arXiv subjects

Anais Möller

Publications and source records attributed to Anais Möller.

At least 19 recordsLinked to original sources

arXiv2026

Supernovae Unite: Combining Pantheon+ and DES-SN5YR

We present the Hubble diagram and cosmological constraints resulting from the combination of the Pantheon+ and DES-SN5YR supernova (SN) samples, which we refer to as Unite. Unite updates Pantheon+, including methodology improvements that the DES sample enabled, and adds the new SN from the DES survey. This represents the most comprehensive and internally consistent SN Ia dataset available, consisting of 2884 likely type Ia supernovae. This work features consistent SN Ia modelling, sample selection, and bias corrections. We also redetermine host-galaxy stellar masses for over 98% of the sample using a consistent framework. Using Unite alone, for a flat universe with a cosmological constant (Flat-$Λ$CDM), we find $Ω_{\rm m} = 0.310^{+0.012}_{-0.011}$. We further present constraints using the Unite sample combined with Cosmic Microwave Background and both DES and DESI Baryon Acoustic Oscillation data. We find strong evidence of tension between CMB measurements and the combined BAO and supernova datasets when fitting a model with constant dark energy equation of state ($w$), which is alleviated when allowing $w$ to vary with time. For a flat universe with dark energy that has a time-evolving equation of state parametrized by $w_0$ and $w_a$ we find $(Ω_{\rm m},w_0,w_a) = (0.305\pm0.004,-0.861^{+0.044}_{-0.042},-0.60^{+0.17}_{-0.19})$ with a dark energy Figure of Merit of 315. This corresponds to a $\sim30\%$ reduction in the area of the $w_0-w_a$ confidence region relative to previous constraints from the combination of DES Y6, DESI-DR2, and CMB data (arXiv:2605.27221). The Bayesian evidence indicates only weak preference for time evolving dark energy, whereas frequentist analyses yield a preference over Flat-$Λ$CDM at a significance of $3.3σ$ ($3.1σ$) when using the maximum $\textit{a posteriori}$ probability (maximum likelihood).

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arXiv2026

LStein: A new approach to visualizing sparse 2.5-dimensional data

Visualization of high-dimensional data is crucial to retrieve all the knowledge that is contained within a dataset. Effective and informative presentation of three-dimensional data via a two-dimensional medium is challenging, especially if the dataset more closely resembles a 2.5-dimensional (2.5D) entity due to sparse sampling. We present LStein (Linking Series to envision information neatly), a novel visualization approach implemented in Python, in an attempt to solve this challenge. Inspired by the astrophysical application of displaying photometric timeseries in multiple passbands with minimal loss of information, we compare our method to traditional approaches. While astronomy -- specifically multi-passband visualization for lightcurves obtained with the Rubin Observatory -- serves as the principal driver for the design, we demonstrate that LStein can be used in any context with 2.5D datasets from radio astronomy to machine learning hyperparameter search visualization. LStein provides a complementary visualization to traditional techniques. LStein can be installed from GitHub (https://github.com/TheRedElement/LStein).

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arXiv2026

Decadal pre-explosion activity and circumstellar interaction in a supernova

When a massive star explodes as a supernova, crucial information about its immediate environment is lost within hours. Here we report rapid optical observations from Lulin Observatory of the broad-lined Type Ic supernova SN 2026gzf, beginning 1.25 hours after Einstein Probe detected the X-ray transient EP260321a. Our data led to the discovery of the optical counterpart and showed a luminous blue first-day excess that cannot be reproduced by standard radioactive models. We find that interaction between the ejecta and $\approx 0.02$ M$_{\odot}$ of circumstellar material accounts for the early excess. Archival Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) images show variability at the explosion site over the previous $\sim 12$ years, with the source brightening by a factor of $\sim 1.5$ in the final $\sim 3$ years before explosion, providing rare evidence for pre-explosion activity in a stripped-envelope progenitor system. The precursor brightening suggests enhanced eruptive mass loss during late-stage oxygen burning before core collapse, while an additional silicon-burning episode shortly before explosion may have created the compact nearby material responsible for the X-ray shock-breakout signal. SN 2026gzf therefore offers the first view of how a stripped progenitor modifies its immediate environment shortly before death, linking long-term precursor variability, circumstellar interaction and the explosion itself.

