arXiv subjects

Anais Möller

Publications and source records attributed to Anais Möller.

33 records · Page 2Linked to original sources

arXiv2024

Recovered SN Ia rate from simulated LSST images

The Legacy Survey of Space and Time (LSST) will revolutionize Time Domain Astronomy by detecting millions of transients. In particular, it is expected to increment the number of type Ia supernovae (SNIa) of a factor of 100 compared to existing samples up to z~1.2. Such a high number of events will dramatically reduce statistical uncertainties in the analysis of SNIa properties and rates. However, the impact of all other sources of uncertainty on the measurement must still be evaluated. The comprehension and reduction of such uncertainties will be fundamental both for cosmology and stellar evolution studies, as measuring the SNIa rate can put constraints on the evolutionary scenarios of different SNIa progenitors. We use simulated data from the DESC Data Challenge 2 (DC2) and LSST Data Preview 0 (DP0) to measure the SNIa rate on a 15 deg2 region of the Wide-Fast-Deep area. We select a sample of SN candidates detected on difference images, associate them to the host galaxy, and retrieve their photometric redshifts (z-phot). Then, we test different light curves classification methods, with and without redshift priors. We discuss how the distribution in redshift measured for the SN candidates changes according to the selected host galaxy and redshift estimate. We measure the SNIa rate analyzing the impact of uncertainties due to z-phot, host galaxy association and classification on the distribution in redshift of the starting sample. We found a 17% average lost fraction of SNIa with respect to the simulated sample. As 10% of the bias is due to the uncertainty on the z-phot alone (which also affects classification when used as a prior), it results to be the major source of uncertainty. We discuss possible reduction of the errors in the measurement of the SNIa rate, including synergies with other surveys, which may help using the rate to discriminate different progenitor models.

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arXiv2022

A very luminous jet from the disruption of a star by a massive black hole

Tidal disruption events (TDEs) are bursts of electromagnetic energy released when supermassive black holes (SMBHs) at the centers of galaxies violently disrupt a star that passes too close. TDEs provide a new window to study accretion onto SMBHs; in some rare cases, this accretion leads to launching of a relativistic jet, but the necessary conditions are not fully understood. The best studied jetted TDE to date is Swift J1644+57, which was discovered in gamma-rays, but was too obscured by dust to be seen at optical wavelengths. Here we report the optical discovery of AT2022cmc, a rapidly fading source at cosmological distance (redshift z=1.19325) whose unique lightcurve transitioned into a luminous plateau within days. Observations of a bright counterpart at other wavelengths, including X-rays, sub-millimeter, and radio, supports the interpretation of AT2022cmc as a jetted TDE containing a synchrotron "afterglow", likely launched by a SMBH with spin $a \gtrsim 0.3$. Using 4 years of Zwicky Transient Facility (ZTF) survey data, we calculate a rate of $0.02 ^{+ 0.04 }_{- 0.01 }$ Gpc$^{-3}$ yr$^{-1}$ for on-axis jetted TDEs based on the luminous, fast-fading red component, thus providing a measurement complementary to the rates derived from X-ray and radio observations. Correcting for the beaming angle effects, this rate confirms that about 1% of TDEs have relativistic jets. Optical surveys can use AT2022cmc as a prototype to unveil a population of jetted TDEs.

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arXiv2022

The Pantheon+ Analysis: Cosmological Constraints

We present constraints on cosmological parameters from the Pantheon+ analysis of 1701 light curves of 1550 distinct Type Ia supernovae (SNe Ia) ranging in redshift from $z=0.001$ to 2.26. This work features an increased sample size, increased redshift span, and improved treatment of systematic uncertainties in comparison to the original Pantheon analysis and results in a factor of two improvement in cosmological constraining power. For a Flat$Λ$CDM model, we find $Ω_M=0.334\pm0.018$ from SNe Ia alone. For a Flat$w_0$CDM model, we measure $w_0=-0.90\pm0.14$ from SNe Ia alone, H$_0=73.5\pm1.1$ km s$^{-1}$ Mpc$^{-1}$ when including the Cepheid host distances and covariance (SH0ES), and $w_0=-0.978^{+0.024}_{-0.031}$ when combining the SN likelihood with constraints from the cosmic microwave background (CMB) and baryon acoustic oscillations (BAO); both $w_0$ values are consistent with a cosmological constant. We also present the most precise measurements to date on the evolution of dark energy in a Flat$w_0w_a$CDM universe, and measure $w_a=-0.1^{+0.9}_{-2.0}$ from Pantheon+ alone, H$_0=73.3\pm1.1$ km s$^{-1}$ Mpc$^{-1}$ when including SH0ES, and $w_a=-0.65^{+0.28}_{-0.32}$ when combining Pantheon+ with CMB and BAO data. Finally, we find that systematic uncertainties in the use of SNe Ia along the distance ladder comprise less than one third of the total uncertainty in the measurement of H$_0$ and cannot explain the present "Hubble tension" between local measurements and early-Universe predictions from the cosmological model.

