publications

2023

1. Enabling the discovery of fast transients. A kilonova science module for the Fink broker

   B. Biswas, E. E. O. Ishida, J. Peloton, and 4 more authors

   \aap, Sep 2023
2. Ready for O4 II: GRANDMA Observations of Swift GRBs during eight-weeks of Spring 2022

   I. Tosta e Melo, J. -G. Ducoin, Z. Vidadi, and 106 more authors

   arXiv e-prints, Oct 2023
3. Broad-emission-line dominated hydrogen-rich luminous supernovae

   P. J. Pessi, J. P. Anderson, G. Folatelli, and 27 more authors

   \mnras, Aug 2023
4. Core-collapse supernovae in the Dark Energy Survey: luminosity functions and host galaxy demographics

   M. Grayling, C. P. Gutiérrez, M. Sullivan, and 74 more authors

   \mnras, Mar 2023
5. Radio transients and variables in the tenth Deeper, Wider, Faster observing run

   D. Dobie, J. Pritchard, Y. Wang, and 23 more authors

   \mnras, Mar 2023
6. Concerning colour: The effect of environment on type Ia supernova colour in the dark energy survey

   L. Kelsey, M. Sullivan, P. Wiseman, and 68 more authors

   \mnras, Feb 2023
7. The Dark Energy Survey Supernova Program results: type Ia supernova brightness correlates with host galaxy dust

   C. Meldorf, A. Palmese, D. Brout, and 70 more authors

   \mnras, Jan 2023
8. The Dark Energy Survey supernova program: cosmological biases from supernova photometric classification

   M. Vincenzi, M. Sullivan, A. Möller, and 82 more authors

   \mnras, Jan 2023
9. DeepZipper. II. Searching for Lensed Supernovae in Dark Energy Survey Data with Deep Learning

   R. Morgan, B. Nord, K. Bechtol, and 64 more authors

   \apj, Jan 2023

2022

1. The dark energy survey 5-yr photometrically identified type Ia supernovae

   A. Möller, M. Smith, M. Sako, and 3 more authors

   MNRAS, Aug 2022

   Abs HTML

   As part of the cosmology analysis using Type Ia Supernovae (SN Ia) in the Dark Energy Survey (DES), we present photometrically identified SN Ia samples using multi-band light-curves and host galaxy redshifts. For this analysis, we use the photometric classification framework SuperNNova (SNN; Möller et al. 2019) trained on realistic DES-like simulations. For reliable classification, we process the DES SN programme (DES-SN) data and introduce improvements to the classifier architecture, obtaining classification accuracies of more than 98 per cent on simulations. This is the first SN classification to make use of ensemble methods, resulting in more robust samples. Using photometry, host galaxy redshifts, and a classification probability requirement, we identify 1,863 SNe Ia from which we select 1,484 cosmology-grade SNe Ia spanning the redshift range of 0.07 < z < 1.14. We find good agreement between the light- curve properties of the photometrically-selected sample and simulations. Additionally, we create similar SN Ia samples using two types of Bayesian Neural Network classifiers that provide uncertainties on the classification probabilities. We test the feasibility of using these uncertainties as indicators for out- of-distribution candidates and model confidence. Finally, we discuss the implications of photometric samples and classification methods for future surveys such as Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST).