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arXiv2026

Fast and Flexible Characterisation of Astronomical Light Curves Using Multi-Time Attention

We present an unsupervised, data-driven framework for rapid characterisation of astronomical photometric time series using a Multi-Time Attention Network. The model learns time-aware latent representations directly from irregular, partial light curves without heavy preprocessing. Through application on ZTF alert data retrieved with Fink, a community alert broker for Rubin LSST, we demonstrate that the model: (i) produces accurate interpolations with small bias (0.01 mag) and scatter (0.1 mag) even for sparse light curves, (ii) learns a temporally distributed latent space correlating with physically meaningful properties (duration, peak time, variability, color) while being robust to unimportant properties such as observed magnitude and number of observations, (iii) separates general SN and AGN samples despite data being heavily dominated by AGNs, and (iv) generalises to unseen classes: The long-period variable and TDE show good interpolation and sensible latent space placement; however, the model cannot capture RRLyrae's $\sim$0.4-0.5 day pulsation period, which is far below our model's chosen two-day temporal resolution. Attention map analysis reveals the capability of multi-time attention to capture local structure. The model is extremely lightweight (a few hundred kilobytes) and has fast inference ($\sim$0.01 and $\sim$$3\times10^{-4}$ s per light curve on CPU and GPU, respectively) that is independent of the number of observations, unlike GP regression. Our approach offers flexible and scalable characterisation, with high relevance in the Rubin LSST era. We discuss future possibilities to incorporate observational uncertainties and symmetries for robustness and forecasting applications for real-time follow-up.

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arXiv2026

AMPM I. A Targeted Search for Asteroid Mass Primordial Black Hole Microlenses

Gravitational microlensing is a powerful technique for constraining the abundance of dark matter in asteroid mass to supermassive primordial black holes at masses of $-11 \lesssim \log M/\mathrm{M}_\odot \lesssim 5$. In this work, we introduce a new high-cadence stellar microlensing survey in the Large Magellanic Cloud, AMPM. The primary goal of AMPM is to place constraints in the asteroid-to-planetary-mass regime of primordial black hole dark matter. We present the five nights of survey data, the microlensing detection pipeline, and the microlensing efficiency of AMPM. We explore the impact of the stellar distribution in the Large Magellanic Cloud on the microlensing detection efficiency and conduct a detailed analysis of second-order microlensing effects and the impact on the primordial black hole dark matter constraints. Our findings indicate that these second-order effects shift the maximum sensitivity of AMPM toward the lunar-mass black hole regime at $10^{-8} - 10^{-6} \, M_{\odot}$. From the five nights of data, we detect a single microlensing candidate and find that AMPM can constrain at the 95\% C.L up to 30\% of the Galactic primordial black hole dark matter distribution.

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arXiv2026

AMPM II. A Lunar-Mass Primordial Black Hole Microlensing Candidate in the Milky Way Halo

Primordial Black Holes (PBH) are hypothesised to form during inflation and have long been considered a candidate for compact dark matter. Gravitational microlensing is known as a productive method for exoplanet discovery and characterisation, but also provides an experimental avenue to constrain the PBH abundance in the mass regime from $\sim 10^{-11}\ M_{\odot}$ to $\sim 10^5\ M_{\odot}$. We performed a high-cadence, optical microlensing survey with DECam over five nights towards the Large Magellanic Cloud, sensitive to microlensing timescales from minutes to days. Here, we report the discovery of an hour-long microlensing event. An optical depth probabilistic analysis indicates that the lensing object, which we refer to as Phoebe, is 5 orders of magnitude more likely to be part of the Milky Way's dark matter halo than part of the stellar content of the Milky Way and Large Magellanic Cloud. No matter the location of Phoebe, it is among the fastest and lowest mass microlensing signals ever detected, with an Einstein timescale of approximately 60 minutes. Using Bayesian modelling, we interpret Phoebe as a PBH with mass $0.032^{+0.227}_{-0.027} M_{\oplus}$, or approximately 3 lunar masses. Phoebe suggests a population of compact, lunar-mass objects associated with the dark matter distribution of the Milky Way, and potentially opens a new window to the physics of inflation.

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arXiv2026

ASKAP J005512.2-255834: A Luminous, Long-Lived Radio Transient at z = 0.1 -- an Orphan Afterglow or an off-nuclear TDE from an IMBH?

