Browsing by Author "Bauer, Franz E."

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    BASS XXXI: Outflow scaling relations in low redshift X-ray AGN host galaxies with MUSE
    (2022) Kakkad, D.; Sani, E.; Rojas, A. F.; Mallmann, Nicolas D.; Veilleux, S.; Bauer, Franz E.; Ricci, F.; Mushotzky, R.; Koss, M.; Ricci, C.; Treister, E.; Privon, George C.; Nguyen, N.; Bär, R.; Harrison, F.; Oh, K.; Powell, M.; Riffel, R.; Stern, D.; Trakhtenbrot, B.; Urry, C. M.
    Ionized gas kinematics provide crucial evidence of the impact that active galactic nuclei (AGNs) have in regulating star formation in their host galaxies. Although the presence of outflows in AGN host galaxies has been firmly established, the calculation of outflow properties such as mass outflow rates and kinetic energy remains challenging. We present the [O iii]lambda 5007 ionized gas outflow properties of 22 z<0.1 X-ray AGN, derived from the BAT AGN Spectroscopic Survey using MUSE/VLT. With an average spatial resolution of 1 arcsec (0.1-1.2 kpc), the observations resolve the ionized gas clouds down to sub-kiloparsec scales. Resolved maps show that the [O iii] velocity dispersion is, on average, higher in regions ionized by the AGN, compared to star formation. We calculate the instantaneous outflow rates in individual MUSE spaxels by constructing resolved mass outflow rate maps, incorporating variable outflow density and velocity. We compare the instantaneous values with time-averaged outflow rates by placing mock fibres and slits on the MUSE field-of-view, a method often used in the literature. The instantaneous outflow rates (0.2-275 M-circle dot yr(-1)) tend to be two orders of magnitude higher than the time-averaged outflow rates (0.001-40 M-circle dot yr(-1)). The outflow rates correlate with the AGN bolometric luminosity (L-bol similar to 10(42.71)-10(45.62) erg s(-1)) but we find no correlations with black hole mass (10(6.1)-10(8.9) M-circle dot), Eddington ratio (0.002-1.1), and radio luminosity (10(21)-10(26) W Hz(-1)). We find the median coupling between the kinetic energy and L-bol to be 1 per cent, consistent with the theoretical predictions for an AGN-driven outflow.
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    Deep Learning Identification of Galaxy Hosts in Transients (DELIGHT)
    (2022) Forster, Francisco; Muñoz Arancibia, Alejandra M.; Reyes, Ignacio; Gagliano, Alexander; Britt, Dylan J.; Cuellar-Carrillo, Sara; Figueroa-Tapia, Felipe; Polzin, Ava; Yousef, Yara; Arredondo, Javier; Rodríguez-Mancini, Diego; Correa-Orellana, Javier; Bayo, Amelia; Bauer, Franz E.; Catelan, Márcio; Cabrera-Vives, Guillermo; Dastidar, Raya; Estévez, Pablo A.; Pignata, Giuliano; Hernández-Garcia, Lorena; Huijse, Pablo; Reyes, Esteban; Sánchez-Sáez, Paula; Ramírez, Mauricio; Grandón, Daniela; Pineda-García, Jonathan; Chabour-Barra, Francisca; Silva-Farfán, Javier
    The Deep Learning Identification of Galaxy Hosts in Transients (DELIGHT, Förster et al. 2022, submitted) is a library created by the ALeRCE broker to automatically identify host galaxies of transient candidates using multi-resolution images and a convolutional neural network (you can test it with our example notebook, that you can run in Colab). The initial idea for DELIGHT started as a project proposed for the La Serena School of Data Science in 2021. You can install it using pip install astro-delight, but we recommend cloning this repository and pip install . from there. The library has a class with several methods that allow you to get the most likely host coordinates starting from given transient coordinates. In order to do this, the delight object needs a list of object identifiers and coordinates (oid, ra, dec). With this information, it downloads PanSTARRS images centered around the position of the transients (2 arcmin x 2 arcmin), gets their WCS solutions, creates the multi-resolution images, does some extra preprocessing of the data, and finally predicts the position of the hosts using a multi-resolution image and a convolutional neural network. It can also estimate the host's semi-major axis if requested taking advantage of the multi-resolution images. Note that DELIGHT's prediction time is currently dominated by the time to download PanSTARRS images using the panstamps service. In the future, we expect that there will be services that directly provide multi-resolution images, which should be more lightweight with no significant loss of information. Once these images are obtained, the processing times are only milliseconds per host. If you cannot install some of the dependencies, e.g. tensorflow, you can try running DELIGHT directly from Google Colab (test in this link). Github link: https://github.com/fforster/delight PyPi link: https://pypi.org/project/astro-delight/...
