| 1. |
- Gutierrez, C. P., et al.
(författare)
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DES16C3cje : A low-luminosity, long-lived supernova
- 2020
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Ingår i: Monthly notices of the Royal Astronomical Society. - : Oxford University Press (OUP). - 0035-8711 .- 1365-2966. ; 496:1, s. 95-110
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Tidskriftsartikel (refereegranskat)abstract
- We present DES16C3cje, a low-luminosity, long-lived type II supernova (SN II) at redshift 0.0618, detected by the Dark Energy Survey (DES). DES16C3cje is a unique SN. The spectra are characterized by extremely narrow photospheric lines corresponding to very low expansion velocities of less than or similar to 1500 km s(-1), and the light curve shows an initial peak that fades after 50 d before slowly rebrightening over a further 100 d to reach an absolute brightness of M-r similar to 15.5 mag. The decline rate of the late-time light curve is then slower than that expected from the powering by radioactive decay of Co-56, but is comparable to that expected from accretion power. Comparing the bolometric light curve with hydrodynamical models, we find that DES16C3cje can be explained by either (i) a low explosion energy (0.11 foe) and relatively large Ni-56 production of 0.075 M-circle dot from an similar to 15 M-circle dot red supergiant progenitor typical of other SNe II, or (ii) a relatively compact similar to 40 M-circle dot star, explosion energy of 1 foe, and 0.08 M-circle dot of Ni-56. Both scenarios require additional energy input to explain the late-time light curve, which is consistent with fallback accretion at a rate of similar to 0.5 x 10(-)(8) M-circle dot s(-1).
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| 2. |
- Dörpinghaus, Jens, et al.
(författare)
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Centrality Measures in multi-layer Knowledge Graphs
- 2022
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Ingår i: Communication Papers of the of the 17th Conference on Computer Science and Intelligence Systems. - : Polish information processing society (PTI). - 9788396589743 - 9788396589750 ; , s. 163-170
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Konferensbidrag (refereegranskat)abstract
- Knowledge graphs play a central role for linkingdifferent data which leads to multiple layers. Thus, they are widely used in big data integration, especially for connecting data from different domains. Few studies have investigated the questions how multiple layers within graphs impact methods and algorithms developed for single-purpose networks, for example social networks. This manuscript investigates the impact on the centrality measures of graphs with multiple layers compared to a those measures in single-purpose graphs. In particular, (a) we develop an experimental environment to (b) evaluate two different centrality measures --- degree and betweenness centrality --- on random graphs inspired by social network analysis: small-world and scale-free networks. The presented approach (c) shows that the graph structures and topology has a great impact on its robustness for additional data stored. Although the experimental analysis of random graphs allows us to make some basic observations we will (d) make suggestions for additional research on particular graph structures that have a great impact on the stability of networks.
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| 3. |
- Schweinsberg, Martin, et al.
(författare)
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Same data, different conclusions : Radical dispersion in empirical results when independent analysts operationalize and test the same hypothesis
- 2021
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Ingår i: Organizational Behavior and Human Decision Processes. - : Elsevier BV. - 0749-5978 .- 1095-9920. ; 165, s. 228-249
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Tidskriftsartikel (refereegranskat)abstract
- In this crowdsourced initiative, independent analysts used the same dataset to test two hypotheses regarding the effects of scientists' gender and professional status on verbosity during group meetings. Not only the analytic approach but also the operationalizations of key variables were left unconstrained and up to individual analysts. For instance, analysts could choose to operationalize status as job title, institutional ranking, citation counts, or some combination. To maximize transparency regarding the process by which analytic choices are made, the analysts used a platform we developed called DataExplained to justify both preferred and rejected analytic paths in real time. Analyses lacking sufficient detail, reproducible code, or with statistical errors were excluded, resulting in 29 analyses in the final sample. Researchers reported radically different analyses and dispersed empirical outcomes, in a number of cases obtaining significant effects in opposite directions for the same research question. A Boba multiverse analysis demonstrates that decisions about how to operationalize variables explain variability in outcomes above and beyond statistical choices (e.g., covariates). Subjective researcher decisions play a critical role in driving the reported empirical results, underscoring the need for open data, systematic robustness checks, and transparency regarding both analytic paths taken and not taken. Implications for orga-nizations and leaders, whose decision making relies in part on scientific findings, consulting reports, and internal analyses by data scientists, are discussed.
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