Code to compute coadds for Lya-forest data in BOSS and eBOSS.
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Searching for quasars ======= Code:
mpirun -np 4 run_catalog.py
The calculations and main functions are located at:
qso_catalog.py
Python libraries you may need: fitsio, pandas, mpi4py, healpy. I am using Python 3.5.2.
- It goes through the spAll-v5_10_0.fits file and filter all qsos within BOSS, EBOSS (filtering conditions can be easily modified). (the full 12GB file can be read, however to save some time we are using a subset, subset_spAll, with only the columns we are interested on)
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Filter the Quasars that satisfy the the bit condition:
('Quasars with Bit condition in EBOSS_TARGET1 =', 201742)
('Quasars with Bit condition in EBOSS_TARGET0 =', 54108)
('Quasars with Bit condition in BOSS_TARGET1 =' , 466098)
Total qsos = 3008000
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Filter the Quasars that satisfy the condition 'CLASS== "QSO" & (OBJTYPE=="QSO" | ''OBJTYPE=="NA".) & THING_ID != -1'
('Quasars with General condition in EBOSS_TARGET1 =', 160854)
('Quasars with General condition in EBOSS_TARGET0 =', 38164)
('Quasars with General condition in BOSS_TARGET1 =' , 285071)
Total qsos = 610184
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Filtering by using both conditions, we have:
('Quasars with Both condition in EBOSS_TARGET1 =', 160854)
('Quasars with Both condition in EBOSS_TARGET0 =', 38164)
('Quasars with Both condition in BOSS_TARGET1 = ' , 285071)
Total qsos = 484089
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The names of the files that contain the 484089 spectra corresponding to these quasars are located at SpAll_files.csv, (use Qsos.write_names = True)
- Once filtered and selected the Qsos, we compute the healpix given RA and DEC. Then group quasar observations given by healpix. i.e.
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Withing a healpix, finds the repeated objects with the same THING_ID (i.e. self.rep_thid = 4), Coadd them (average ivar*flux.) and compute the chisq with respect to zero flux:
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Eliminate those observations with Chisq< 2 (trim_chisq = 2) to neglect noise, i.e.
- Write a file for each healpix that contains THING_ID's. For each THING_ID coadd 'flux'. For example, considering the Healpix 24985, there are three different THING_ID with their corresponding coadd, ivar, and_mask and or_mask:
- The master file contains the information from all accepted spectra: healpix, thing_id, mjd, plate, fiberid, etc..
- The figure shows the distribution of THING_ID. The x-axis displays the number spectra per THING_ID, while the y-axis the number of Accepted spectra within a THING_ID. Blue rectangles represent the total of THING_IDs, and the top label the total of spectra. For example, let's consider the case with #Specs per THING_ID = 18. there are two THING_IDs with 18 spectra, one has only 16 accepted and the other has all accepted (18), given a total of 34 accepted spectra.
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Additional info may be found here
upstream/master