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A search for Miras in M33 S. He (stat.), W.Yuan (astro.) June 16, 2015 Indo-US Branch Collaborators: James Long (stat.) Jianhua Huang (stat.)

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Presentation on theme: "A search for Miras in M33 S. He (stat.), W.Yuan (astro.) June 16, 2015 Indo-US Branch Collaborators: James Long (stat.) Jianhua Huang (stat.)"— Presentation transcript:

1 A search for Miras in M33 S. He (stat.), W.Yuan (astro.) June 16, 2015 Indo-US Workshop@Cook’s Branch Collaborators: James Long (stat.) Jianhua Huang (stat.) Lucas Macri (astro.) Nice Cover

2 Outline  M33 observations  Search Miras using CLEAN algorithm  Search Miras using Gaussian Process  Results

3 What are Miras?  Aged stars: red giants  They are pulsating  Period > 100 days  V > 2.5 magnitude Percy, John. Understanding Variable Stars. Cambridge. 2007. Print.

4 What are Miras? I. Soszynski (2009)  Over 1600 Miras in LMC are well observed by the OGLE project  Not strictly periodic, but with chaotic long term variation  Fortunately most of them do not show a phase shift during the several years’ observations

5 What are Miras?  Amplitude (or 2*A in physics) > 0.8 mag in I-band  Period > 100 days, and up to 1400 days

6 Observations of M33 Image credit: Andrew Z. Colvin  M33 (Triangulum Galaxy, NGC 598), is one of the Milky Way neighbors with a distance about 0.8 Mpc.  It is a spiral galaxy with inclination 54 o, and suitable for variable search/distance calibration.

7 Observations of M33 + WIYN Adapted from Pellerin & Macri (2011) DSS image of M33 7-year range Observations (multi-band):  DIRECT project (29 fields): F. L. Whipple Observatory 1.2m Michigan-Dartmouth-MIT 1.3m  Wisconsin-Indiana-Yale- NOAO Observatory (WIYN) 3.5m Aimed to searching Cepheids and Long Period Variables (LPVs).

8 Observations of M33  In 2011, Anne Pellerin and Lucas Macri finished the Cepheids search.  We use the reduced data from their photometry and continue the LPVs search.

9 Prepare the Data Bad observations might be due to:  Bad weather  Mismatch between frames  Photometry artifacts  …

10 Prepare the Data Measurement uncertainty as a function of magnitude and observation date (V-band, field-c) And there are observations with very large uncertainties. To get rid of bad observations, we fitted the error magnitude relation for each night/field with a function of 3 free parameters. σ – I relation for field-a on MJD = 2558 Grey points are labeled as bad observations

11 Prepare the Data Besides the grey points, we also removed the night/field with: (B 0.1)

12 Prepare the Data σ – I relation for all the kept observations Histogram of number of good observations for each single object. We do bother those objects with N obs < 20 in the following analysis.

13 CLEAN Search We performed the CLEAN algorithm (Hogbom, 1974) using VARTOOLS (Hartman et al., 2008) to search the possible periods for all the objects with Stetson index (Stetson, 1996) J>0.75 The CLEAN algorithm takes into account the observation patterns to avoid alias periods and ‘clean’ the frequency spectrum up, only leaving few peaks. (1) For each object, we find 4 top possible periods, no matter they are variables or not: Largest phase gap for period1 Cycles covered for period2

14 CLEAN Search (2) Adopt p1 as detected period, fold the observations in phase space, then fit the function: With a 0, a 1, a 2, and φ as free parameters. Then we obtain a new table:

15 CLEAN Search (3) Perform the same calculation on simulated light curves, which is down sampled from LMC observations and with noise added.  3 periods are given by the OGLE catalog to characterize the short-term and long-term variation.  Left panel: Histogram of dP / P o for P o = Primary True Period. (Spike at -1 are those failed to detect a period)  Right panel: Same as left but with P o = One of the 3 true periods that most close to detected period

16 CLEAN Search (4) Make cut based on simulated results.  Left panel: dP / P o vs. number of observations. 2D histogram are in log scale.  Right panel: Recover rate (|dP/P o |<0.05) for each bin of number of observations.  N obs >= 20 are applied on M33 real light curves.

