Browsing by Author "Dastidar, Raya"

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    Alert Classification for the ALeRCE Broker System: The Anomaly Detector
    (IOP Publishing Ltd, 2023) Pérez-Carrasco, Manuel; Cabrera-Vives, Guillermo; Hernández-García, Lorena; Forster, F.; Sanchez-Saez, Paula; Muñoz Arancibia, Alejandra M.; Arredondo, Javier; Astorga, Nicolas; Bauer, Franz Erik; Bayo, Amelia; Catelan, Marcio; Dastidar, Raya; Estevez, P. A.; Lira, Paulina; Pignata, Giuliano
    Astronomical broker systems, such as Automatic Learning for the Rapid Classification of Events (ALeRCE), are currently analyzing hundreds of thousands of alerts per night, opening up an opportunity to automatically detect anomalous unknown sources. In this work, we present the ALeRCE anomaly detector, composed of three outlier detection algorithms that aim to find transient, periodic, and stochastic anomalous sources within the Zwicky Transient Facility data stream. Our experimental framework consists of cross-validating six anomaly detection algorithms for each of these three classes using the ALeRCE light-curve features. Following the ALeRCE taxonomy, we consider four transient subclasses, five stochastic subclasses, and six periodic subclasses. We evaluate each algorithm by considering each subclass as the anomaly class. For transient and periodic sources the best performance is obtained by a modified version of the deep support vector data description neural network, while for stochastic sources the best results are obtained by calculating the reconstruction error of an autoencoder neural network. Including a visual inspection step for the 10 most promising candidates for each of the 15 ALeRCE subclasses, we detect 31 bogus candidates (i.e., those with photometry or processing issues) and seven potential astrophysical outliers that require follow-up observations for further analysis.
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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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    SN 2020udy: A New Piece of the Homogeneous Bright Group in the Diverse Iax Subclass
    (2024) Singh, Mridweeka; Sahu, Devendra K.; Barna, Barnabas; Gangopadhyay, Anjasha; Dastidar, Raya; Teja, Rishabh Singh; Misra, Kuntal; Howell, D. Andrew; Wang, Xiaofeng; Mo, Jun; Yan, Shengyu; Hiramatsu, Daichi; Pellegrino, Craig; Anupama, G. C.; Joshi, Arti; Bostroem, K. Azalee; Burke, Jamison; McCully, Curtis; Subramanian, Rama, V; Li, Gaici; Xi, Gaobo; Li, Xin; Li, Zhitong; Srivastav, Shubham; Im, Hyobin; Dutta, Anirban
    We present optical observations and analysis of the bright type Iax supernova SN 2020udy hosted by NGC 0812. The evolution of the light curve of SN 2020udy is similar to that of other bright type Iax SNe. Analytical modeling of the quasi-bolometric light curves of SN 2020udy suggests that 0.08 +/- 0.01 M circle dot of 56Ni would have been synthesized during the explosion. The spectral features of SN 2020udy are similar to those of the bright members of type Iax class, showing a weak Si ii line. The late-time spectral sequence is mostly dominated by iron group elements with broad emission lines. Abundance tomography modeling of the spectral time series of SN 2020udy using TARDIS indicates stratification in the outer ejecta; however, to confirm this, spectral modeling at a very early phase is required. After maximum light, uniform mixing of chemical elements is sufficient to explain the spectral evolution. Unlike in the case of normal type Ia SNe, the photospheric approximation remains robust until +100 days, requiring an additional continuum source. Overall, the observational features of SN 2020udy are consistent with the deflagration of a carbon-oxygen white dwarf.
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    Unveiling the nature of two dwarf novae: CRTS J080846.2+313106 and V416 Dra
    (2024) Joshi, Arti; Catelan, Marcio; Scaringi, Simone; Schwope, Axel; Anupama, G. C.; Rawat, Nikita; Sahu, Devendra K.; Singh, Mridweeka; Dastidar, Raya; Subramanian, Rama Venkata; Rao, Srinivas M.
    We present the analysis of optical photometric and spectroscopic observations of two non-magnetic cataclysmic variables, namely CRTS J080846.2+313106 and V416 Dra. We find CRTS J080846.2+313106 to vary with a period of 4.9116 +/- 0.0003 h, which was not found in earlier studies and which we provisionally suggest is the orbital period of the system. In both long-period systems, the observed dominant signal at the second harmonic of the orbital frequency and the orbital modulation during quiescence are suggestive of ellipsoidal variation from changing aspects of the secondary, with an additional contribution from the accretion stream or hotspot. However, during the outburst, the hotspot itself is overwhelmed by the increased brightness, which is possibly associated with the accretion disc. The mid-eclipse phase for V416 Dra occurs earlier and the width of the eclipse is greater during outbursts compared to quiescence, suggesting an increased accretion disc radius during outbursts. Furthermore, from our investigation of the accretion disc eclipse in V416 Dra, we find that a total disc eclipse is possible during quiescence, whereas the disc seems to be partially obscured during outbursts, which further signifies that the disc may grow in size as the outburst progresses. The optical spectra of CRTS J080846.2+313106 and V416 Dra are typical of dwarf novae during quiescence, and they both show a significant contribution from the M2-4V secondary. The light curve patterns, orbital periods, and spectra observed in the two systems look remarkably similar, and seem to resemble the characteristics of U Gem-type dwarf novae.