Exercise of data reduction for MOIRCS Multi-Object Spectroscopy

Transcription

1 1D spectrum (Goal for quick workers) Subaru Autumn School 2014, on September 2014 Exercise of data reduction for MOIRCS Multi-Object Spectroscopy Schedule on 25 th Sep. (as a guide) 10:10-12:00 (~2 hours): Step 1 Step 2 13:00-15:00 (2 hours): Step 3 Step 7 15:30-17:30 (2 hours): Step 8 Step 10 Target galaxy: MODS (Detector 1) Standard star: M53735 (Detector 2) Masao Hayashi (NAOJ) and Anna Ferré-Mateu (Subaru) This document is for helping you understand a basic procedure of MOIRCS MOS data reduction. You should read the handbook carefully to get the detail. UNIX commands pwd - shows the current location of the directory cd X - changes current directory to the directory X mkdir X - makes new directory named X ls (-lrt) - lists all the files/directories in the current directory (by date created) cp A B - copies the file/directory A to the file/directory B less file.txt - shows the contents of file.txt sed s/x/y/ file.txt - reads character strings in file.txt and exchanges the strings following the requirement X to the string Y IRAF epar X - edits parameters of the task X.exit - to quit mcsmdp Be careful not to overwrite the existing files when you run a task Be careful to avoid a typo when you type a command, or you may encounter an error message. Data used for this exercise Feel free to ask us when you have any problems. There is no problem with you going ahead rapidly if you can understand the individual processes by reading the handbook and this document by yourself. Raw data Reduced 2D spectrum (Goal for all)

2 Step 1 Preparation for the data reduction Setup your working directory $ mkdir /data/users/youraccount $ mkdir /data/users/youraccount/red (comments) (directory for data reduction) Getting the MOIRCS MOS sample data $ cd /data/users/youraccount/red $ cp /mfst01d/hayshiso/autumn14/mcsmdp_sample.tbz. $ tar jxf MCSMDP_sample.tbz (extraction of the *.tbz file) You can also get the sample data from the website as follows: $ wget Setup the software (MCSMDP) Add the following path in your ~/.bashrc: $ emacs ~/.bashrc (You can use any text editors that you like) source /data/local/subaru/mcsmdp_v1_1_3/etc/mcsmdp.sh To enable the change of ~/.bashrc $ source ~/.bashrc Starting of MCSMDP $ cd /data/users/youraccount/red/mcsmdp_sample/ $ mcsmdp mkiraf? (yes): ( Enter) > mcsmdp (starting of ds9) Checking the data To make sure what kind of the data each frame is > hselect *.fits $I,OBS-MOD,DATA-TYP,OBJECT,DISPERSR,EXPTIME,DET-ID,K_DITCNT yes Keywords of the FITS header (see Table 2.2 in the manual) Making lists of the data processed simultaneously The frames of the same detector and the same nodding position are listed shows the requirement to list > hselect MCSA*.fits $I "OBJECT = 'DOMEFLAT' = 1" > flat1.lst > hselect MCSA*.fits $I "OBJECT = 'DOMEFLAT' = 2" > flat2.lst > hselect MCSA*.fits $I "OBJECT = 'CDFN_MASK02' = 1 & K_DITCNT = 1" > obj1a.lst > hselect MCSA*.fits $I "OBJECT = 'CDFN_MASK02' = 1 & K_DITCNT = 2" > obj1b.lst > hselect MCSA*.fits $I "OBJECT = 'CDFN_MASK02' = 2 & K_DITCNT = 1" > obj2a.lst > hselect MCSA*.fits $I "OBJECT = 'CDFN_MASK02' = 2 & K_DITCNT = 2" > obj2b.lst Check the lists and that they are created properly > ls > less flat1.lst (You should check all of the lists you created) DET-ID: Detector ID, 1 or 2 K_DITCNT: Nth number of nodding, 1 means A position and 2 means B position