We report the discovery of a slowly evolving, extragalactic radio transient, ASKAP J005512.2--255834 (hereafter ASKAP J0055-2558), identified using the Australian SKA Pathfinder in a search for orphan afterglows associated with archival gravitational wave events. Although discovered in this context, there is no evidence that the transient is associated with any known gravitational wave event. Nonetheless, this source exhibits a 20-fold increase in flux density over $<250$ days, and it remains in a declining, detectable state more than 1000 days after the initial detection. Follow-up observations from 0.3 to 9 GHz reveal an evolving spectrum consistent with synchrotron emission. ASKAP J0055-2558 is spatially coincident with a low-mass, star-forming galaxy at redshift $z = 0.116$ ($d_{\rm L}$= 543 Mpc), placing its peak radio luminosity at $νL_ν\sim 10^{39}\,\rm erg\,s^{-1}$. Analysis of its radio light curve, inferred blastwave velocity, energetics, host galaxy properties and the absence of counterparts at other wavelengths suggest that ASKAP J0055-2558 is most consistent with either the late-time phase of an orphan long gamma-ray burst afterglow or a tidal disruption event involving an intermediate-mass black hole spatially offset from the galaxy nucleus. The radio discovery of either of these phenomena is extremely rare, with only a few or no confirmed examples to date.

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arXiv2026

Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration

The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will produce unprecedented volumes of heterogeneous astronomical data (images, catalogs, and alerts) that challenge traditional analysis pipelines. The LSST Dark Energy Science Collaboration (DESC) aims to derive robust constraints on dark energy and dark matter from these data, requiring methods that are statistically powerful, scalable, and operationally reliable. Artificial intelligence and machine learning (AI/ML) are already embedded across DESC science workflows, from photometric redshifts and transient classification to weak lensing inference and cosmological simulations. Yet their utility for precision cosmology hinges on trustworthy uncertainty quantification, robustness to covariate shift and model misspecification, and reproducible integration within scientific pipelines. This white paper surveys the current landscape of AI/ML across DESC's primary cosmological probes and cross-cutting analyses, revealing that the same core methodologies and fundamental challenges recur across disparate science cases. Since progress on these cross-cutting challenges would benefit multiple probes simultaneously, we identify key methodological research priorities, including Bayesian inference at scale, physics-informed methods, validation frameworks, and active learning for discovery. With an eye on emerging techniques, we also explore the potential of the latest foundation model methodologies and LLM-driven agentic AI systems to reshape DESC workflows, provided their deployment is coupled with rigorous evaluation and governance. Finally, we discuss critical software, computing, data infrastructure, and human capital requirements for the successful deployment of these new methodologies, and consider associated risks and opportunities for broader coordination with external actors.

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arXiv2026

The Deeper, Wider, Faster programme's first DECam optical data release

The transient and variable optical sky is relatively poorly characterised on fast ($<$1$\,$hr) timescales. With the Dark Energy Camera (DECam), the Deeper, Wider, Faster programme (DWF) probes a unique parameter space with its deep (median of $g\sim22.2$ AB mag), minute-cadence imaging. In this work, we present DWF's first data release which comprises high cadence photometry extracted from $\sim$12000 images and 166 hours of telescope time. We present a novel data processing pipeline, $\texttt{dwf-postpipe}$, developed to identify sources and extract their light curves. The accuracy of the photometry is assessed by cross-matching to public catalogues. In addition, we injected a population of synthetic GRB afterglows into a subset of the DWF DECam imaging to compare the efficiency of our pipeline with a standard difference imaging approach. Both pipelines show performance and reliably recover injected transients with peak magnitudes $g<22$ AB mag with an efficiency of $97.24^{+0.7}_{-1.0}$ percent for \texttt{dwf-postpipe} and $96.14^{+0.9}_{-1.1}$ percent for a difference imaging approach. However, we find that $\texttt{dwf-postpipe}$ is less likely to recover transients appearing in galaxies that are brighter or comparable in brightness to the transient itself. To demonstrate the power of the data in this release, we conduct a search for uncatalogued variable stars in a single night of DWF DECam imaging and find ten pulsating variables, two eclipsing binaries and one ZZ ceti. We also conduct a search for variable phenomena in the Chandra Deep Field South, a Rubin deep drilling field, and identify two flares from likely UV ceti type stars.