2. Fink: Early supernovae Ia classification using active learning

   M. Leoni, E. E. O. Ishida, J. Peloton, and 1 more author

   A & A, Jul 2022

   Abs HTML

   We describe how the Fink broker early supernova Ia classifier optimizes its ML classifications by employing an active learning (AL) strategy. We demonstrate the feasibility of implementation of such strategies in the current Zwicky Transient Facility (ZTF) public alert data stream. We compare the performance of two AL strategies: uncertainty sampling and random sampling. Our pipeline consists of 3 stages: feature extraction, classification and learning strategy. Starting from an initial sample of 10 alerts (5 SN Ia and 5 non-Ia), we let the algorithm identify which alert should be added to the training sample. The system is allowed to evolve through 300 iterations. Our data set consists of 23 840 alerts from the ZTF with confirmed classification via cross-match with SIMBAD database and the Transient name server (TNS), 1 600 of which were SNe Ia (1 021 unique objects). The data configuration, after the learning cycle was completed, consists of 310 alerts for training and 23 530 for testing. Averaging over 100 realizations, the classifier achieved 89% purity and 54% efficiency. From 01/November/2020 to 31/October/2021 Fink has applied its early supernova Ia module to the ZTF stream and communicated promising SN Ia candidates to the TNS. From the 535 spectroscopically classified Fink candidates, 459 (86%) were proven to be SNe Ia. Our results confirm the effectiveness of active learning strategies for guiding the construction of optimal training samples for astronomical classifiers. It demonstrates in real data that the performance of learning algorithms can be highly improved without the need of extra computational resources or overwhelmingly large training samples. This is, to our knowledge, the first application of AL to real alerts data.

3. The Dark Energy Survey Supernova Program: Cosmological biases from supernova photometric classification

   M. Vincenzi, M. Sullivan, and A. et al. Möller

   MNRAS, Jun 2022

   Abs HTML

   Cosmological analyses of samples of photometrically-identified Type Ia supernovae (SNe Ia) depend on understanding the effects of ’contamination’ from core-collapse and peculiar SN Ia events. We employ a rigorous analysis on state-of-the-art simulations of photometrically identified SN Ia samples and determine cosmological biases due to such ’non-Ia’ contamination in the Dark Energy Survey (DES) 5-year SN sample. As part of the analysis, we test on our DES simulations the performance of SuperNNova, a photometric SN classifier based on recurrent neural networks. Depending on the choice of non-Ia SN models in both the simulated data sample and training sample, contamination ranges from 0.8-3.5 %, with the efficiency of the classification from 97.7-99.5 %. Using the Bayesian Estimation Applied to Multiple Species (BEAMS) framework and its extension ’BEAMS with Bias Correction’ (BBC), we produce a redshift-binned Hubble diagram marginalised over contamination and corrected for selection effects and we use it to constrain the dark energy equation-of-state, w. Assuming a flat universe with Gaussian \Omega_M prior of 0.311\pm0.010, we show that biases on w are <0.008 when using SuperNNova and accounting for a wide range of non-Ia SN models in the simulations. Systematic uncertainties associated with contamination are estimated to be at most \sigma_w, \mathrmsyst=0.004. This compares to an expected statistical uncertainty of \sigma_w,\mathrmstat=0.039 for the DES-SN sample, thus showing that contamination is not a limiting uncertainty in our analysis. We also measure biases due to contamination on w_0 and w_a (assuming a flat universe), and find these to be <0.009 in w_0 and <0.108 in w_a, hence 5 to 10 times smaller than the statistical uncertainties expected from the DES-SN sample.

4. Using host galaxy spectroscopy to explore systematics in the standardization of Type Ia supernovae

   M. Dixon, C. Lidman, J. Mould, and 75 more authors

   \mnras, Dec 2022
5. Enabling the discovery of fast transients: A kilonova science module for the Fink broker

   B. Biswas, E. E. O. Ishida, J. Peloton, and 4 more authors

   arXiv e-prints, Oct 2022
6. GRANDMA observations of ZTF/Fink transients during summer 2021

   V. Aivazyan, M. Almualla, S. Antier, and 80 more authors

   \mnras, Oct 2022
7. Type Ia supernova Hubble diagrams with host galaxy photometric redshifts

   V. Ruhlmann-Kleider, C. Lidman, and A. Möller

   \jcap, Oct 2022
8. The Pantheon+ Analysis: Cosmological Constraints

   Dillon Brout, Dan Scolnic, Brodie Popovic, and 45 more authors

   \apj, Oct 2022
9. Measuring Cosmological Parameters with Type Ia Supernovae in redMaGiC Galaxies

   R. Chen, D. Scolnic, E. Rozo, and 82 more authors

   \apj, Oct 2022
10. A galaxy-driven model of type Ia supernova luminosity variations

    P. Wiseman, M. Vincenzi, M. Sullivan, and 60 more authors

    \mnras, Sep 2022
11. Rubin Observatory LSST Transients and Variable Stars Roadmap

    Kelly M. Hambleton, Federica B. Bianco, Rachel Street, and 79 more authors

    arXiv e-prints, Aug 2022
12. The GRANDMA network in preparation for the fourth gravitational-wave observing run