3 Step 2 Flat fielding Combining all dome flat data (per Detector 1 or Detector 2) > epar imcombine (see section in the manual and make sure to set the proper parameters) > HK500_CDFN2_Domeflat1.fits > imarith HK500_CDFN2_Domeflat1.fits / HK500_CDFN2_Domeflat1.fits [Important parameters] combine, reject, scale, weight To better understand Why is median taken instead of average? Consider why it is divided by 10000? Try > histogram HK500_CDFN2_Domeflat1.fits Making lists for the resulting files >!sed 's/ (.* )/fl 1/' obj1a.lst > flobj1a.lst >!sed 's/ (.* )/fl 1/' obj1b.lst > flobj1b.lst ( means a backslash) Flat fielding > / > / Check the resulting files! Step 3 Interpolation of bad pixels Getting a bad pixel mask from the database (MDPDB) Name of the bad pixel mask for MCSA***.fits : MCSA***.pl > mkdir BPM (the directory where bad pixel masks for each frames are stored) >!sed 's/ (.* ).fits/bpm / 1.pl/' obj1a.lst > bpm1a.lst (lists of name of output files) >!sed 's/ (.* ).fits/bpm / 1.pl/' obj1b.lst > bpm1b.lst > imcopy mdpdb$bpm/nlbpm1_ff64r.fits,mdpdb$bpm/nlbpm1_ff64r.fits,mdpdb$bpm/nlbpm1_ff64r.fits, (this task must be written in one line) > imcopy mdpdb$bpm/nlbpm1_ff64r.fits,mdpdb$bpm/nlbpm1_ff64r.fits,mdpdb$bpm/nlbpm1_ff64r.fits, (this task must be written in one line) Check the resulting files! $ ls BPM to see the files in the directory BPM Fixing bad pixels specified by the BPM mask >!sed 's/ (.* )/cr 1/' flobj1a.lst > crobj1a.lst (lists of name of output files) >!sed 's/ (.* )/cr 1/' flobj1b.lst > crobj1b.lst (copy the frames before fixing the pixels) > epar fixpix (edit parameters of the task, fixpix) [Important parameters] cinterp, linterp You would notice that there are a lot of hot pixels left. So, there is still a room to improve this process. Check the resulting files! Consider how you can improve this process.

4 Step 4 Cosmic rays This process is skipped this time, because it is not significant for the data we are now analyzing. You should be able to realize this by blinking the several consecutive frames as described in section of the manual. However, in general, it is better to remove cosmic rays on each frame. Step 5 A-B sky subtraction Subtracting the background by comparing two consecutive frames >!sed 's/ (.* )/ab 1/' crobj1a.lst > abobj1a.lst > - slit object A B A-B sky background A B A-B Consider why the positive/negative OH sky lines are still seen in the A-B frames? The sky background may be subtracted better by more frames (i.e, 3 or more) being used in this process. Consider how you can/may improve this process. Step 6 Distortion correction Correcting the spectra from spatial and spectral distortions (especially for those spectra near the edge). You have to use the database files corresponding to the date the data was obtained (see MCSRED for other database dates). >!sed 's/ (.* )/gc 1/' abobj1a.lst > gcobj1a.lst > epar geotran parameter: database mdpdb$geomap/mcsdistcrr1_feb07new.dbs mcsdistcrr1_feb07new.gmp parameter: transforms 2D spectrum Before the correction After the correction Check the resulting files! The process, grism rotation correction, is skipped this time, because it is not significant for the data. Step 7 Extraction of individual 2D spectra > maskplot CDFN_MASK02.mdp image=gcabcrflmcsa fits raw+ >!sed 's/ (.* ).fits/ 1_MODS fits/' gcobj1a.lst > gcmods lst >!sed 's/ (.* ).fits/ 1_MODS _rot.fits/' gcobj1a.lst > gcmods _rot.lst >!sed 's/ (.* )/ 1[*,1755:1840]/' gcobj1a.lst > cut.lst X range: all (you can extract the specific X range as well) Y range: from 1755 pixel to 1840 pixel (check the image visually!) (extract the 2D spectrum of the target object) (Detector 1: wavelength with x, so rotate the frames by 180 degree) wavelength x-coordinates

