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1 Automated Extraction of Photometric Data: A demonstration project using MaximDL on CV Images Jerry Horne San Jose, California USA AAVSO Spring Meeting.

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Presentation on theme: "1 Automated Extraction of Photometric Data: A demonstration project using MaximDL on CV Images Jerry Horne San Jose, California USA AAVSO Spring Meeting."— Presentation transcript:

1 1 Automated Extraction of Photometric Data: A demonstration project using MaximDL on CV Images Jerry Horne San Jose, California USA AAVSO Spring Meeting 5-6 May 2006

2 AAVSO Spring Meeting 06Automated Extraction2 Contents Abstract Background Concept Design The Software Some Results Conclusions

3 AAVSO Spring Meeting 06Automated Extraction3 Abstract Automated Extraction of Photometric Data: A demonstration project using MaximDL on CV images The intrinsic photometric functions and scripting capability of the image processing software MaximDL is used to automate the extraction of photometric data from images of Cataclysmic Variable stars using standard AAVSO comparison stars. The resulting photometric data is then formatted for inclusion in the AAVSO variable star database. This automated technique is compared with manual data extraction methods and other photometric software.

4 AAVSO Spring Meeting 06Automated Extraction4 Background Multiple Image Processing and Photometry programs available: –MaxIm DL, AIP4Win, Mira, Astro Art, IRAF, Canopus, AstroMB, xPhot… All have varying ability to process single or multiple images, then extract photometric data, and: –Produce light curves –Output data files that can be further analyzed by other programs

5 AAVSO Spring Meeting 06Automated Extraction5 Background No single program or tool that –Computes the magnitude of AAVSO program stars –Using data on comparison stars on AAVSO charts –To produce formatted output ready for inclusion in the data base, via Web Obs or PC Obs Problem: How to reduce the labor required to extract data from images taken the night before and send the data off to AAVSO Hq.

6 AAVSO Spring Meeting 06Automated Extraction6 Need a Specialized Software Tool Fill the Gap between: And:

7 AAVSO Spring Meeting 06Automated Extraction7 Concept How to go from:(with data from): To: CY LYR CCDV 125,139, FHJZ Err: 0.03 By clicking on:

8 AAVSO Spring Meeting 06Automated Extraction8 Software Design Main Requirements: 1) A programmable interface to an existing Image Processing Software program 2)Ability to load and align images 3)Find & Store Magnitude and stellar position data

9 AAVSO Spring Meeting 06Automated Extraction9 Software Design Main Requirements (continued): 4)Ability to Identify specific stars on an image 5)Ability to measure intensities and SNR 6)Perform calculations and format data

10 AAVSO Spring Meeting 06Automated Extraction10 Design (continued) Programmable interface possibilities for MaximDL – Windows Scripting Language (VBS) – Visual Basic or Visual C++ stand-alone program – VB or Visual C++ plug-in VB stand-alone program chosen –Ease and speed of development –MaximDL data structure reasons

11 AAVSO Spring Meeting 06Automated Extraction11 Design (continued) Ability to Load and Align Images – MaximDL provides software access to standard FITS load functions and image align routines Find/Store Magnitude and Position Data –MaximDL contains intrinsic tools to obtain X & Y position data for stars in the image –Input magnitude data from AAVSO Charts

12 AAVSO Spring Meeting 06Automated Extraction12 Design (continued) Ability to Identify Specific Stars –Two possible methods: 1)Astrometrically solve a new image using a large star catalog such as GSC 2)Align a new image to a reference image of the star field where the positions of stars of interest have already been identified. –#2 is easier and faster

13 AAVSO Spring Meeting 06Automated Extraction13 Design (continued) Align New Image to Reference Image: ReferenceNew

14 AAVSO Spring Meeting 06Automated Extraction14 Design (continued) Ability to measure intensities and SNR –Internal MaximDL functions: –Document.CalcInformation( X, Y[, Rings ]) Integrated intensity of star image in aperture Signal to Noise ratio of star image with respect to the background –Rings settings (Aperture, Gap, Annulus)

15 AAVSO Spring Meeting 06Automated Extraction15 Design (continued) Perform calculations and format data Multiple calculation methods for differential photometry : 1)Basic V-C: the magnitude of the variable found by using a single comparison star: V = (v – c) o + C { e.g. V = } Also use of a check star to gauge accuracy: (K – C) o =? (K – C) s

16 AAVSO Spring Meeting 06Automated Extraction16 Design (continued) Methods of calculation - continued 2)Average = Mean of variable magnitudes found using each comparison star : V i = (v – c) i + C i { e.g. V i = } Then: n V = ( V i ) / i {e.g. V = ( ) / 3 } i=1

17 AAVSO Spring Meeting 06Automated Extraction17 Design (continued) Methods of calculation - continued 3)Biased Mean = Mean of variable magnitude using the results from selected comparison stars - using comparison stars closest in magnitude to the variable: a) Perform V-C Calculation, for example, using C1 ( 12.5 ) {16.3 = } b) Since variables magnitude is faint, go back and use the fainter comparison stars (C3 = 15.6, C4 = 16.0, C5 =16.4) for the calculation : { e.g. V = ) / 3 = 16.4}

18 AAVSO Spring Meeting 06Automated Extraction18 Design (continued) Methods of calculations - continued 4)Weighted Mean - weight the average by the inverse of the standard errors: n V w = (V i / i ) * 1/(1/ i + 1/ i+1 …+ 1/ n ) i=1 where i = individual error and V i = the individual calculated magnitudes from each comparison star

