arXiv subjects

Rodríguez

Publications and source records attributed to Rodríguez.

3 recordsLinked to original sources

arXiv2021

Discovery of a Highly Collimated Flow from the High-Mass Protostar ISOSS J23053+5953 SMM2

We present Very Large Array C, X, and Q-band continuum observations, as well as 1.3 mm continuum and CO(2-1) observations with the Submillimeter Array toward the high-mass protostellar candidate ISOSS J23053+5953 SMM2. Compact cm continuum emission was detected near the center of the SMM2 core with a spectral index of 0.24 between 6 and 3.6 cm, and a radio luminosity of 1.3 mJy kpc$^2$. The 1.3 mm thermal dust emission indicates a mass of the SMM2 core of 45.8 Msun. The CO(2-1) observations reveal a large, massive molecular outflow centered on the SMM2 core. This fast outflow ($>$ 50 km/s from the cloud systemic velocity) is highly collimated, with a broader, lower-velocity component. The large values for outflow mass (45.2 Msun), and momentum rate (6 x 10$^{-3}$ Msun km/s/yr) derived from the CO emission are consistent with those of flows driven by high-mass YSOs. The dynamical timescale of the flow is between 1.5 - 7.2 x 10$^4$ yr. We also found from the C18O to thermal dust emission ratio that CO is depleted by a factor of about 20, possibly due to freeze out of CO molecules on dust grains. Our data are consistent with previous findings that ISOSS J23053+5953 SMM2 is an emerging high-mass protostar in an early phase of evolution, with an ionized jet, and a fast, highly collimated, and massive outflow.

astro-ph.GA↗

arXiv2014

Beyond correlation in spatial statistics modeling

We introduce a model for spatial statistics which can account explicitly for interactions among more than two field components at a time. The theoretical aspects of the model are dealt with: cumulant and moment generating functions, spatial consistency and parameter estimation. On the basis of a detailed synthetic example, we show the kind of inference about the (partially observed) spatial field that can be very wrong, if one validates his model by checking only one and two dimensional marginal fit, and covariance function fit. We suggest statistics that can be used additionally for model validation, which help assess interdependence among groups of variables. The implications of considering multivariate interactions for intense daily precipitation forecasting over a small catchment in southeastern Germany (that of the Saalach river) are investigated.

stat.ME↗

arXiv2014

Multivariate interactions modeling through their manifestations: low dimensional model building via the Cumulant Generating Function

Growing dimensionality of data calls for beyond-pairwise interactions quantification. Measures of multidimensional interactions quantification are hindered, among others, by two issues: 1. Interpretation difficulties, 2. the curse of dimensionality. We propose to deal with multidimensional interactions by identifying subject-matter specific "interaction manifestations" and then building a low-dimensional model that reproduces as close as possible such manifestations. We argue that an adequate model building approach is to build the model in the form of a cumulant generating function, i.e. to use joint cumulants as building blocks. The whole approach resembles that of probability inversion in the area of expert knowledge based risk assessment, where a discrimination is made between "elicitation" variables, familiar to the experts, and "target" (or model) variables, consisting of the more abstract parameters of a mathematical model. A synthetic example is provided to illustrate these ideas.

stat.ME↗