arXiv subjects

Martin Meyer

Publications and source records attributed to Martin Meyer.

65 records · Page 4Linked to original sources

arXiv2006

Catching Dissolving Clusters: a New Approach

Traditional studies of stellar clusters in external galaxies use surface photometry and therefore focus on systems that are still bright and compact enough to be separated from the stellar background. Consequently, the latter stages of unbound cluster evolution are still poorly understood. This dramatically constrains our knowledge of the dissolution processes of stellar clusters in various physical environments. We present the first results of a new approach to directly detect and quantify the characteristics of evolved stellar clusters. Using the exceptional spatial resolution and sensitivity of HST/ACS images to resolve the stellar content nearby galaxies, we construct colour-magnitude diagrams for the observed fields. This enable us to separate the younger population likely present in young clusters from the older stellar content of the star field background. We utilize a clustering algorithm to assign each star to a group based on its local spatial density. This novel approach makes use of algorithms typically applied in N-body and cosmological studies. We test the method and show that it successfully detects less compact clusters that would normally be lost in the star field background. We also detect B-type stars well spread in the galaxy disk of NGC 1313, probably the result of infant mortality of stellar clusters.

astro-ph↗

arXiv2006

Massive-Star Supernovae as Major Dust Factories

We present late-time optical and mid-infrared observations of the Type-II supernova 2003gd in NGC 628. Mid-infrared excesses consistent with cooling dust in the ejecta are observed 499-678 days after outburst, and are accompanied by increasing optical extinction and growing asymmetries in the emission-line profiles. Radiative-transfer models show that up to 0.02 solar masses of dust has formed within the ejecta, beginning as early as 250 days after outburst. These observations show that dust formation in supernova ejecta can be efficient and that massive-star supernovae can be major dust producers throughout the history of the Universe.

astro-ph↗

arXiv1999

Apparent multifractality in financial time series

We present a exactly soluble model for financial time series that mimics the long range volatility correlations known to be present in financial data. Although our model is `monofractal' by construction, it shows apparent multiscaling as a result of a slow crossover phenomenon on finite time scales. Our results suggest that it might be hard to distinguish apparent and true multifractal behavior in financial data. Our model also leads to a new family of stable laws for sums of correlated random variables.

cond-mat↗

arXiv1999

Ivory Tower Universities and Competitive Business Firms

There is nowadays considerable interest on ways to quantify the dynamics of research activities, in part due to recent changes in research and development (R&D) funding. Here, we seek to quantify and analyze university research activities, and compare their growth dynamics with those of business firms. Specifically, we analyze five distinct databases, the largest of which is a National Science Foundation database of the R&D expenditures for science and engineering of 719 United States (US) universities for the 17-year period 1979--1995. We find that the distribution of growth rates displays a ``universal'' form that does not depend on the size of the university or on the measure of size used, and that the width of this distribution decays with size as a power law. Our findings are quantitatively similar to those independently uncovered for business firms, and consistent with the hypothesis that the growth dynamics of complex organizations may be governed by universal mechanisms.

cond-mat.stat-mech↗

arXiv1999

Scaling of the distribution of fluctuations of financial market indices

We study the distribution of fluctuations over a time scale $Δt$ (i.e., the returns) of the S&P 500 index by analyzing three distinct databases. Database (i) contains approximately 1 million records sampled at 1 min intervals for the 13-year period 1984-1996, database (ii) contains 8686 daily records for the 35-year period 1962-1996, and database (iii) contains 852 monthly records for the 71-year period 1926-1996. We compute the probability distributions of returns over a time scale $Δt$, where $Δt$ varies approximately over a factor of 10^4 - from 1 min up to more than 1 month. We find that the distributions for $Δt \leq$ 4 days (1560 mins) are consistent with a power-law asymptotic behavior, characterized by an exponent $α\approx 3$, well outside the stable Lévy regime $0 < α< 2$. To test the robustness of the S&P result, we perform a parallel analysis on two other financial market indices. Database (iv) contains 3560 daily records of the NIKKEI index for the 14-year period 1984-97, and database (v) contains 4649 daily records of the Hang-Seng index for the 18-year period 1980-97. We find estimates of $α$ consistent with those describing the distribution of S&P 500 daily-returns. One possible reason for the scaling of these distributions is the long persistence of the autocorrelation function of the volatility. For time scales longer than $(Δt)_{\times} \approx 4$ days, our results are consistent with slow convergence to Gaussian behavior.

