A new tool to derive simultaneously exponent and extremes of power-law distributions
Many experimental quantities show a power-law distribution p(x) ∝ x−α. In astrophysics, examples are: size distribution of dust grains or luminosity function of galaxies. Such distributions are characterized by the exponent α and by the extremes xminxmax where the distribution extends. There are no...
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| Hauptverfasser: | , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
November 2023
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| In: |
Monthly notices of the Royal Astronomical Society
Year: 2023, Jahrgang: 525, Heft: 3, Pages: 4744-4760 |
| ISSN: | 1365-2966 |
| DOI: | 10.1093/mnras/stad2634 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1093/mnras/stad2634
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| Verfasserangaben: | S. Pezzuto, A. Coletta, R.S. Klessen, E. Schisano, M. Benedettini, D. Elia, S. Molinari, J.D. Soler and A. Traficante |
MARC
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| 100 | 1 | |a Pezzuto, Stefania |d 1966- |e VerfasserIn |0 (DE-588)133201111X |0 (DE-627)1890867284 |4 aut | |
| 245 | 1 | 2 | |a A new tool to derive simultaneously exponent and extremes of power-law distributions |c S. Pezzuto, A. Coletta, R.S. Klessen, E. Schisano, M. Benedettini, D. Elia, S. Molinari, J.D. Soler and A. Traficante |
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| 520 | |a Many experimental quantities show a power-law distribution p(x) ∝ x−α. In astrophysics, examples are: size distribution of dust grains or luminosity function of galaxies. Such distributions are characterized by the exponent α and by the extremes xminxmax where the distribution extends. There are no mathematical tools that derive the three unknowns at the same time. In general, one estimates a set of α corresponding to different guesses of xminxmax. Then, the best set of values describing the observed data is selected a posteriori. In this paper, we present a tool that finds contextually the three parameters based on simple assumptions on how the observed values xi populate the unknown range between xmin and xmax for a given α. Our tool, freely downloadable, finds the best values through a non-linear least-squares fit. We compare our technique with the maximum likelihood estimators for power-law distributions, both truncated and not. Through simulated data, we show for each method the reliability of the computed parameters as a function of the number N of data in the sample. We then apply our method to observed data to derive: (i) the slope of the core mass function in the Perseus star-forming region, finding two power-law distributions: α = 2.576 between $1.06\, \mathrm{M}_{\odot }$ and $3.35\, \mathrm{M}_{\odot }$, α = 3.39 between $3.48\, \mathrm{M}_{\odot }$ and $33.4\, \mathrm{M}_{\odot }$; (ii) the slope of the γ-ray spectrum of the blazar J0011.4+0057, extracted from the Fermi-LAT archive. For the latter case, we derive α = 2.89 between 1484 MeV and 28.7 GeV; then we derive the time-resolved slopes using subsets of 200 photons each. | ||
| 700 | 1 | |a Coletta, A |e VerfasserIn |4 aut | |
| 700 | 1 | |a Klessen, Ralf S. |d 1968- |e VerfasserIn |0 (DE-588)120533820 |0 (DE-627)392381532 |0 (DE-576)178685399 |4 aut | |
| 700 | 1 | |a Schisano, E |e VerfasserIn |4 aut | |
| 700 | 1 | |a Benedettini, M |e VerfasserIn |4 aut | |
| 700 | 1 | |a Elia, D |e VerfasserIn |4 aut | |
| 700 | 1 | |a Molinari, S |e VerfasserIn |4 aut | |
| 700 | 1 | |a Soler, J D |e VerfasserIn |4 aut | |
| 700 | 1 | |a Traficante, A |e VerfasserIn |4 aut | |
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