5 wavelength [A] y-coordinates Step 8 Wavelength calibration Preparing the spectra without the A-B subtraction to use OH sky lines for the calibration First, distortion correction is performed for the frames where the sky is not subtracted yet. >!sed 's/ (.* )/gcsky 1/' crobj1a.lst > gcsky1a.lst mdpdb$geomap/mcsdistcrr1_feb07new.dbs mcsdistcrr1_feb07new.gmp Next, the spectrum is extracted. This is the same process as the step-7, but for A -spectrum, not A-B -spectrum. >!sed 's/ (.* ).fits/ 1_MODS fits/' gcsky1a.lst > gcskymods lst >!sed 's/ (.* ).fits/ 1_MODS _rot.fits/' gcsky1a.lst > gcskymods _rot.lst >!sed 's/ (.* )/ 1[*,1755:1840]/' gcsky1a.lst > cut.lst Compare the spectra with those obtained in the Step-7. Can you see the strong OH sky lines on the 2D spectrum? Identifying the OH sky lines to determine the relation between the X-coordinates and wavelength Please read section of the handbook carefully and understand what is preformed in this process. (The figure 4.1 of the manual is provided in a paper apart.) > epar identify > identify gcskycrflmcsa _mods fits nsum section = middle line fwidth x-coordinates wavelength(x): wavelength as a function of x at a given y-coordinate Extract 1D spectrum along the line In the case that the fitting function is linear, function, order residual x [pixel] Fig. 4.2 of the handbook Command keys used frequently: m, f, l Figure similar to Fig. 4.3 of the handbook Command keys used frequently: d, f [Important parameters] section, nsum, fwidth, function, order Identifying the OH sky lines at different y-coordinates as well At this stage, y-coordinates can correspond to different wavelengths at a given x-coordinate. parameter: reference > epar reidentify > reidentify gcskycrflmcsa _mods fits gcskycrflmcsa _mods fits [Important parameters] trace, step parameter: images

6 y-coordinates 1D spectrum wavelength(x) at y3 nsum step 1D spectrum wavelength(x) at y2 fwidth x-coordinates 1D spectrum wavelength(x) at y1 Deriving the relation between the wavelength and (x,y) coordinates wavelength1(x) at y1 wavelength2(x) at y2 wavelength3(x) at y3 wavelength?(x) at y? wavelength(x, y) > epar fitcoords > fitcoords gcskycrflmcsa _mods [Important parameters] function, xorder, yorder Check the fitting results. Type xxyy and r, or type xxyr and r (see Table 4.3 of the handbook) Transforming the spectrum based on the database of the wavelength as a func. of (x,y) Here, we assume that the relation between wavelength and (x,y) derived for a given frame is applicable to the other frames as well. However, in general, the assumption is not always true. > epar transform >!sed 's/ (.* )/tr 1/' gcmods lst > trmods lst gcskycrflmcsa _mods (the name of the database) To check the transformed spectrum > mdpdisplay gcabcrflmcsa _mods fits frame=1 > mdpdisplay trgcabcrflmcsa _mods fits frame=2

7 Step 9 Removal of residual sky emission Subtracting the residual sky emission by fitting the background >!sed 's/ (.* )/bg 1/' trmods lst > bgmods lst > epar background ( q and then return to exit) [Important parameters] axis, function, order Step 10 Combining all of the spectra Shifting the spectra by the nodding length the nodding length > "$I,K_DITWID" yes (check the nodding length) >!sed 's/ (.* )/sh 1/' bgmods lst > shmods lst (26 = 3 / 0.117, 1pixel=0.117 ) A-B Inverting the negative value into the positive one >!sed 's/ (.* )/ng 1/' shmods lst > ngmods lst > * Combining the spectra > epar imcombine > HK500_MODS A A bg bg A+B A Shift B + B B Combine bg+ng sh Invert ng A B B A Now, you obtained the 2-D spectrum! The reduction is nearly completed.