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arXiv2022

Bayesian Neural Networks for classification tasks in the Rubin big data era

Upcoming surveys such as the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will detect up to 10 million time-varying sources in the sky every night for ten years. This information will be transmitted in a continuous stream to brokers that will select the most promising events for a variety of science cases using machine learning algorithms. We study the benefits and challenges of Bayesian Neural Networks (BNNs) for this type of classification tasks. BNNs are found to be accurate classifiers which also provide additional information: they quantify the classification uncertainty which can be harnessed to analyse this upcoming data avalanche more efficiently.

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arXiv2022

The Dark Energy Survey Supernova Program results: Type Ia Supernova brightness correlates with host galaxy dust

Cosmological analyses with type Ia supernovae (SNe Ia) often assume a single empirical relation between color and luminosity ($β$) and do not account for varying host-galaxy dust properties. However, from studies of dust in large samples of galaxies, it is known that dust attenuation can vary significantly. Here we take advantage of state-of-the-art modeling of galaxy properties to characterize dust parameters (dust attenuation $A_V$, and a parameter describing the dust law slope $R_V$) for the Dark Energy Survey (DES) SN Ia host galaxies using the publicly available \texttt{BAGPIPES} code. Utilizing optical and infrared data of the hosts alone, we find three key aspects of host dust that impact SN Ia cosmology: 1) there exists a large range ($\sim1-6$) of host $R_V$ 2) high stellar mass hosts have $R_V$ on average $\sim0.7$ lower than that of low-mass hosts 3) there is a significant ($>3σ$) correlation between the Hubble diagram residuals of red SNe Ia that when corrected for reduces scatter by $\sim13\%$ and the significance of the ``mass step'' to $\sim1σ$. These represent independent confirmations of recent predictions based on dust that attempted to explain the puzzling ``mass step'' and intrinsic scatter ($σ_{\rm int}$) in SN Ia analyses. We also find that red-sequence galaxies have both lower and more peaked dust law slope distributions on average in comparison to non red-sequence galaxies. We find that the SN Ia $β$ and $σ_{\rm int}$ both differ by $>3σ$ when determined separately for red-sequence galaxy and all other galaxy hosts. The agreement between fitted host-$R_V$ and SN Ia $β$ \& $σ_{\rm int}$ suggests that host dust properties play a major role in SN Ia color-luminosity standardization and supports the claim that SN Ia intrinsic scatter is driven by $R_V$ variation.

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arXiv2022

Fink: early supernovae Ia classification using active learning

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.

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arXiv2021

OzDES Reverberation Mapping Program: Lag recovery reliability for 6-year CIV analysis

We present the statistical methods that have been developed to analyse the OzDES reverberation mapping sample. To perform this statistical analysis we have created a suite of customisable simulations that mimic the characteristics of each source in the OzDES sample. These characteristics include: the variability in the photometric and spectroscopic lightcurves, the measurement uncertainties, and the observational cadence. By simulating the sources in the OzDES sample that contain the CIV emission line, we developed a set of criteria that rank the reliability of a recovered time lag depending on the agreement between different recovery methods, the magnitude of the uncertainties, and the rate at which false positives were found in the simulations. These criteria were applied to simulated light curves and these results used to estimate the quality of the resulting Radius-Luminosity relation.We grade the results using three quality levels (gold, silver and bronze). The input slope of the R-L relation was recovered within $1σ$ for each of the three quality samples, with the gold standard having the lowest dispersion with a recovered a R-L relation slope of $0.454\pm 0.016$ with an input slope of 0.47. Future work will apply these methods to the entire OzDES sample of 771 AGN.

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arXiv2021

SCONE: Supernova Classification with a Convolutional Neural Network

We present a novel method of classifying Type Ia supernovae using convolutional neural networks, a neural network framework typically used for image recognition. Our model is trained on photometric information only, eliminating the need for accurate redshift data. Photometric data is pre-processed via 2D Gaussian process regression into two-dimensional images created from flux values at each location in wavelength-time space. These "flux heatmaps" of each supernova detection, along with "uncertainty heatmaps" of the Gaussian process uncertainty, constitute the dataset for our model. This preprocessing step not only smooths over irregular sampling rates between filters but also allows SCONE to be independent of the filter set on which it was trained. Our model has achieved impressive performance without redshift on the in-distribution SNIa classification problem: $99.73 \pm 0.26$% test accuracy with no over/underfitting on a subset of supernovae from PLAsTiCC's unblinded test dataset. We have also achieved $98.18 \pm 0.3$% test accuracy performing 6-way classification of supernovae by type. The out-of-distribution performance does not fully match the in-distribution results, suggesting that the detailed characteristics of the training sample in comparison to the test sample have a big impact on the performance. We discuss the implication and directions for future work. All of the data processing and model code developed for this paper can be found in the SCONE software package located at github.com/helenqu/scone.

