Anais Möller

I am driven to understand the nature of Dark Energy and the physics of transients in the Universe. To do so, I work with extremely large astronomical datasets. To maximize the information we can use to understand our Universe, I develop innovative tools with a large emphasis on machine learning.

Research interests:

I am co-leading Fink broker which will process, enrich and filter transient data from the largest optical survey in the world at the Vera C. Rubin Observatory.
I obtain constraints on the nature of Dark Energy with Type Ia Supernovae at the Dark Energy Survey.
I study multi-wavelength and messenger fast-transients with the world’s largest transient collaboration the Deeper Wider Faster program.
I am an Associate Investigator at the ARC Centre of Excellence for Gravitational Wave Discovery where I work on the optical and multi-wavelength follow-up of gravitational waves events.
I develop high-end machine learning algorithms for astronomy and cosmology such as the photometric classification framework SuperNNova.

I am currently a Postdoctoral Research Fellow at Swinburne University Centre for Astrophysics and Supercomputing in Australia. I previously worked for CNRS at Laboratoire de Physique de Clermont (LPC), and at the Australian National University as an ARC CoE CAASTRO Postdoctoral Fellow in Dark Energy.

I was born in Venezuela and did my undergrad at Universidad Simón Bolívar. I then moved to France and did the NPAC master and a PhD at Irfu Cea Saclay and Université Paris Diderot. I enjoy communicating science and was selected to be a CAASTRO Astronomer in Residence at Uluru, Australia. I speak spanish, english, french and german. I also ejoy hiking, rock climbing, diving, sailing and playing cello.

news

Sep 1, 2021	New job alert ! I have joined the Swinburne University Centre for Astrophysics and Supercomputing as Postdoctoral Research Fellow.
Aug 13, 2021	Fink has been selected to process the Vera C. Rubin Observatory LSST alert stream for its 10-years of operation. Fink will enable transient science in this big data era, including SNe Ia cosmology and beyond!

selected publications

The Dark Energy Survey 5-year photometrically identified Type Ia Supernovae

Möller, A., Smith, M., Sako, M., Sullivan, M., Vincenzi, M., and Wiseman, et al.

arXiv e-prints Jan 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).
Fink: early supernovae Ia classification using active learning

Leoni, Marco, Ishida, Emille E. O., Peloton, Julien, and Möller, Anais

arXiv e-prints Nov 2021

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

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

arXiv e-prints Nov 2021

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.