    S. Agayeva, V. Aivazyan, S. Alishov, and 99 more authors

    In Observatory Operations: Strategies, Processes, and Systems IX, Aug 2022
13. The dark energy survey 5-yr photometrically identified type Ia supernovae

    A. Möller, M. Smith, M. Sako, and 80 more authors

    \mnras, Aug 2022
14. Science Cases for the Keck Wide-Field Imager

    J. Cooke, C. Angus, K. Auchettl, and 22 more authors

    arXiv e-prints, Jul 2022
15. A Convolutional Neural Network Approach to Supernova Time-Series Classification

    Helen Qu, Masao Sako, Anais Moller, and 1 more author

    arXiv e-prints, Jul 2022
16. Bayesian Neural Networks for classification tasks in the Rubin big data era

    Anais Möller, and Thibault de Boissière

    arXiv e-prints, Jul 2022
17. Fink: Early supernovae Ia classification using active learning

    M. Leoni, E. E. O. Ishida, J. Peloton, and 1 more author

    \aap, Jul 2022
18. Fink: incorporating expert knowledge in machine learning applications for large scale sky surveys

    Emille E. O. Ishida, Julien Peloton, and Anais Moller

    In SciOps 2022: Artificial Intelligence for Science and Operations in Astronomy (SCIOPS). Proceedings of the ESA/ESO SCOPS Workshop held 16-20 May, May 2022
19. DeepZipper: A Novel Deep-learning Architecture for Lensed Supernovae Identification

    R. Morgan, B. Nord, K. Bechtol, and 48 more authors

    \apj, Mar 2022
20. Dark Energy Survey Year 3 results: A 2.7% measurement of baryon acoustic oscillation distance scale at redshift 0.835

    T. M. C. Abbott, M. Aguena, S. Allam, and 110 more authors

    \prd, Feb 2022
21. OzDES reverberation mapping program: Lag recovery reliability for 6-yr C IV analysis

    A. Penton, U. Malik, T. M. Davis, and 69 more authors

    \mnras, Jan 2022
22. Dark Energy Survey Year 3 results: galaxy sample for BAO measurement

    A. Carnero Rosell, M. Rodriguez-Monroy, M. Crocce, and 107 more authors

    \mnras, Jan 2022

2021

1. OzDES Reverberation Mapping Programme: the first Mg II lags from 5 yr of monitoring

   Zhefu Yu, Paul Martini, A. Penton, and 63 more authors

   \mnras, Nov 2021
2. Rates and delay times of Type Ia supernovae in the Dark Energy Survey

   P. Wiseman, M. Sullivan, M. Smith, and 88 more authors

   \mnras, Sep 2021
3. Understanding the extreme luminosity of DES14X2fna

   M. Grayling, C. P. Gutiérrez, M. Sullivan, and 68 more authors

   \mnras, Aug 2021
4. The Dark Energy Survey supernova programme: modelling selection efficiency and observed core-collapse supernova contamination

   M. Vincenzi, M. Sullivan, O. Graur, and 80 more authors

   \mnras, Aug 2021
5. SCONE: Supernova Classification with a Convolutional Neural Network

   Helen Qu, Masao Sako, Anais Möller, and 1 more author

   \aj, Aug 2021
6. The first Hubble diagram and cosmological constraints using superluminous supernovae

   C. Inserra, M. Sullivan, C. R. Angus, and 89 more authors

   \mnras, Jun 2021
7. SkyMapper optical follow-up of gravitational wave triggers: Alert science data pipeline and LIGO/Virgo O3 run

   Seo-Won Chang, Christopher A. Onken, Christian Wolf, and 7 more authors

   \pasa, May 2021
8. The effect of environment on Type Ia supernovae in the Dark Energy Survey three-year cosmological sample

   L. Kelsey, M. Sullivan, M. Smith, and 74 more authors

   \mnras, Mar 2021
9. FINK, a new generation of broker for the LSST community

   Anais Möller, Julien Peloton, Emille E. O. Ishida, and 33 more authors

   \mnras, Mar 2021
10. DES Y1 results: Splitting growth and geometry to test \ensuremathΛ CDM