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arXiv2021

SkyMapper Optical Follow-up of Gravitational Wave Triggers: Alert Science Data Pipeline and LIGO/Virgo O3 Run

We present an overview of the SkyMapper optical follow-up program for gravitational-wave event triggers from the LIGO/Virgo observatories, which aims at identifying early GW170817-like kilonovae out to $\sim 200$ Mpc distance. We describe our robotic facility for rapid transient follow-up, which can target most of the sky at $δ<+10°$ to a depth of $i_\mathrm{AB}\approx 20$ mag. We have implemented a new software pipeline to receive LIGO/Virgo alerts, schedule observations and examine the incoming real-time data stream for transient candidates. We adopt a real-bogus classifier using ensemble-based machine learning techniques, attaining high completeness ($\sim$98%) and purity ($\sim$91%) over our whole magnitude range. Applying further filtering to remove common image artefacts and known sources of transients, such as asteroids and variable stars, reduces the number of candidates by a factor of more than 10. We demonstrate the system performance with data obtained for GW190425, a binary neutron star merger detected during the LIGO/Virgo O3 observing campaign. In time for the LIGO/Virgo O4 run, we will have deeper reference images allowing transient detection to $i_\mathrm{AB}\approx $21 mag.

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arXiv2020

Fink, a new generation of broker for the LSST community

Fink is a broker designed to enable science with large time-domain alert streams such as the one from the upcoming Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). It exhibits traditional astronomy broker features such as automatised ingestion, annotation, selection and redistribution of promising alerts for transient science. It is also designed to go beyond traditional broker features by providing real-time transient classification which is continuously improved by using state-of-the-art Deep Learning and Adaptive Learning techniques. These evolving added values will enable more accurate scientific output from LSST photometric data for diverse science cases while also leading to a higher incidence of new discoveries which shall accompany the evolution of the survey. In this paper we introduce Fink, its science motivation, architecture and current status including first science verification cases using the Zwicky Transient Facility alert stream.

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arXiv2020

Dark Energy Survey Identification of A Low-Mass Active Galactic Nucleus at Redshift 0.823 from Optical Variability

We report the identification of a low-mass AGN, DES J0218$-$0430, in a redshift $z = 0.823$ galaxy in the Dark Energy Survey (DES) Supernova field. We select DES J0218$-$0430 as an AGN candidate by characterizing its long-term optical variability alone based on DES optical broad-band light curves spanning over 6 years. An archival optical spectrum from the fourth phase of the Sloan Digital Sky Survey shows both broad Mg II and broad H$β$ lines, confirming its nature as a broad-line AGN. Archival XMM-Newton X-ray observations suggest an intrinsic hard X-ray luminosity of $L_{\rm 2-12\,keV}\sim7.6\pm0.4\times10^{43}$ erg s$^{-1}$, which exceeds those of the most X-ray luminous starburst galaxies, in support of an AGN driving the optical variability. Based on the broad H$β$ from SDSS spectrum, we estimate a virial BH mass of $M_{\bullet}\approx10^{6.43}$-$10^{6.72}M_{\odot}$ (with the error denoting 1$σ$ statistical uncertainties only), consistent with the estimation from OzDES, making it the lowest mass AGN with redshift $>$ 0.4 detected in optical. We estimate the host galaxy stellar mass to be $M_{\ast}\sim10^{10.5\pm0.3}M_{\odot}$ based on modeling the multi-wavelength spectral energy distribution. DES J0218$-$0430 extends the $M_{\bullet}$-$M_{\ast}$ relation observed in luminous AGNs at $z\sim1$ to masses lower than being probed by previous work. Our work demonstrates the feasibility of using optical variability to identify low-mass AGNs at higher redshift in deeper synoptic surveys with direct implications for the upcoming Legacy Survey of Space and Time at Vera C. Rubin Observatory.