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    DELIGHT: Deep Learning Identification of Galaxy Hosts of Transients using Multiresolution Images
    (2022) Förster, Francisco; Muñoz Arancibia, Alejandra M.; Reyes-Jainaga, Ignacio; Gagliano, Alexander; Britt, Dylan; Cuellar-Carrillo, Sara; Figueroa-Tapia, Felipe; Polzin, Ava; Yousef, Yara; Arredondo, Javier; Rodríguez-Mancini, Diego; Correa-Orellana, Javier; Bayo, Amelia; Bauer, Franz E.; Catelan, Márcio; Cabrera-Vives, Guillermo; Dastidar, Raya; Estévez, Pablo A.; Pignata, Giuliano; Hernández-García, Lorena; Huijse, Pablo; Reyes, Esteban; Sánchez-Sáez, Paula; Ramírez, Mauricio; Grandón, Daniela; Pineda-García, Jonathan; Chabour-Barra, Francisca; Silva-Farfán, Javier
    We present DELIGHT, or Deep Learning Identification of Galaxy Hosts of Transients, a new algorithm designed to automatically and in real time identify the host galaxies of extragalactic transients. The proposed algorithm receives as input compact, multiresolution images centered at the position of a transient candidate and outputs two-dimensional offset vectors that connect the transient with the center of its predicted host. The multiresolution input consists of a set of images with the same number of pixels, but with progressively larger pixel sizes and fields of view. A sample of 16,791 galaxies visually identified by the Automatic Learning for the Rapid Classification of Events broker team was used to train a convolutional neural network regression model. We show that this method is able to correctly identify both relatively large (10″ < r < 60″) and small (r ≤ 10″) apparent size host galaxies using much less information (32 kB) than with a large, single-resolution image (920 kB). The proposed method has fewer catastrophic errors in recovering the position and is more complete and has less contamination (<0.86%) recovering the crossmatched redshift than other state-of-the-art methods. The more efficient representation provided by multiresolution input images could allow for the identification of transient host galaxies in real time, if adopted in alert streams from new generation of large -etendue telescopes such as the Vera C. Rubin Observatory....
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    Optimization of the Observing Cadence for the Rubin Observatory Legacy Survey of Space and Time: A Pioneering Process of Community-focused Experimental Design
    (2022) Bianco, Federica B.; Ivezić, Željko; Jones, R. Lynne; Graham, Melissa L.; Marshall, Phil; Saha, Abhijit; Strauss, Michael A.; Yoachim, Peter; Ribeiro, Tiago; Anguita, Timo; Bauer, A. E.; Bauer, Franz E.; Bellm, Eric C.; Blum, Robert D.; Brandt, William N.; Brough, Sarah; Catelan, Márcio; Clarkson, William I.; Connolly, Andrew J.; Gawiser, Eric; Gizis, John E.; Hložek, Renée; Kaviraj, Sugata; Liu, Charles T.; Lochner, Michelle; Mahabal, Ashish A.; Mandelbaum, Rachel; McGehee, Peregrine; Neilsen, Eric H., Jr.; Olsen, Knut A. G.; Peiris, Hiranya V.; Rhodes, Jason; Richards, Gordon T.; Ridgway, Stephen; Schwamb, Megan E.; Scolnic, Dan; Shemmer, Ohad; Slater, Colin T.; Slosar, Anže; Smartt, Stephen J.; Strader, Jay; Street, Rachel; Trilling, David E.; Verma, Aprajita; Vivas, A. K.; Wechsler, Risa H.; Willman, Beth
    Vera C. Rubin Observatory is a ground-based astronomical facility under construction, a joint project of the National Science Foundation and the U.S. Department of Energy, designed to conduct a multipurpose 10 yr optical survey of the Southern Hemisphere sky: the Legacy Survey of Space and Time. Significant flexibility in survey strategy remains within the constraints imposed by the core science goals of probing dark energy and dark matter, cataloging the solar system, exploring the transient optical sky, and mapping the Milky Way. The survey's massive data throughput will be transformational for many other astrophysics domains and Rubin's data access policy sets the stage for a huge community of potential users. To ensure that the survey science potential is maximized while serving as broad a community as possible, Rubin Observatory has involved the scientific community at large in the process of setting and refining the details of the observing strategy. The motivation, history, and decision-making process of this strategy optimization are detailed in this paper, giving context to the science-driven proposals and recommendations for the survey strategy included in this Focus Issue....