ASCA Observations of NLS1s: BH Mass Estimation from X-ray Variability and X-ray Spectra

ASCA observations of Narrow-Line Seyfert 1 galaxies (NLS1s) are presented. We focus on the black hole size of the NLS1 sources by employing two independent methods for the mass estimation; one is using X-ray variability, the other is using a blackbody fit to the soft component. Although the coincidence is not good for some sources, the mass estimated by these methods ranges from 1e5 to 1e7 solar masses, systematically smaller than those for typical (broad line) Seyfert 1. We consider the small mass black hole to be the principal cause of the several extreme characteristics of the NLS1s.Comment: Contributed talk presented at the Joint MPE,AIP,ESO workshop on NLS1s, Bad Honnef, Dec. 1999, to appear in New Astronomy Reviews; also available at http://wave.xray.mpe.mpg.de/conferences/nls1-worksho

On the spinor L-function of Miyawaki-Ikeda lifts

We consider lifts from two elliptic modular forms to Siegel modular forms of odd degrees which are special cases of Miyawaki-Ikeda lifts. Assuming non-vanishing of these Miyawaki-Ikeda lifts, we show that the spinor L-functions of these Miyawaki-Ikeda lifts are products of some kind of symmetric power L-functions determined by original two elliptic modular forms

Comparison of AGASA data with CORSIKA simulation

An interpretation of AGASA (Akeno Giant Air Shower Array) data by comparing the experimental results with the simulated ones by CORSIKA (COsmic Ray SImulation for KASCADE) has been made. General features of the electromagnetic component and low energy muons observed by AGASA can be well reproduced by CORSIKA. The form of the lateral distribution of charged particles agrees well with the experimental one between a few hundred metres and 2000 m from the core, irrespective of the hadronic interaction model studied and the primary composition (proton or iron). It does not depend on the primary energy between 10^17.5 and 10^20 eV as the experiment shows. If we evaluate the particle density measured by scintillators of 5 cm thickness at 600 m from the core (S_0(600), suffix 0 denotes the vertically incident shower) by taking into account the similar conditions as in the experiment, the conversion relation from S_0(600) to the primary energy is expressed as E [eV] = 2.15 x 10^17 x S_0(600)^1.015, within 10% uncertainty among the models and composition used, which suggests the present AGASA conversion factor is the lower limit. Though the form of the muon lateral distribution fits well to the experiment within 1000 m from the core, the absolute values change with hadronic interaction model and primary composition. The slope of the rho_mu(600) (muon density above 1 GeV at 600 m from the core) vs. S_0(600) relation in experiment is flatter than that in simulation of any hadronic model and primary composition. Since the experimental slope is constant from 10^15 eV to 10^19 eV, we need to study this relation in a wide primary energy range to infer the rate of change of chemical composition with energy. keywords: cosmic ray, extensive air shower, simulation, primary energy estimation PACS number ; 96.40.De, 96.40.PqComment: 30 pages, 15 figures, accepted by Astroparticle Physics at 6. Dec 199

Maximum margin classifier working in a set of strings

Numbers and numerical vectors account for a large portion of data. However, recently the amount of string data generated has increased dramatically. Consequently, classifying string data is a common problem in many fields. The most widely used approach to this problem is to convert strings into numerical vectors using string kernels and subsequently apply a support vector machine that works in a numerical vector space. However, this non-one-to-one conversion involves a loss of information and makes it impossible to evaluate, using probability theory, the generalization error of a learning machine, considering that the given data to train and test the machine are strings generated according to probability laws. In this study, we approach this classification problem by constructing a classifier that works in a set of strings. To evaluate the generalization error of such a classifier theoretically, probability theory for strings is required. Therefore, we first extend a limit theorem on the asymptotic behavior of a consensus sequence of strings, which is the counterpart of the mean of numerical vectors, as demonstrated in the probability theory on a metric space of strings developed by one of the authors and his colleague in a previous study [18]. Using the obtained result, we then demonstrate that our learning machine classifies strings in an asymptotically optimal manner. Furthermore, we demonstrate the usefulness of our machine in practical data analysis by applying it to predicting protein--protein interactions using amino acid sequences.Comment: This manuscript has been withdrawn because the experiments in Section 6 are insufficien

Anisotropy studies around the galactic center

We present the first results for anisotropy searches around the galactic center at EeV energies using data from the Pierre Auger Observatory. Our analysis, based on a substantially larger data set, do not support previous claim of anisotropy found in this region by the AGASA and Sugar experiment. Furthermore we place un upper limit on a possible point like source located at the galactic center which exclude several scenarios predicting neutron sources in this location.Comment: 10 pages. 6 figures. Proceeding of the CRIS 2006 conferenc