Zobrazeno 1 - 10
of 1 096
pro vyhledávání: '"Haegele, P."'
Autor:
Oliveira, C. B., Dors, O. L., Zinchenko, I. A., Cardaci, M. V., Hägele, G. F., Morais, I. N., Santos, P. C., Almeida, G. C.
In this paper, we present a semi-empirical calibration between the oxygen abundance and the $N2$ emission-line ratio for Low Ionization Nuclear Emission Regions (LINERs). This relation was derived by comparing the optical spectroscopic data of 118 nu
Externí odkaz:
http://arxiv.org/abs/2411.02043
Progressive dimensionality reduction algorithms allow for visually investigating intermediate results, especially for large data sets. While different algorithms exist that progressively increase the number of data points, we propose an algorithm tha
Externí odkaz:
http://arxiv.org/abs/2410.19430
The datasets of most image quality assessment studies contain ratings on a categorical scale with five levels, from bad (1) to excellent (5). For each stimulus, the number of ratings from 1 to 5 is summarized and given in the form of the mean opinion
Externí odkaz:
http://arxiv.org/abs/2410.00817
Current research provides methods to communicate uncertainty and adapts classical algorithms of the visualization pipeline to take the uncertainty into account. Various existing visualization frameworks include methods to present uncertain data but d
Externí odkaz:
http://arxiv.org/abs/2409.10217
For the Bio+Med-Vis Challenge 2024, we propose a visual analytics system as a redesign for the scatter pie chart visualization of cell type proportions of spatial transcriptomics data. Our design uses three linked views: a view of the histological im
Externí odkaz:
http://arxiv.org/abs/2409.07306
Dimensionality reduction (DR) is a well-established approach for the visualization of high-dimensional data sets. While DR methods are often applied to typical DR benchmark data sets in the literature, they might suffer from high runtime complexity a
Externí odkaz:
http://arxiv.org/abs/2408.04129
Autor:
Espinoza, Néstor, Steinrueck, Maria E., Kirk, James, MacDonald, Ryan J., Savel, Arjun B., Arnold, Kenneth, Kempton, Eliza M. -R., Murphy, Matthew M., Carone, Ludmila, Zamyatina, Maria, Lewis, David A., Samra, Dominic, Kiefer, Sven, Rauscher, Emily, Christie, Duncan, Mayne, Nathan, Helling, Christiane, Rustamkulov, Zafar, Parmentier, Vivien, May, Erin M., Carter, Aarynn L., Zhang, Xi, López-Morales, Mercedes, Allen, Natalie, Blecic, Jasmina, Decin, Leen, Mancini, Luigi, Molaverdikhani, Karan, Rackham, Benjamin V., Palle, Enric, Tsai, Shang-Min, Ahrer, Eva-Maria, Bean, Jacob L., Crossfield, Ian J. M., Haegele, David, Hébrard, Eric, Kreidberg, Laura, Powell, Diana, Schneider, Aaron D., Welbanks, Luis, Wheatley, Peter, Brahm, Rafael, Crouzet, Nicolas
Transmission spectroscopy has been a workhorse technique over the past two decades to constrain the physical and chemical properties of exoplanet atmospheres. One of its classical key assumptions is that the portion of the atmosphere it probes -- the
Externí odkaz:
http://arxiv.org/abs/2407.10294
Autor:
Dors, O. L., Almeida, G. C., Oliveira, C. B., Flury, S. R., Riffel, R., Riffel, R. A., Cardaci, M. V., Hägele, G. F., Ilha, G. S., Krabbe, A. C., Arellano-Córdova, K. Z., Santos, P. C., Morais, I. N.
For the first time, a calibration between the HeI $\lambda5876$/H$\beta$ emission line ratio and the helium abundance $y$=12+log(He/H) for Narrow line regions (NLRs) of Seyfert~2 Active Galactic Nuclei (AGN) is proposed. In this context, observationa
Externí odkaz:
http://arxiv.org/abs/2406.03259
Autor:
Hägele, Alexander, Bakouch, Elie, Kosson, Atli, Allal, Loubna Ben, Von Werra, Leandro, Jaggi, Martin
Scale has become a main ingredient in obtaining strong machine learning models. As a result, understanding a model's scaling properties is key to effectively designing both the right training setup as well as future generations of architectures. In t
Externí odkaz:
http://arxiv.org/abs/2405.18392
Autor:
Dors, O. L., Cardaci, M. V., Hägele, G. F., Valerdi, M., Ilha, G. S., Oliveira, C. B., Riffel, R. A., Flury, S. R., Arellano-Córdova, K. Z., Storchi-Bergmann, T., Riffel, R., Almeida, G. C., Morais, I. N.
We derive the nitrogen and oxygen abundances in the Narrow Line Regions (NLRs) of a sample of 38 local ($z \: < \: 0.4$) Seyfert~2 nuclei. For that, we consider narrow optical emission line intensities and direct estimates of the electron temperature
Externí odkaz:
http://arxiv.org/abs/2405.13906