Gaia GraL: Gaia DR2 Gravitational Lens Systems. VII. XMM-Newton Observations of Lensed Quasars

© 2022. The Author(s). Published by the American Astronomical Society. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.3847/1538-4357/ac4476We present XMM-Newton X-ray observations of nine confirmed lensed quasars at 1 ≲ z ≲ 3 identified by the Gaia Gravitational Lens program. Eight systems are strongly detected, with 0.3-8.0 keV fluxes F 0.3-8.0 ≳ 5 ×10-14 erg cm-2 s-1. Modeling the X-ray spectra with an absorbed power law, we derive power-law photon indices and 2-10 keV luminosities for the eight detected quasars. In addition to presenting sample properties for larger quasar population studies and for use in planning for future caustic-crossing events, we also identify three quasars of interest: a quasar that shows evidence of flux variability from previous ROSAT observations, the most closely separated individual lensed sources resolved by XMM-Newton, and one of the X-ray brightest quasars known at z > 3. These sources represent the tip of the discoveries that will be enabled by SRG/eROSITA.Peer reviewe

Gaia GraL: Gaia DR2 Gravitational Lens Systems. VII. XMM-Newton Observations of Lensed Quasars

Abstract We present XMM-Newton X-ray observations of nine confirmed lensed quasars at 1 ≲ z ≲ 3 identified by the Gaia Gravitational Lens program. Eight systems are strongly detected, with 0.3–8.0 keV fluxes F 0.3−8.0 ≳ 5 ×10−14 erg cm−2 s−1. Modeling the X-ray spectra with an absorbed power law, we derive power-law photon indices and 2–10 keV luminosities for the eight detected quasars. In addition to presenting sample properties for larger quasar population studies and for use in planning for future caustic-crossing events, we also identify three quasars of interest: a quasar that shows evidence of flux variability from previous ROSAT observations, the most closely separated individual lensed sources resolved by XMM-Newton, and one of the X-ray brightest quasars known at z &gt; 3. These sources represent the tip of the discoveries that will be enabled by SRG/eROSITA.</jats:p

What are phone trade conversations ?

International audienceWe propose a multi-disciplinary overview of the content & structure of a large collection of call center conversations data (about 1500 recorded inbound conversations from a French call center). The aim of the research is twofold : to define what are phone trade conversations, mainly using a quantitative analysis & to question the efficiency of usual call centers measures of their activity. Our ongoing empirical research aims at linking structural elements (length, number of topics, topics, rebounds, clients expression of satisfaction), & agent evaluation criteria used in the call center (use of polite formula, reformulation of the callers complaint,). A qualitative approach (conversational analysis) of selected parts of interactions completes the analysis. Special attention is paid to the way partakers use their localization during the talks & to the way call center agents orally express the actions they are accomplishing to the callers. Finally, through a comparison of the agents evaluation criteria & the analysis done, we intend to qualify what are/arent perfect talks according to the call center managements criteria

Modeling call centers agent client interaction performance

International audienceThis research proposes to model the performance of the interaction agent/client during conversations in call centers. The call centers represent strategic CRM tools for a majority of companies spending big budgets in their management without really making data mining to measure their agents efficiency face to their clients. Contrary to previous researches based on questionnaires and declarative data, we use a rich dataset of conversations between agents and clients and measure interaction performance based only on the conversation content. These talks have been recorded and manually fully transcribed (we also worked on automatic transcribed data by speech recognition and compared the results). We decided to focus on the reality of the dyadic interactions talks without any interpretation or rationalization from none of those who take part of it. We first split the conversations in different phases (open, identification, treatment, proposal, and closure). We then coded the conversations in several quantitative variables (such as length, number of topics, number of talk rebound-linked to a commercial proposal done by the agent-, commercial proposal acceptance..) and qualitative variables based on sentiment analysis (using text mining) and emotions measure. Based on literature on recovery satisfaction, we defined three types of performance: procedural, commercial and interactional performance and finally measure a global performance of the conversation. Using structural equation models, this research provides new insights on the measure of the performance of the interaction agent/client in call centers, improving knowledge in the area of call centers data mining