    J. Muir, E. Baxter, V. Miranda, and 101 more authors

    \prd, Jan 2021

2020

1. First Cosmology Results using Supernovae Ia from the Dark Energy Survey: Survey Overview, Performance, and Supernova Spectroscopy

   M. Smith, C. B. D’Andrea, M. Sullivan, and 112 more authors

   \aj, Dec 2020
2. Active learning with RESSPECT: Resource allocation for extragalactic astronomical transients

   Noble Kennamer, Emille E. O. Ishida, Santiago Gonzalez-Gaitan, and 12 more authors

   arXiv e-prints, Oct 2020
3. The host galaxies of 106 rapidly evolving transients discovered by the Dark Energy Survey

   P. Wiseman, M. Pursiainen, M. Childress, and 74 more authors

   \mnras, Oct 2020
4. Weak lensing of Type Ia Supernovae from the Dark Energy Survey

   E. Macaulay, D. Bacon, R. C. Nichol, and 70 more authors

   \mnras, Aug 2020
5. Dark Energy Survey identification of a low-mass active galactic nucleus at redshift 0.823 from optical variability

   Hengxiao Guo, Colin J. Burke, Xin Liu, and 57 more authors

   \mnras, Aug 2020
6. DES16C3cje: A low-luminosity, long-lived supernova

   C. P. Gutiérrez, M. Sullivan, L. Martinez, and 91 more authors

   \mnras, Jul 2020
7. OzDES multi-object fibre spectroscopy for the Dark Energy Survey: results and second data release

   C. Lidman, B. E. Tucker, T. M. Davis, and 102 more authors

   \mnras, Jul 2020
8. Studying Type II supernovae as cosmological standard candles using the Dark Energy Survey

   T. de Jaeger, L. Galbany, S. González-Gaitán, and 86 more authors

   \mnras, Jul 2020
9. Supernova host galaxies in the dark energy survey: I. Deep coadds, photometry, and stellar masses

   P. Wiseman, M. Smith, M. Childress, and 81 more authors

   \mnras, Jul 2020
10. The mystery of photometric twins DES17X1boj and DES16E2bjy

    M. Pursiainen, C. P. Gutiérrez, P. Wiseman, and 81 more authors

    \mnras, Jun 2020
11. Supernova Siblings: Assessing the Consistency of Properties of Type Ia Supernovae that Share the Same Parent Galaxies

    D. Scolnic, M. Smith, A. Massiah, and 83 more authors

    \apjl, Jun 2020
12. First cosmology results using type Ia supernovae from the Dark Energy Survey: the effect of host galaxy properties on supernova luminosity

    M. Smith, M. Sullivan, P. Wiseman, and 89 more authors

    \mnras, May 2020
13. Probing the extragalactic fast transient sky at minute time-scales with DECam

    I. Andreoni, J. Cooke, S. Webb, and 22 more authors

    \mnras, Feb 2020
14. Peculiar velocity cosmology with type Ia supernovae

    R. Graziani, M. Rigault, N. Regnault, and 20 more authors

    arXiv e-prints, Jan 2020
15. SuperNNova: an open-source framework for Bayesian, neural network-based supernova classification

    A. Möller, and T. de Boissière

    \mnras, Jan 2020
16. Quasar Accretion Disk Sizes from Continuum Reverberation Mapping in the DES Standard-star Fields

    Zhefu Yu, Paul Martini, T. M. Davis, and 72 more authors

    \apjs, Jan 2020

2019

1. C IV black hole mass measurements with the Australian Dark Energy Survey (OzDES)

   J. K. Hoormann, P. Martini, T. M. Davis, and 67 more authors

   \mnras, Aug 2019
2. First Results of the SkyMapper Transient Survey

   A. Möller, B. E. Tucker, P. Armstrong, and 13 more authors

   In Southern Horizons in Time-Domain Astronomy, Aug 2019
3. VizieR Online Data Catalog: griz light curves of 15 DES quasars (Mudd+, 2018)

   D. Mudd, P. Martini, Y. Zu, and 69 more authors

   VizieR Online Data Catalog, Jul 2019
4. A fast radio burst with frequency-dependent polarization detected during Breakthrough Listen observations