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arXiv2025

Strategy for identifying Vera C. Rubin Observatory kilonova candidates for targeted gravitational-wave searches

Since the observation of the binary neutron star merger GW170817 and the associated kilonova AT2017gfo, the next joint gravitational-wave/optical kilonova has been highly anticipated. Overlapping observations between the Vera C. Rubin Observatory and the international gravitational-wave detector network are expected soon. Wide-field survey facilities, such as Rubin, can serve dual roles in gravitational-wave astronomy: conducting dedicated optical counterpart searches following gravitational-wave triggers and, through surveys such as the Legacy Survey of Space and Time (LSST), providing opportunities for fortuitous kilonova discoveries during routine operations. We use simulations to develop a strategy for identifying kilonova candidates observed by Rubin and processed by the Fink broker. These candidates can be used as astrophysical triggers for a targeted gravitational-wave search. We simulate kilonovae light-curves for the first year of Rubin with the latest observing strategy for the Wide-Fast-Deep and the Deep Drilling Fields. Assuming a kilonova rate of 250 Gpc$^{-3}$ yr$^{-1}$, we find that Rubin brokers should observe $\sim 4$ kilonovae per year with at least one alert above a signal-to-noise ratio of 5 within the gravitational-wave detector horizon ($\sim 350$ Mpc). Most of these will be faint, and detected 1-2 days following the neutron star merger. Photometric and spectroscopic follow-up will be limited to large telescopes. Using archival data from the Zwicky Transient Facility (ZTF) and our proposed selection criteria, we estimate a minimum contamination of at least 30 events per month from other transients and variables, even under our strictest selection criteria. A deep gravitational-wave search targeting Rubin kilonova candidates may lead to the next multi-messenger discovery.

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arXiv2025

An extremely fast fading population II dwarf nova candidate: caught spectroscopically on the rise

We present AT2022kak, a rapidly evolving optical transient discovered by the KiloNova and Transients Program (KNTraP). This interesting burst exhibited extremely fast evolution, with a large amplitude blue outburst of m > 3.3 in a single night, and a rapid fade back to quiescence in the following two nights. We deployed a multi-wavelength follow-up campaign, monitoring the object for the next two months, but saw no recurrent burst. Three years later, while observing to get spectroscopy of the object in quiescence, there was a new outburst, enabling the collection of time-resolved spectra of the rise and fade of the outburst. The light curve properties of the first burst and spectra of the second burst are consistent with a dwarf nova. Its fast evolving behaviour makes it one of the fastest and faintest dwarf novae observed. The estimated distance of AT2022kak from the Galactic centre is ~6.6 kpc, with a scale height of ~2 kpc. This scale height places it in the Galactic thick disk, where only very few dwarf novae have been found, and is therefore a potential Population II dwarf novae system.

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arXiv2025

A Machine Learning empowered search for Sub-Minute Optical Transient Events with the Deeper, Wider, Faster programme

Optical transient surveys continue to generate increasingly large datasets, prompting the introduction of machine-learning algorithms to search for quality transient candidates efficiently. Existing machine-learning infrastructure can be leveraged in novel ways to search these datasets for new classes of transients. We present a machine-learning accelerated search pipeline for the Deeper, Wider, Faster (DWF) programme designed to identify high-quality astrophysical transient candidates that contain a single detection. Given the rapid observing cadence of the DWF programme, these single-detection transient candidates have durations on sub-minute timescales. This work marks the first time optical transients have been systematically explored on these timescales, to a depth of m$\sim$23. We report the discovery of two high-quality sub-minute transient candidates from a pilot study of 671,761 light curves and investigate their potential origins with multiwavelength data. We discuss, in detail, possible non-astrophysical false positives, confidently reject electronic artefacts and asteroids, ruling out glints from satellites below 800 km and strongly disfavouring those at higher altitudes. We calculate a rate on the sky of $4.72^{+6.39}_{-3.28}\times10^5$ per day for these sub-minute transient candidates.