cond-mat.stat-mech↗

arXiv1999

The statistical properties of the volatility of price fluctuations

We study the statistical properties of volatility---a measure of how much the market is likely to fluctuate. We estimate the volatility by the local average of the absolute price changes. We analyze (a) the S&P 500 stock index for the 13-year period Jan 1984 to Dec 1996 and (b) the market capitalizations of the largest 500 companies registered in the Trades and Quotes data base, documenting all trades for all the securities listed in the three major stock exchanges in the US for the 2-year period Jan 1994 to Dec 1995. For the S&P 500 index, the probability density function of the volatility can be fit with a log-normal form in the center. However, the asymptotic behavior is better described by a power-law distribution characterized by an exponent 1 + μ\approx 4. For individual companies, we find a power law asymptotic behavior of the probability distribution of volatility with exponent 1 + μ\approx 4, similar to the S&P 500 index. In addition, we find that the volatility distribution scales for a range of time intervals. Further, we study the correlation function of the volatility and find power law decay with long persistence for the S&P 500 index and the individual companies with a crossover at approximately 1.5 days. To quantify the power-law correlations, we apply power spectrum analysis and a recently-developed modified root-mean-square analysis, termed detrended fluctuation analysis (DFA). For the S&P 500 index, DFA estimates for the exponents characterizing the power law correlations are α_1=0.66 for short time scales (within \approx 1.5 days) and α_2=0.93 for longer time scales (up to a year). For individual companies, we find α_1=0.60 and α_2=0.74, respectively. The power spectrum gives consistent estimates of the two power-law exponents.

cond-mat.stat-mech↗

arXiv1998

Inverse Cubic Law for the Probability Distribution of Stock Price Variations

The probability distribution of stock price changes is studied by analyzing a database (the Trades and Quotes Database) documenting every trade for all stocks in three major US stock markets, for the two year period Jan 1994 -- Dec 1995. A sample of 40 million data points is extracted, which is substantially larger than studied hitherto. We find an asymptotic power-law behavior for the cumulative distribution with an exponent alpha approximately 3, well outside the Levy regime 0< alpha <2.

cond-mat.stat-mech↗

arXiv1998

Environmental changes, co-extinction, and patterns in the fossil record

We introduce a new model for large scale evolution and extinction in which species are organized into food chains. The system evolves by two processes: origination/speciation and extinction. In the model, extinction of a given species can be due to an externally induced change in the environment or due to the extinction of all preys of that species (co-extinction). The model is able to reproduce the empirical observations, such as the statistical fractality of the fossil record or the scale-free distribution of extinction events, without invoking extinctions due to competition between species.

cond-mat.stat-mech↗

arXiv1998

Universal features in the growth dynamics of complex organizations

We analyze the fluctuations in the gross domestic product (GDP) of 152 countries for the period 1950--1992. We find that (i) the distribution of annual growth rates for countries of a given GDP decays with ``fatter'' tails than for a Gaussian, and (ii) the width of the distribution scales as a power law of GDP with a scaling exponent $β\approx 0.15$. Both findings are in surprising agreement with results on firm growth. These results are consistent with the hypothesis that the evolution of organizations with complex structure is governed by similar growth mechanisms.

cond-mat.stat-mech↗

arXiv1997

Volatility distribution in the S&P500 Stock Index

We study the volatility of the S&P500 stock index from 1984 to 1996 and find that the volatility distribution can be very well described by a log-normal function. Further, using detrended fluctuation analysis we show that the volatility is power-law correlated with Hurst exponent $α\cong0.9$.

cond-mat.stat-mech↗

arXiv1997

Correlations in Economic Time Series

The correlation function of a financial index of the New York stock exchange, the S&P 500, is analyzed at 1 min intervals over the 13-year period, Jan 84 -- Dec 96. We quantify the correlations of the absolute values of the index increment. We find that these correlations can be described by two different power laws with a crossover time t_\times\approx 600 min. Detrended fluctuation analysis gives exponents $α_1=0.66$ and $α_2=0.93$ for $t t_\times$ respectively. Power spectrum analysis gives corresponding exponents $β_1=0.31$ and $β_2=0.90$ for $f>f_\times$ and $f< f_\times$ respectively.

cond-mat.stat-mech↗