8 Step 11 Reduction of spectrum of a standard star The spectrum of a standard star is used for flux calibration and telluric correction. Standard star: M53735 The spectrum was taken on the detector 2. ## flat > epar imcombine > HK500_CDFN2_Domeflat2.fits > imarith HK500_CDFN2_Domeflat2.fits / HK500_CDFN2_Domeflat2.fits > imarith MCSA fits / HK500_CDFN2_Domeflat2.fits flmcsa fits > imarith MCSA fits / HK500_CDFN2_Domeflat2.fits flmcsa fits ## bad pixel > imcopy mdpdb$bpm/nlbpm2_ff64r.fits BPM/MCSA pl > imcopy mdpdb$bpm/nlbpm2_ff64r.fits BPM/MCSA pl > imcopy flmcsa fits crflmcsa fits > imcopy flmcsa fits crflmcsa fits > epar fixpix > fixpix crflmcsa fits BPM/MCSA pl > fixpix crflmcsa fits BPM/MCSA pl ## A-B sky subtraction > imarith crflmcsa fits - crflmcsa fits abcrflmcsa fits ## distortion > epar geotran > geotran abcrflmcsa fits gcabcrflmcsa fits mdpdb$geomap/mcsdistcrr2_feb07new.dbs mcsdistcrr2_feb07new.gmp ## extraction of a slit > maskplot CDFN_MASK02.mdp image=gcabcrflmcsa fits raw+ > imcopy gcabcrflmcsa fits[*,902:1022] gcabcrflmcsa _m53735.fits ## wavelengh calibration > geotran crflmcsa fits gcskycrflmcsa fits mdpdb$geomap/mcsdistcrr2_feb07new.dbs mcsdistcrr2_feb07new.gmp > imcopy gcskycrflmcsa fits[*,902:1022] gcskycrflmcsa _m53735.fits > epar identify > identify gcskycrflmcsa _m53735.fits > epar reidentify > reidentify gcskycrflmcsa _m53735.fits gcskycrflmcsa _m53735.fits > epar fitcoords > fitcoords gcskycrflmcsa _m53735 > epar transform > transform gcabcrflmcsa _m53735.fits trgcabcrflmcsa _m53735.fits gccrflmcsa _m53735 > mdpdisplay gcabcrflmcsa _m53735.fits frame=1 > mdpdisplay trgcabcrflmcsa _m53735.fits frame=2 ## residual background subtraction > epar background > background trgcabcrflmcsa _m53735.fits bgtrgcabcrflmcsa _m53735.fits

9 ## A+B combine > hselect bgtrgcabcrflmcsa _m53735.fits "$I,K_DITWID" yes > imshift bgtrgcabcrflmcsa _m53735.fits shbgtrgcabcrflmcsa _m53735.fits 0 43 > imarith shbgtrgcabcrflmcsa _m53735.fits * -1 ngshbgtrgcabcrflmcsa _m53735.fits > epar imcombine > imcombine bgtrgcabcrflmcsa _m53735.fits,ngshbgtrgcabcrflmcsa _m53735.fits HK500_M53735.fits Extracting the 1D spectrum of the standard star Please read section 4.6 of the handbook carefully and understand what is preformed in this task. ## apall > apall HK500_M53735.fits Step 12 Flux calibration and telluric correction Please read section 4.6 of the handbook carefully and understand what is preformed in this process. > epar rescurve > rescurve HK500_M53735.ms.fits resc_cdfn2_hk500.fits > mdpfcalib HK500_MODS fits resc_cdfn2_hk500.fits HK500_MODS fl.fits Detection of emission line The reduction is completed!!

10 Step 13 Data anaysis For example, Measurement of redshift of the target galaxy > splot HK500_MODS fl.fits 41 :nsum 5 the region [*,39:43] is used to plot the spectrum, i.e 5 pixels around 41 w and then x zoom the plot in x w and then c zoom the plot around the cursor k in the left side of emission line and then k in the right profile fitting Consider what is the redshift of this target galaxy, given that this line is Hα? Measurement of luminosity of the emission line Estimation of star formation rate of this galaxy And so on Enjoy the analysis with near-infrared spectra taken by MOIRCS MOS!!