Phylogenetic Tools in Astrophysics

Fraix-Burnet, Didier

doi:10.1002/9781118445112.stat07935

Cited by 5 publications

(3 citation statements)

References 23 publications

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“…Interestingly, even though "FeIIness" and radio-loudness are at opposite ends of Eigenvector space (e.g., [129], [130], [18], [48]) 3 , most radio-loud NLS1 galaxies are strong FeII emitters, with FeII/Hβ ratios even larger than a significant fraction of the radio-quiet NLS1 population ([74], [150]; Fig. 7).…”

Section: Emission-line Spectroscopy: Feiimentioning

confidence: 99%

Multi-wavelength properties of radio-loud Narrow-line Seyfert 1 galaxies

Komossa

2018

Proceedings of Revisiting Narrow-Line Seyfert 1 Galaxies and Their Place in the Universe — PoS(NLS1-2018)

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Radio-loud Narrow-line Seyfert 1 (NLS1) galaxies are unique probes of the formation of powerful radio jets at extreme (near-Eddington) accretion rates and low black hole masses, in a regime very different from classical blazars and, therefore, not probed before. Further, strong thermal and nonthermal components simultaneously present in their spectral energy distributions (SEDs), shed new light on the jet-disk connection at much shorter timescales, given their low SMBH masses. While NLS1 galaxies have been studied thoroughly at optical and X-ray energies for decades, the populations of radio-loud, and γ-ray emitting NLS1 galaxies have only emerged more recently. This contribution provides a short review of the multi-wavelength properties of this intriguing class of Active Galactic Nuclei (AGN), including X-ray variability and emission mechanisms, γ-ray discoveries, SEDs, star formation activity, host galaxy morphologies, different methods of black hole mass estimation, (unexpected) FeII properties, and high-velocity large-scale outflows possibly driven by the radio jet and with a surprisingly pronounced ionization stratification.Revisiting narrow-line Seyfert 1 galaxies and their place in the Universe Radio-loud NLS1 galaxies S. Komossa this then implies accretion at or near the Eddington limit (Fig. 1). NLS1 galaxies are, therefore, rapidly growing their supermassive black holes (SMBHs), and may represent young or re-juvenated AGN ([99]). In the Eigenvector (EV) analysis of [18], the Eddington ratio L bol /L Edd drives the first eigenvector EV1, while accretion rate drives EV2.While NLS1 galaxies are on average more radio-quiet than broad-line AGN, a fraction of NLS1s are beamed and radio-loud, are highly variable at radio frequencies, and are detected at γ-rays. This contribution provides a review of the multi-wavelength properties of this remarkable new class of radio-loud, jet-emitting AGN.

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Section: Emission-line Spectroscopy: Feiimentioning

confidence: 99%

Multi-wavelength properties of radio-loud Narrow-line Seyfert 1 galaxies

Komossa

2018

Proceedings of Revisiting Narrow-Line Seyfert 1 Galaxies and Their Place in the Universe — PoS(NLS1-2018)

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“…O'Brien and Lyman, ) and even astrophysics (e.g. Fraix‐Burnet, ; Fraix‐Burnet et al., ). However, particular attention should be paid to its general usage due to the heterogeneity of real data.…”

Section: Introductionmentioning

confidence: 99%

Biogeography of the freshwater fishes of the Guianas using a partitioned parsimony analysis of endemicity with reappraisal of ecoregional boundaries

Lemopoulos

Covain

2018

Cladistics

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The Guianas are one of the most diverse regions of the Neotropics, hosting a particularly high rate of freshwater fish endemism. The present distributional patterns of freshwater fish species in the major catchments of the Guianas (comprising Guyana, Suriname and French Guiana) were analysed to reveal the faunal relationships between rivers, evaluate different hypotheses concerning biogeographical units, and redefine the boundaries of the Guianese freshwater ecoregions. A parsimony analysis of endemicity was performed using a data partitioning strategy to alleviate some drawbacks inherent to the method (e.g. long branch attraction artefact, heterotachy), and take into account alternative parsimony models assigning different constraints on state changes for the different species. A strong spatial element was present in the data with a structuring of species along a west-east gradient. Two main biogeographical units were highlighted: one to the west, ranging from the Essequibo to the Commewijne rivers and including the Proto Berbice and Surinamese regions, and one to the east ranging from the Maroni to the Oyapock rivers and including the Western, Central and Eastern French Guiana regions. Each ecoregion possessed distinctive fish assemblages, and three to four potential zones of faunal exchanges between Amazonian and Guianese rivers have been confirmed.

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“…Problems of this kind appear in many areas of application such as astronomy (blurred images), econometrics (instrumental variables), medical imaging (tomography, dynamic contrast enhanced Computerized Tomography and Magnetic Resonance Imaging), finance (model calibration of volatility) and many others where similar curves are measured and can be recovered together. Indeed, clustering has been applied to solution of ill-posed inverse problems for decades in pattern recognition [4], astronomy [21], astrophysics [12], pattern-based time series segmentation [8], medical imaging [7], elastography for computation of the unknown stiffness distribution [3] and for detecting early warning signs on stock market bubbles [17], to name a few. While in some of other settings the main objective is finding group assignments, we are considering only applications where clustering is used merely as a denoising technique.…”

Section: Introductionmentioning

confidence: 99%

Clustering in statistical ill-posed linear inverse problems

Rajapakshage¹,

Pensky²

2018

Preprint

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In many statistical linear inverse problems, one needs to recover classes of similar curves from their noisy images under an operator that does not have a bounded inverse. Problems of this kind appear in many areas of application. Routinely, in such problems clustering is carried out at the pre-processing step and then the inverse problem is solved for each of the cluster averages separately. As a result, the errors of the procedures are usually examined for the estimation step only. The objective of this paper is to examine, both theoretically and via simulations, the effect of clustering on the accuracy of the solutions of general ill-posed linear inverse problems. In particular, we assume that one observes, where functions f m can be grouped into K classes and one needs to recover a vector function f = (f 1 , • • • , f M ) T . We construct an estimators for f as a solution of a penalized optimization problem and derive an oracle inequality for its precision. By deriving upper and minimax lower bounds for the error, we confirm that the estimator is minimax optimal or nearly minimax optimal up to a logarithmic factor of the number of observations. One of the advantages of our estimation procedure is that we do not assume that the number of clusters is known in advance. We conclude that clustering does not have an adverse effect on the estimation precision as long as class sizes and the number of observations are large enough. However, significant improvement in accuracy occurs only if the problem is not severely ill-posed.

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Phylogenetic Tools in Astrophysics

Cited by 5 publications

References 23 publications

Multi-wavelength properties of radio-loud Narrow-line Seyfert 1 galaxies

Multi-wavelength properties of radio-loud Narrow-line Seyfert 1 galaxies

Biogeography of the freshwater fishes of the Guianas using a partitioned parsimony analysis of endemicity with reappraisal of ecoregional boundaries

Clustering in statistical ill-posed linear inverse problems

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