1 / 6

Distribution of Maximal Luminosities of Galaxies in SDSS

Distribution of Maximal Luminosities of Galaxies in SDSS. Manuchehr Taghizadeh-Popp (JHU) Zoltán Rácz, Alex Szalay, Katalin Ozogany, Enikö Regös. Extreme Value Statistics (EVS) -Used in calculation of risk, stock market, structural failures, insurance claims, flooding, hurricanes…

neveah
Download Presentation

Distribution of Maximal Luminosities of Galaxies in SDSS

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Distribution of Maximal Luminosities of Galaxies in SDSS Manuchehr Taghizadeh-Popp (JHU) Zoltán Rácz, Alex Szalay, Katalin Ozogany, Enikö Regös

  2. Extreme Value Statistics (EVS) -Used in calculation of risk, stock market, structural failures, insurance claims, flooding, hurricanes… -Distribution known for the extremes of N i.i.d. random variables (of parent distribution P(x) ) when N  ∞. 3 Universal distributions depending on tail behavior of parent distribution P(x): (1) P(x) ~ x^(-γ-1) (power law tail) ζ > 0 (Frechet distribution) (2) P(x) ~ exp(-x^a) (exponential tail) ζ = 0 (Gumbel distribution) (3) P(x) ~ (x0-x)^(γ-1) (finite cutoff tail) ζ < 0 (Weibull distribution) -Motivation: -Are Maximal Luminosities gumbel distributed? How good is Schechter fit for P(x)? -Can we observe the appearance of the first order correction of Ф(x) due to finite N?

  3. The Galaxy Samples from SDSS DR6 Use carefully the best photometry possible for: - LRG (luminous red galaxies): 0.20 < redshift < 0.60 ( ~67000 galaxies) - MGS (Main Galaxy Sample): 0.05 < redshift < 0.15 (~280000 galaxies) -Divide MGS in 2 populations: MGS blue and MGS red: -Fit bimodal gaussian to the Color Magnitude Diagram (CMD) -Use naive Bayes classifier for constructing optimal separating curve in the CMD.

  4. Pencil-Beams Need N close-to-i.i.d. samples of Luminosities: -Create N pencil-beams along redshift and sample the maximum Luminosity inside each of them. -Use HEALPix tessellation (equal area cells in the sky) as the beams projections on the sky. -Create high resolution HEALPix footprint inside SQL server using HTM ID’s ranges as a proxy. -Degrade HEALPix footprint to desired angular size of cell. -Use cells which have 98% of their area inside DR6 footprint.

  5. -Distribution of Number of galaxies/Pencil Beam: • Reaching ~15 galaxies • per beam at smallest • cell size (~55’). •  Can’t go smaller. • -Estimating the Tail Index ζ using PEAKS OVER A THRESHOLD of extreme luminosities: • -Fit to GENERALIZED PARETO or use • Pickands, DEdH estimators for ζ. • Looks like ζ ~ 0 •  Gumbel distribution is appropriate. • no finite cutoff of the Luminosity function at high Luminosities

  6. Comparing Data to Standard Gumbel Distribution -Histograms of standarized extreme Luminosities. Some good finite correction fits, but some some cases correction has opposite sign.  Influence of variable number of Luminosities in the pencil beams? Current status: Trying simulations…..

More Related