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  1. AU="Copin, Y."
  2. AU="Twomey, Bernadette"
  3. AU="Greendale, Gail A"
  4. AU="Haseli Mashhadi, Nazanin"
  5. AU="Pilecki, Z."
  6. AU="Alegado, Rosanna A"
  7. AU="Lv, Xinpeng"
  8. AU="Mare, Marzia"
  9. AU="Saleem, Nadia"
  10. AU="Garcia, F G"
  11. AU="Choi, Jong-Il"
  12. AU="Jandial, Tanvi"
  13. AU="Sartori, Chiara"
  14. AU="Pugsley, T A"
  15. AU=Passino Claudio
  16. AU="Ji, Ziwei"
  17. AU="Lim, K E"
  18. AU="Foresti, C."
  19. AU="Czimer, Dávid"
  20. AU="Nayak, Naren"
  21. AU="Khan, Jahidur Rahman"
  22. AU="Huber, Tobias B"
  23. AU="Özbek, Süha Süreyya"
  24. AU="Elujoba, Anthony A"
  25. AU="Lucas, Brian P"
  26. AU="Ngabo, Lucien"
  27. AU="M Elizabeth H. Hammond"
  28. AU="Poppe, Katrina"
  29. AU=Du Ping
  30. AU=Adorno E AU=Adorno E
  31. AU="Rehn, Alexandra"
  32. AU="Senff-Ribeiro, Andrea"

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  1. Buch ; Online: Euclid preparation. XXV. The Euclid Morphology Challenge -- Towards model-fitting photometry for billions of galaxies

    Euclid Collaboration / Merlin, E. / Castellano, M. / Bretonnière, H. / Huertas-Company, M. / Kuchner, U. / Tuccillo, D. / Buitrago, F. / Peterson, J. R. / Conselice, C. J. / Caro, F. / Dimauro, P. / Nemani, L. / Fontana, A. / Kümmel, M. / Häußler, B. / Hartley, W. G. / Ayllon, A. Alvarez / Bertin, E. /
    Dubath, P. / Ferrari, F. / Ferreira, L. / Gavazzi, R. / Hernández-Lang, D. / Lucatelli, G. / Robotham, A. S. G. / Schefer, M. / Tortora, C. / Aghanim, N. / Amara, A. / Amendola, L. / Auricchio, N. / Baldi, M. / Bender, R. / Bodendorf, C. / Branchini, E. / Brescia, M. / Camera, S. / Capobianco, V. / Carbone, C. / Carretero, J. / Castander, F. J. / Cavuoti, S. / Cimatti, A. / Cledassou, R. / Congedo, G. / Conversi, L. / Copin, Y. / Corcione, L. / Courbin, F.

    2022  

    Abstract: The ESA Euclid mission will provide high-quality imaging for about 1.5 billion galaxies. A software pipeline to automatically process and analyse such a huge amount of data in real time is being developed by the Science Ground Segment of the Euclid ... ...

    Abstract The ESA Euclid mission will provide high-quality imaging for about 1.5 billion galaxies. A software pipeline to automatically process and analyse such a huge amount of data in real time is being developed by the Science Ground Segment of the Euclid Consortium; this pipeline will include a model-fitting algorithm, which will provide photometric and morphological estimates of paramount importance for the core science goals of the mission and for legacy science. The Euclid Morphology Challenge is a comparative investigation of the performance of five model-fitting software packages on simulated Euclid data, aimed at providing the baseline to identify the best suited algorithm to be implemented in the pipeline. In this paper we describe the simulated data set, and we discuss the photometry results. A companion paper (Euclid Collaboration: Bretonni\`ere et al. 2022) is focused on the structural and morphological estimates. We created mock Euclid images simulating five fields of view of 0.48 deg2 each in the $I_E$ band of the VIS instrument, each with three realisations of galaxy profiles (single and double S\'ersic, and 'realistic' profiles obtained with a neural network); for one of the fields in the double S\'ersic realisation, we also simulated images for the three near-infrared $Y_E$, $J_E$ and $H_E$ bands of the NISP-P instrument, and five Rubin/LSST optical complementary bands ($u$, $g$, $r$, $i$, and $z$). To analyse the results we created diagnostic plots and defined ad-hoc metrics. Five model-fitting software packages (DeepLeGATo, Galapagos-2, Morfometryka, ProFit, and SourceXtractor++) were compared, all typically providing good results. (cut)