19 AAVSO Spring Meeting 06Automated Extraction19 Design (continued) Methods of calculations - continued 5)Aggregate – combining all comparison star intensities and magnitudes to form a virtual star to compare with the variable (also called ensemble, composite, master star) : n C (total) = ( -2.5)Log 10 ( 10 (-C i /2.5) ) { sum comparison magnitudes} i =1 n I (total) = I i {sum intensities} i =1 Then: V agg = -2.5 Log 10 (I v /I (total) ) + C (total) { find var mag}

20 AAVSO Spring Meeting 06Automated Extraction20 Design (continued) Methods of calculations - continued 6)Ensemble – (Inhomogenous Exposures - Honeycutt, 1992) combining all comparison star intensities and magnitudes from multiple images using a sophisticated weighting technique to form a reference frame to measure all stars against: m(e, s) = m0(s) + em(e) {instrumental mag} ee ss = [m(e, s) - m0(s) em(e)] 2 w(e,s){least sqrs} e=1 s=1

21 AAVSO Spring Meeting 06Automated Extraction21 Format Data Format for output is specified on an AAVSO webpage: Column # Design. Name Julian Date Magn. CommentStep Mag Charts Init.Remarks Codes or Comp Stars xxxx+xxxnnnnnnnnnnxxxxxxx.xxxx

22 AAVSO Spring Meeting 06Automated Extraction22 The Software Start:

23 AAVSO Spring Meeting 06Automated Extraction23 The Software Analysis Panel with Maxim DL: Maxim DL is started when tool starts

24 AAVSO Spring Meeting 06Automated Extraction24 The Software Analysis Panel: Expand Analysis Panel

25 AAVSO Spring Meeting 06Automated Extraction25 The Software Extended Main Panel: Load Photometry Data

26 AAVSO Spring Meeting 06Automated Extraction26 The Software Load Star Data: Select File to Load

27 AAVSO Spring Meeting 06Automated Extraction27 The Software Star Data Loaded, showing variable star info: Variable and Position DataMultiple SequencesObserver and Comments Reference Files

28 AAVSO Spring Meeting 06Automated Extraction28 The Software Star Data Loaded, showing C1 star info: Comparison Magnitude and Position Data

29 AAVSO Spring Meeting 06Automated Extraction29 The Software Edit Set-Up information: Photometry SettingsCalculation Types Set SNR

30 AAVSO Spring Meeting 06Automated Extraction30 The Software Choose Image Files:

31 AAVSO Spring Meeting 06Automated Extraction31 The Software Ready for Analysis: Image Files SetClick on Analyze

32 AAVSO Spring Meeting 06Automated Extraction32 The Software Analysis in Progress: Analysis Log Images Loaded and Aligned with Reference Image

33 AAVSO Spring Meeting 06Automated Extraction33 The Software Analysis Complete: Save to FileScroll through Results Maximize Log

34 AAVSO Spring Meeting 06Automated Extraction34 The Software Analysis Log: Each selected calculation is displayed Selecting fainter comparison stars Variable not detected

35 AAVSO Spring Meeting 06Automated Extraction35 The Software Analysis Log: (continued) Signal-to-noise values known K - C minus observed K-C

36 AAVSO Spring Meeting 06Automated Extraction36 The Software Analysis Log: (continued) Comparison of Selected Calculation Methods

37 AAVSO Spring Meeting 06Automated Extraction37 The Software Marking Comparison and Variable Star Positions: Ref. File Loaded Select Seq to Mark Click on Mark Button

38 AAVSO Spring Meeting 06Automated Extraction38 The Software Marking Comparison and Variable Star Positions (continued): Set Star Positions Enter Mag for Comps Click Done Maxim DL Info Panel

39 AAVSO Spring Meeting 06Automated Extraction39 The Software Marking Comparison and Variable Star Positions (continued): Marked Position carried over to analysis panelSave Data

40 AAVSO Spring Meeting 06Automated Extraction40 Some Comparisons Tool vs AIP4Win : Used approximately 50 observations CY & AY Lyr: Bias Mean (AIP – Tool) –Mean Difference = 0.01, Std Dev = 0.11 Aggregate (AIP – Tool) –Mean Difference = 0.03, Std Dev = 0.11 Average (AIP – Tool) –Mean Difference = 0.00, Std Dev = 0.13

41 AAVSO Spring Meeting 06Automated Extraction41 The Software Limitations and Notes: Software assumes Image Files are fully processed beforehand (Flat, Bias, Dark) Reference and Image Files must be the same scale –If you change your f-ratio, you must take new reference images The 0.1 mag values of many AAVSO charts obviously limits the accuracy that could be achieved.

42 AAVSO Spring Meeting 06Automated Extraction42 Conclusions This is a demonstration piece of software –Different techniques or algorithms could have been used. –It does seem to provide a straight-forward method of obtaining photometric data. As always, the photometric results are only as good as the images. –It cannot pull good data out of bad images –Each observer must evaluate the results in terms of the errors, consistency, and overall image quality.

43 AAVSO Spring Meeting 06Automated Extraction43 References 1. Berry, R., Burnell, J. 2005, The Handbook of Astronomical Image Processing, 2 nd Edition. 2. Crawford, T., 2006, JAAVSO Submission 3. Honeycutt, K., 1992, PASP 104, Kundik, T. et al, 1995, Astrophys. J., 455, L5-L8 5. Percy, J, Kolin, D., 2000, PASP 112,

44 AAVSO Spring Meeting 06Automated Extraction44 Questions?

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