus scope — requires specialized software
• Examples: KOALA, DAMIT database

For these studies, maximize:

• Number of apparitions (different viewing geometries)
• Phase coverage per apparition
• Photometric precision (<0.02 mag RMS)

11. Quick Reference Checklist

Use this checklist to ensure you've completed all critical steps:

12. Additional Resources

12.1 Software & Tools

• MPO Canopus: https://minorplanetobserver.com/
• DS9 Image Viewer: https://sites.google.com/cfa.harvard.edu/saoimageds9
• Astrometrica: http://www.astrometrica.at/ (plate solving)

12.2 Catalogs & Databases

• Minor Planet Center: https://minorplanetcenter.net/
• VSX (Variable Star Index): https://www.aavso.org/vsx/
• APASS (photometric catalog): https://www.aavso.org/apass
• LCDB (Lightcurve Database): https://alcdef.org/
• Gaia Archive: https://gea.esac.esa.int/archive/

12.3 Educational Materials

• ALCDEF Standard: https://alcdef.org/alcdef-standard
• MPO Canopus Manual: Included with software installation
• Asteroid Photometry Handbook: Available from MPO website

12.4 Scientific Background

• Harris et al., "Photoelectric Observations of Asteroids," Icarus (various)
• Pravec et al., "Asteroid Rotations," chapter in Asteroids IV (2015)
• Warner et al., "The Asteroid Lightcurve Database," Icarus 202, 134 (2009)