    Comment: 29 pages, 33 figures. Euclid pre-launch key paper. Companion paper: Bretonniere et al. 2022
    Schlagwörter Astrophysics - Astrophysics of Galaxies ; Astrophysics - Instrumentation and Methods for Astrophysics
    Thema/Rubrik (Code) 302
    Erscheinungsdatum 2022-09-26
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    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  2. Buch ; Online: Euclid

    Contarini, S. / Verza, G. / Pisani, A. / Hamaus, N. / Sahlén, M. / Carbone, C. / Dusini, S. / Marulli, F. / Moscardini, L. / Renzi, A. / Sirignano, C. / Stanco, L. / Aubert, M. / Bonici, M. / Castignani, G. / Courtois, H. M. / Escoffier, S. / Guinet, D. / Kovacs, A. /
    Lavaux, G. / Massara, E. / Nadathur, S. / Pollina, G. / Ronconi, T. / Ruppin, F. / Sakr, Z. / Veropalumbo, A. / Wandelt, B. D. / Amara, A. / Auricchio, N. / Baldi, M. / Bonino, D. / Branchini, E. / Brescia, M. / Brinchmann, J. / Camera, S. / Capobianco, V. / Carretero, J. / Castellano, M. / Cavuoti, S. / Cledassou, R. / Congedo, G. / Conselice, C. J. / Conversi, L. / Copin, Y. / Corcione, L. / Courbin, F. / Cropper, M. / Da Silva, A. / Degaudenzi, H.

    Cosmological forecasts from the void size function

    2022  

    Abstract: The Euclid mission $-$ with its spectroscopic galaxy survey covering a sky area over $15\,000 \ \mathrm{deg}^2$ in the redshift range $0.9

    Abstract The Euclid mission $-$ with its spectroscopic galaxy survey covering a sky area over $15\,000 \ \mathrm{deg}^2$ in the redshift range $0.9<z<1.8\ -$ will provide a sample of tens of thousands of cosmic voids. This paper explores for the first time the constraining power of the void size function on the properties of dark energy (DE) from a survey mock catalogue, the official Euclid Flagship simulation. We identify voids in the Flagship light-cone, which closely matches the features of the upcoming Euclid spectroscopic data set. We model the void size function considering a state-of-the art methodology: we rely on the volume conserving (Vdn) model, a modification of the popular Sheth & van de Weygaert model for void number counts, extended by means of a linear function of the large-scale galaxy bias. We find an excellent agreement between model predictions and measured mock void number counts. We compute updated forecasts for the Euclid mission on DE from the void size function and provide reliable void number estimates to serve as a basis for further forecasts of cosmological applications using voids. We analyse two different cosmological models for DE: the first described by a constant DE equation of state parameter, $w$, and the second by a dynamic equation of state with coefficients $w_0$ and $w_a$. We forecast $1\sigma$ errors on $w$ lower than $10\%$, and we estimate an expected figure of merit (FoM) for the dynamical DE scenario $\mathrm{FoM}_{w_0,w_a} = 17$ when considering only the neutrino mass as additional free parameter of the model. The analysis is based on conservative assumptions to ensure full robustness, and is a pathfinder for future enhancements of the technique. Our results showcase the impressive constraining power of the void size function from the Euclid spectroscopic sample, both as a stand-alone probe, and to be combined with other Euclid cosmological probes.<br />
    Comment: 19 pages, 7 figures, 4 tables - published in A&A
    Schlagwörter Astrophysics - Cosmology and Nongalactic Astrophysics
    Thema/Rubrik (Code) 612
    Erscheinungsdatum 2022-05-23
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    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  3. Buch ; Online: A Probabilistic Autoencoder for Type Ia Supernovae Spectral Time Series