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arXiv2020

Probing the extragalactic fast transient sky at minute timescales with DECam

Searches for optical transients are usually performed with a cadence of days to weeks, optimised for supernova discovery. The optical fast transient sky is still largely unexplored, with only a few surveys to date having placed meaningful constraints on the detection of extragalactic transients evolving at sub-hour timescales. Here, we present the results of deep searches for dim, minute-timescale extragalactic fast transients using the Dark Energy Camera, a core facility of our all-wavelength and all-messenger Deeper, Wider, Faster programme. We used continuous 20s exposures to systematically probe timescales down to 1.17 minutes at magnitude limits $g > 23$ (AB), detecting hundreds of transient and variable sources. Nine candidates passed our strict criteria on duration and non-stellarity, all of which could be classified as flare stars based on deep multi-band imaging. Searches for fast radio burst and gamma-ray counterparts during simultaneous multi-facility observations yielded no counterparts to the optical transients. Also, no long-term variability was detected with pre-imaging and follow-up observations using the SkyMapper optical telescope. We place upper limits for minute-timescale fast optical transient rates for a range of depths and timescales. Finally, we demonstrate that optical $g$-band light curve behaviour alone cannot discriminate between confirmed extragalactic fast transients such as prompt GRB flashes and Galactic stellar flares.

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arXiv2019

SuperNNova: an open-source framework for Bayesian, Neural Network based supernova classification

We introduce SuperNNova, an open source supernova photometric classification framework which leverages recent advances in deep neural networks. Our core algorithm is a recurrent neural network (RNN) that is trained to classify light-curves using photometric information only. Additional information such as host-galaxy redshift can be incorporated to improve performance. We evaluate our framework using realistic supernovae simulations that include survey detection. We show that our method, for the type Ia vs. non Ia supernovae classification problem, reaches accuracies greater than 96.92 +- 0.09 without any redshift information and up to 99.55 +- 0.06 when redshift, either photometric or spectroscopic, is available. Further, we show that our method attains unprecedented performance for classification of incomplete light-curves, reaching accuracies >86.4 +- 0.1 (>93.5 +- 0.8) without host-galaxy redshift (with redshift information) two days before maximum light. In contrast with previous methods, there is no need for time-consuming feature engineering and we show that our method scales to very large datasets with a modest computing budget. In addition, we investigate often neglected pitfalls of machine learning algorithms. We show that commonly used algorithms suffer from poor calibration and overconfidence on out-of-distribution samples when applied to supernovae data. We devise extensive tests to estimate the robustness of classifiers and cast the learning procedure under a Bayesian light, demonstrating a much better handling of uncertainties. We study the benefits of Bayesian RNNs for SN Ia cosmology. Our code is open-sourced and available on https://github.com/supernnova/SuperNNova.

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arXiv2018

Dependence of Type Ia supernova luminosities on their local environment

We present a fully consistent catalog of local and global properties of host galaxies of 882 Type Ia supernovæ (SNIa) that were selected based on their light-curve properties, spanning the redshift range $0.01 < z < 1.\text{}$ This catalog corresponds to a preliminary version of the compilation sample and includes Supernova Legacy Survey (SNLS) 5-year data, Sloan Digital Sky Survey (SDSS), and low-redshift surveys. We measured low- and moderate-redshift host galaxy photometry in SNLS and SDSS images and used spectral energy distribution (SED) fitting techniques to derive host properties such as stellar mass and $U-V$ rest-frame colors; the latter are an indicator of the luminosity-weighted age of the stellar population in a galaxy. We also estimated the local observed fluxes at the supernova location within a proper distance radius of 3 kpc, and transposed them into local $U-V$ rest-frame colors. Selecting SNIa based on host photometry quality, we then performed cosmological fits using local color as a third standardization variable, for which we split the sample at the median value. We find a local color step as significant as the maximum mass step effect. Correcting for the maximum mass step correction, we still find a significant local color effect, which shows that additional information is provided by the close environment of SNIa. Departures from the initial choices were investigated, and we discuss the possible implications for cosmology. This will be of tremendous importance for the forthcoming SNIa surveys, and in particular for the Large Synoptic Survey Telescope (LSST), for which uncertainties on the dark energy equation of state will be comparable to the effects reported here.

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arXiv2017

Convolutional Neural Networks for Transient Candidate Vetting in Large-Scale Surveys

Current synoptic sky surveys monitor large areas of the sky to find variable and transient astronomical sources. As the number of detections per night at a single telescope easily exceeds several thousand, current detection pipelines make intensive use of machine learning algorithms to classify the detected objects and to filter out the most interesting candidates. A number of upcoming surveys will produce up to three orders of magnitude more data, which renders high-precision classification systems essential to reduce the manual and, hence, expensive vetting by human experts. We present an approach based on convolutional neural networks to discriminate between true astrophysical sources and artefacts in reference-subtracted optical images. We show that relatively simple networks are already competitive with state-of-the-art systems and that their quality can further be improved via slightly deeper networks and additional preprocessing steps -- eventually yielding models outperforming state-of-the-art systems. In particular, our best model correctly classifies about 97.3% of all 'real' and 99.7% of all 'bogus' instances on a test set containing 1,942 'bogus' and 227 'real' instances in total. Furthermore, the networks considered in this work can also successfully classify these objects at hand without relying on difference images, which might pave the way for future detection pipelines not containing image subtraction steps at all.

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