   D. C. Price, G. Foster, M. Geyer, and 30 more authors

   \mnras, Jul 2019
5. First cosmological results using Type Ia supernovae from the Dark Energy Survey: measurement of the Hubble constant

   E. Macaulay, R. C. Nichol, D. Bacon, and 107 more authors

   \mnras, Jun 2019
6. First cosmology results using Type IA supernovae from the dark energy survey: effects of chromatic corrections to supernova photometry on measurements of cosmological parameters

   J. Lasker, R. Kessler, D. Scolnic, and 74 more authors

   \mnras, Jun 2019
7. Cosmological Constraints from Multiple Probes in the Dark Energy Survey

   T. M. C. Abbott, A. Alarcon, S. Allam, and 161 more authors

   \prl, May 2019
8. First cosmology results using Type Ia supernova from the Dark Energy Survey: simulations to correct supernova distance biases

   R. Kessler, D. Brout, C. B. D’Andrea, and 104 more authors

   \mnras, May 2019
9. Steve: A Hierarchical Bayesian Model for Supernova Cosmology

   S. R. Hinton, T. M. Davis, A. G. Kim, and 66 more authors

   \apj, May 2019
10. First Cosmology Results Using SNe Ia from the Dark Energy Survey: Analysis, Systematic Uncertainties, and Validation

    D. Brout, D. Scolnic, R. Kessler, and 123 more authors

    \apj, Apr 2019
11. Erratum: FRB microstructure revealed by the real-time detection of FRB170827

    W. Farah, C. Flynn, M. Bailes, and 24 more authors

    \mnras, Mar 2019
12. First Cosmology Results Using Type Ia Supernovae from the Dark Energy Survey: Photometric Pipeline and Light-curve Data Release

    D. Brout, M. Sako, D. Scolnic, and 90 more authors

    \apj, Mar 2019
13. First Cosmology Results using Type Ia Supernovae from the Dark Energy Survey: Constraints on Cosmological Parameters

    T. M. C. Abbott, S. Allam, P. Andersen, and 142 more authors

    \apjl, Feb 2019

2018

1. Rapidly evolving transients in the Dark Energy Survey

   M. Pursiainen, M. Childress, M. Smith, and 80 more authors

   \mnras, Nov 2018
2. A nearby super-luminous supernova with a long pre-maximum & “plateau” and strong C II features

   J. P. Anderson, P. J. Pessi, L. Dessart, and 63 more authors

   \aap, Nov 2018
3. DES science portal: Computing photometric redshifts

   J. Gschwend, A. C. Rossel, R. L. C. Ogando, and 68 more authors

   Astronomy and Computing, Oct 2018
4. Dark Energy Survey year 1 results: Cosmological constraints from galaxy clustering and weak lensing

   T. M. C. Abbott, F. B. Abdalla, A. Alarcon, and 198 more authors

   \prd, Aug 2018
5. Dark Energy Survey year 1 results: Galaxy clustering for combined probes

   J. Elvin-Poole, M. Crocce, A. J. Ross, and 124 more authors

   \prd, Aug 2018
6. Quasar Accretion Disk Sizes from Continuum Reverberation Mapping from the Dark Energy Survey

   D. Mudd, P. Martini, Y. Zu, and 70 more authors

   \apj, Aug 2018
7. FRB microstructure revealed by the real-time detection of FRB170827

   W. Farah, C. Flynn, M. Bailes, and 24 more authors

   \mnras, Jul 2018
8. Dark Energy Survey Year 1 Results: redshift distributions of the weak-lensing source galaxies

   B. Hoyle, D. Gruen, G. M. Bernstein, and 136 more authors

   \mnras, Jul 2018
9. Dependence of Type Ia supernova luminosities on their local environment

   M. Roman, D. Hardin, M. Betoule, and 18 more authors

   \aap, Jul 2018
10. VizieR Online Data Catalog: SNe Ia spectra from SNLS (Balland+, 2018)

    C. Balland, F. Cellier-Holzem, C. Lidman, and 24 more authors

    VizieR Online Data Catalog, Jun 2018
11. Dark Energy Survey Year 1 results: cross-correlation redshifts - methods and systematics characterization