    Stein, George / Seljak, Uros / Bohm, Vanessa / Aldering, G. / Antilogus, P. / Aragon, C. / Bailey, S. / Baltay, C. / Bongard, S. / Boone, K. / Buton, C. / Copin, Y. / Dixon, S. / Fouchez, D. / Gangler, E. / Gupta, R. / Hayden, B. / Hillebrandt, W. / Karmen, M. /
    Kim, A. G. / Kowalski, M. / Kusters, D. / Leget, P. F. / Mondon, F. / Nordin, J. / Pain, R. / Pecontal, E. / Pereira, R. / Perlmutter, S. / Ponder, K. A. / Rabinowitz, D. / Rigault, M. / Rubin, D. / Runge, K. / Saunders, C. / Smadja, G. / Suzuki, N. / Tao, C. / Thomas, R. C. / Vincenzi, M.

    2022  

    Abstract: We construct a physically-parameterized probabilistic autoencoder (PAE) to learn the intrinsic diversity of type Ia supernovae (SNe Ia) from a sparse set of spectral time series. The PAE is a two-stage generative model, composed of an Auto-Encoder (AE) ... ...

    Abstract We construct a physically-parameterized probabilistic autoencoder (PAE) to learn the intrinsic diversity of type Ia supernovae (SNe Ia) from a sparse set of spectral time series. The PAE is a two-stage generative model, composed of an Auto-Encoder (AE) which is interpreted probabilistically after training using a Normalizing Flow (NF). We demonstrate that the PAE learns a low-dimensional latent space that captures the nonlinear range of features that exists within the population, and can accurately model the spectral evolution of SNe Ia across the full range of wavelength and observation times directly from the data. By introducing a correlation penalty term and multi-stage training setup alongside our physically-parameterized network we show that intrinsic and extrinsic modes of variability can be separated during training, removing the need for the additional models to perform magnitude standardization. We then use our PAE in a number of downstream tasks on SNe Ia for increasingly precise cosmological analyses, including automatic detection of SN outliers, the generation of samples consistent with the data distribution, and solving the inverse problem in the presence of noisy and incomplete data to constrain cosmological distance measurements. We find that the optimal number of intrinsic model parameters appears to be three, in line with previous studies, and show that we can standardize our test sample of SNe Ia with an RMS of $0.091 \pm 0.010$ mag, which corresponds to $0.074 \pm 0.010$ mag if peculiar velocity contributions are removed. Trained models and codes are released at \href{https://github.com/georgestein/suPAErnova}{github.com/georgestein/suPAErnova}

    Comment: 23 pages, 8 Figures, 1 Table. Accepted to ApJ
    Schlagwörter Astrophysics - Cosmology and Nongalactic Astrophysics ; Computer Science - Machine Learning
    Erscheinungsdatum 2022-07-15
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    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  4. Buch ; Online: Euclid