17 CLEAN Search Also make cuts based on other parameters:

18 CLEAN Search (5) After cut, visually check the best-fit plots and label them as variable or non-variable. Results:  http://people.physics.tamu.edu/yuanwenlong/m33_update_temp/pngs/pngs-flag-1/ http://people.physics.tamu.edu/yuanwenlong/m33_update_temp/pngs/pngs-flag-1/  http://people.physics.tamu.edu/yuanwenlong/m33_update_temp/pngs/pngs-flag-2/ http://people.physics.tamu.edu/yuanwenlong/m33_update_temp/pngs/pngs-flag-2/  http://people.physics.tamu.edu/yuanwenlong/m33_update_temp/pngs/pngs-flag-3/ http://people.physics.tamu.edu/yuanwenlong/m33_update_temp/pngs/pngs-flag-3/  http://people.physics.tamu.edu/yuanwenlong/m33_update_temp/pngs/pngs-flag-0/ http://people.physics.tamu.edu/yuanwenlong/m33_update_temp/pngs/pngs-flag-0/  http://people.physics.tamu.edu/yuanwenlong/m33_update_temp/pngs/pngs-flag-99/ http://people.physics.tamu.edu/yuanwenlong/m33_update_temp/pngs/pngs-flag-99/ Flag=1 Flag=2 Flag=3Flag=0

19 Simulate light curves 1)Fit LMC Mira observations with GP-fit model 2)Pick one M33 light curve, shift MJD’ = MJD + Tshift (random) 3)Obtain LMC best-fit I-band magnitudes at MJD’, then add 6.2 magnitude 4)Calculate measurement uncertainties based on σ – I relation for that night that field 5)Add Gaussian noise: I’ = Gaussian(I, σ) 6)Only keep those simulated light curves with max(σ) < 1.0 mag

20 Simulate light curves We also simulate some non-Mira light curves based on LMC Semi-Regular Variable observations: Method is similar except using smooth-spline instead of GP-fit model for step 1.

21 Simulate light curves σ – I relation for simulated data (left) and M33 observations (right).

22 Period Detection Perform the GP-spectrum model:  (1) All the M33 light curves (~100,000)  (2) Subsample of M33 that picked by clean method previously (~2,000)  (3) Fake Mira light curves (~3,000)  (4) Fake SRV light curves (several thousand) Input files Use GP-spectrum model to derive frequency spectra 1600 cores * 4 days Acknowledge to the Brazos cluster at TAMU Brazos Computational Resource Academy for Advanced Telecommunications and Learning Technologies Texas A&M University

23 Period Detection Results: More results:  http://people.physics.tamu.edu /yuanwenlong/m33_update_tem p/may28/index.php?type=all http://people.physics.tamu.edu /yuanwenlong/m33_update_tem p/may28/index.php?type=all  http://people.physics.tamu.edu /yuanwenlong/m33_update_tem p/may28/index.php?type=cln http://people.physics.tamu.edu /yuanwenlong/m33_update_tem p/may28/index.php?type=cln  http://people.physics.tamu.edu /yuanwenlong/m33_update_tem p/may28/index.php?type=Mira http://people.physics.tamu.edu /yuanwenlong/m33_update_tem p/may28/index.php?type=Mira  http://people.physics.tamu.edu /yuanwenlong/m33_update_tem p/may28/index.php?type=SRV http://people.physics.tamu.edu /yuanwenlong/m33_update_tem p/may28/index.php?type=SRV

24 Period Detection Performance on simulated light curves: Percentage of |Δf/f| < 0.1 71.5%14.6%4.5%

25 Period Detection Compare with other methods: MethodTotal lcf1 rightf2 rightf3 rightnot in f1-f3 Gaussian Process 29762128 (71.5%) 433 (14.5%) 135 (4.5%) 280 (9.4%) Lomb- Scargle 2976215 (7.2 %) 456 (15.3%) 414 (13.9%) 1891 (63.5%) CLEAN2976822 (27.6%) 464 (15.6%) 307 (10.3%) 1383 (46.5%)

26 Fit the Light Curves Adopt the highest peak of GP- spectrum as prior period  (1) All the M33 light curves (~100,000)  (2) Subsample of M33 that picked by clean method previously (~2,000)  (3) Fake Mira light curves (~3,000)  (4) Fake SRV light curves (several thousand) Input files Use the GP-fit model to fit the light curves Perform the GP-fit model:

27 Fit the Light Curves Results: One of the M33 objects in field-3 More results at: http://people.physics.t amu.edu/yuanwenlong/ test_script/pickmira/0/

28 Pick Miras (1)Only consider those with amplitudes > 0.8 mag. (>30,000 objects, including simulated ones) (2)Visually inspected all of them. (A lot human efforts) We calibrated ourselves before starting pick Miras.

29 Pick Miras Some Mira candidates:

30 Pick Miras Results: Mira candidates 2138 Simulated 627 Mira 627 SRV 0 M33 1511 Overlap with CLEAN 990 New 521 Discover rate < 627/2976 = 21% Error rate = 0/(627+0) = 0% Period correct 74.6%

31 Pick Miras Results: Possible Mira candidates 514 Simulated 192 Mira 191 SRV 1 M33 322 Overlap with CLEAN 115 New 207 Error rate = 1/(191+1) = 0.5% Joint discover rate < (627+192)/2976 = 27.5% Period correct 58.6%

32 Next Step Future work: (1)Classify O-rich Miras and C-rich Miras (2)Study the Period-Luminosity Relation at NIR

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