    M. Gatti, P. Vielzeuf, C. Davis, and 104 more authors

    \mnras, Jun 2018
12. The ESO’s VLT type Ia supernova spectral set of the final two years of SNLS

    C. Balland, F. Cellier-Holzem, C. Lidman, and 24 more authors

    \aap, Jun 2018
13. New Science, New Media: An Assessment of the Online Education and Public Outreach Initiatives of The Dark Energy Survey

    R. C. Wolf, A. K. Romer, B. Nord, and 25 more authors

    arXiv e-prints, Apr 2018
14. VizieR Online Data Catalog: Type Ia supernova luminosities (Roman+, 2018)

    M. Roman, D. Hardin, M. Betoule, and 18 more authors

    VizieR Online Data Catalog, Mar 2018
15. Studying the Ultraviolet Spectrum of the First Spectroscopically Confirmed Supernova at Redshift Two

    M. Smith, M. Sullivan, R. C. Nichol, and 89 more authors

    \apj, Feb 2018
16. How Many Kilonovae Can Be Found in Past, Present, and Future Survey Data Sets?

    D. Scolnic, R. Kessler, D. Brout, and 83 more authors

    \apjl, Jan 2018

2017

1. Follow Up of GW170817 and Its Electromagnetic Counterpart by Australian-Led Observing Programmes

   I. Andreoni, K. Ackley, J. Cooke, and 122 more authors

   \pasa, Dec 2017
2. Convolutional neural networks for transient candidate vetting in large-scale surveys

   Fabian Gieseke, Steven Bloemen, Cas van den Bogaard, and 9 more authors

   \mnras, Dec 2017
3. OzDES multifibre spectroscopy for the Dark Energy Survey: 3-yr results and first data release

   M. J. Childress, C. Lidman, T. M. Davis, and 98 more authors

   \mnras, Nov 2017
4. Multi-messenger Observations of a Binary Neutron Star Merger

   B. P. Abbott, R. Abbott, T. D. Abbott, and 3674 more authors

   \apjl, Oct 2017
5. VizieR Online Data Catalog: OzDES DR1 (Childress+, 2017)

   M. J. Childress, C. Lidman, T. M. Davis, and 98 more authors

   VizieR Online Data Catalog, Sep 2017
6. VizieR Online Data Catalog: OzDES DR1 (Childress+, 2017)

   M. J. Childress, C. Lidman, T. M. Davis, and 98 more authors

   VizieR Online Data Catalog, Sep 2017
7. The SkyMapper Transient Survey

   R. A. Scalzo, F. Yuan, M. J. Childress, and 9 more authors

   \pasa, Jul 2017
8. Diffuse Galactic antimatter from faint thermonuclear supernovae in old stellar populations

   Roland M. Crocker, Ashley J. Ruiter, Ivo R. Seitenzahl, and 10 more authors

   Nature Astronomy, Jun 2017
9. Constraining the \ensuremathΛCDM and Galileon models with recent cosmological data

   J. Neveu, V. Ruhlmann-Kleider, P. Astier, and 4 more authors

   \aap, Apr 2017

2016

1. Photometric classification of type Ia supernovae in the SuperNova Legacy Survey with supervised learning

   A. Möller, V. Ruhlmann-Kleider, C. Leloup, and 6 more authors

   \jcap, Dec 2016

2015

1. SNIa detection in the SNLS photometric analysis using Morphological Component Analysis

   A. Möller, V. Ruhlmann-Kleider, F. Lanusse, and 3 more authors

   \jcap, Apr 2015

2014

1. First experimental constraints on the disformally coupled Galileon model

   J. Neveu, V. Ruhlmann-Kleider, P. Astier, and 6 more authors

   \aap, Sep 2014
2. Improved cosmological constraints from a joint analysis of the SDSS-II and SNLS supernova samples

   M. Betoule, R. Kessler, J. Guy, and 60 more authors

   \aap, Aug 2014
3. VizieR Online Data Catalog: Joint analysis of the SDSS-II and SNLS SNe Ia (Betoule+, 2014)

   M. Betoule, R. Kessler, J. Guy, and 60 more authors

   VizieR Online Data Catalog, Jun 2014