    Pöntinen, M. / Granvik, M. / Nucita, A. A. / Conversi, L. / Altieri, B. / Carry, B. / O'Riordan, C. M. / Scott, D. / Aghanim, N. / Amara, A. / Amendola, L. / Auricchio, N. / Baldi, M. / Bonino, D. / Branchini, E. / Brescia, M. / Camera, S. / Capobianco, V. / Carbone, C. /
    Carretero, J. / Castellano, M. / Cavuoti, S. / Cimatti, A. / Cledassou, R. / Congedo, G. / Copin, Y. / Corcione, L. / Courbin, F. / Cropper, M. / Da Silva, A. / Degaudenzi, H. / Dinis, J. / Dubath, F. / Dupac, X. / Dusini, S. / Farrens, S. / Ferriol, S. / Frailis, M. / Franceschi, E. / Fumana, M. / Galeotta, S. / Garilli, B. / Gillard, W. / Gillis, B. / Giocoli, C. / Grazian, A. / Haugan, S. V. H. / Holmes, W. / Hormuth, F. / Hornstrup, A. / Jahnke, K. / Kümmel, M. / Kermiche, S. / Kiessling, A. / Kitching, T. / Kohley, R. / Kunz, M. / Kurki-Suonio, H. / Ligori, S. / Lilje, P. B. / Lloro, I. / Maiorano, E. / Mansutti, O. / Marggraf, O. / Markovic, K. / Marulli, F. / Massey, R. / Medinaceli, E. / Mei, S. / Melchior, M. / Mellier, Y. / Meneghetti, M. / Meylan, G. / Moresco, M. / Moscardini, L. / Munari, E. / Niemi, S. -M. / Nutma, T. / Padilla, C. / Paltani, S. / Pasian, F. / Pedersen, K. / Pettorino, V. / Pires, S. / Polenta, G. / Poncet, M. / Raison, F. / Renzi, A. / Rhodes, J. / Riccio, G. / Romelli, E. / Roncarelli, M. / Rossetti, E. / Saglia, R. / Sapone, D. / Sartoris, B. / Schneider, P. / Secroun, A. / Seidel, G. / Serrano, S. / Sirignano, C. / Sirri, G. / Stanco, L. / Tallada-Crespí, P. / Taylor, A. N. / Tereno, I. / Toledo-Moreo, R. / Torradeflot, F. / Tutusaus, I. / Valenziano, L. / Vassallo, T. / Kleijn, G. Verdoes / Wang, Y. / Weller, J. / Zamorani, G. / Zoubian, J. / Scottez, V.

    Identification of asteroid streaks in simulated images using deep learning

    2023  

    Abstract: Up to 150000 asteroids will be visible in the images of the ESA Euclid space telescope, and the instruments of Euclid offer multiband visual to near-infrared photometry and slitless spectra of these objects. Most asteroids will appear as streaks in the ... ...

    Abstract Up to 150000 asteroids will be visible in the images of the ESA Euclid space telescope, and the instruments of Euclid offer multiband visual to near-infrared photometry and slitless spectra of these objects. Most asteroids will appear as streaks in the images. Due to the large number of images and asteroids, automated detection methods are needed. A non-machine-learning approach based on the StreakDet software was previously tested, but the results were not optimal for short and/or faint streaks. We set out to improve the capability to detect asteroid streaks in Euclid images by using deep learning. We built, trained, and tested a three-step machine-learning pipeline with simulated Euclid images. First, a convolutional neural network (CNN) detected streaks and their coordinates in full images, aiming to maximize the completeness (recall) of detections. Then, a recurrent neural network (RNN) merged snippets of long streaks detected in several parts by the CNN. Lastly, gradient-boosted trees (XGBoost) linked detected streaks between different Euclid exposures to reduce the number of false positives and improve the purity (precision) of the sample. The deep-learning pipeline surpasses the completeness and reaches a similar level of purity of a non-machine-learning pipeline based on the StreakDet software. Additionally, the deep-learning pipeline can detect asteroids 0.25-0.5 magnitudes fainter than StreakDet. The deep-learning pipeline could result in a 50% increase in the number of detected asteroids compared to the StreakDet software. There is still scope for further refinement, particularly in improving the accuracy of streak coordinates and enhancing the completeness of the final stage of the pipeline, which involves linking detections across multiple exposures.

    Comment: 18 pages, 11 figures
    Schlagwörter Astrophysics - Earth and Planetary Astrophysics ; Astrophysics - Instrumentation and Methods for Astrophysics ; Computer Science - Machine Learning
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2023-10-05
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    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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