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arXiv2025

Testing and Combining Transient Spectral Classification Tools on 4MOST-like Blended Spectra

With the 4-meter Multi-Object Spectroscopic Telescope (4MOST) expected to provide an influx of transient spectra when it begins observations in early 2026 we consider the potential for real-time classification of these spectra. We investigate three extant spectroscopic transient classifiers: the Deep Automated Supernova and Host classifier (DASH), Next Generation SuperFit (NGSF) and SuperNova IDentification (SNID), with a focus on comparing the completeness and purity of the transient samples they produce. We manually simulate fibre losses critical for accurately determining host-contamination and use the 4MOST Exposure Time Calculator to produce realistic, 4MOST-like, host-galaxy contaminated spectra. We investigate the three classifiers individually and in all possible combinations. We find that a combination of DASH and NGSF can produce a SN Ia sample with a purity of 99.9% while successfully classifying 70% of SNe Ia. However, it struggles to classify non-SN Ia transients. We investigate photometric cuts to transient magnitude and the transient's fraction of total fibre flux, finding that both can be used to improve non-SN Ia transient classification completeness by 8--44% with SNe Ibc benefitting the most and superluminous (SL) SNe the least. Finally, we present an example classification plan for live classification and the predicted purities and completeness across five transient classes: Ia, Ibc, II, SL and non-SN transients. We find that it is possible to classify 75% of input spectra with >70% purity in all classes except non-SN transients. Precise values can be varied using different classifiers and photometric cuts to suit the needs of a given study.

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arXiv2025

Early Identification of Optical Tidal Disruption Events: A science module for the Fink broker

The detection of tidal disruption events (TDEs) is one of the key science goals of large optical time-domain surveys such as the Zwicky Transient Facility (ZTF) and the upcoming Vera C. Rubin Observatory Legacy Survey of Space and Time. However, identifying TDEs in the vast alert streams produced by these surveys requires automated and reliable classification pipelines that can select promising candidates in real time. We developed a module within the Fink alert broker to identify TDEs during their rising phase. It was built to autonomously operate within the ZTF alert stream, producing a list of candidates every night and enabling spectral and multi-wavelength follow-up near peak brightness. All rising alerts are submitted to selection cuts and feature extraction using the Rainbow multi-band lightcurve fit. Best-fit values were used as input to train an XGBoost classifier with the goal of identifying TDEs. The training set was constructed using ZTF observations for objects with available classification in the Transient Name Server. Finally, candidates with high enough probability were visually inspected. The classifier achieves 76% recall, indicating strong performance in early-phase identification, despite the limited available information before peak. We show that, out of the known TDEs that pass the selection cuts, half of them are flagged as TDE before halfway in their rise, proving the feasibility of early classification. Additionally, new candidates were identified by applying the classifier on archival data, including a likely repeated TDE and some potential TDEs occurring in active galaxies. The module is implemented into the Fink alert processing framework, reporting each night a small number of candidates to dedicated communication channels through a user-friendly interface, for manual vetting and potential follow-up.

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arXiv2025

GRB$\,$220831A: a hostless, intermediate Gamma-ray burst with an unusual optical afterglow

GRB$\,$220831A is a gamma-ray burst (GRB) with a duration and spectral peak energy that places it at the interface between the distribution of long-soft and short-hard GRBs. In this paper, we present the multi-wavelength follow-up campaign to GRB$\,$220831A and its optical, near-infrared, X-ray and radio counterparts. Our deep optical and near-infrared observations do not reveal an underlying host galaxy, and establish that GRB$\,$220831A is observationally hostless to depth, $m_i\gtrsim26.6$ AB mag. Based on the Amati relation and the non-detection of an accompanying supernova, we find that this GRB is most likely to have originated from a collapsar at $z>2$, but it could also possibly be a compact object merger at $z<0.4$ with a large separation distance from its host galaxy. Regardless of its origin, we show that its optical and near-infrared counterpart departs from the evolution expected from a forward shock dominated synchrotron afterglow, exhibiting a steep post-break temporal powerlaw index of $-3.83^{+0.62}_{-0.79}$, too steep to be the jet-break. By analysing a range of models, we find that the observed steep departure from forward shock closure relations is likely due to an internal process producing either a flare or a plateau.

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arXiv2024

An Optically Led Search for Kilonovae to z$\sim$0.3 with the Kilonova and Transients Program (KNTraP)

Compact binary mergers detectable in gravitational waves can be accompanied by a kilonova, an electromagnetic transient powered by radioactive decay of newly synthesised r-process elements. A few kilonova candidates have been observed during short gamma-ray burst follow-up, and one found associated with a gravitational wave detection, GW170817. However, robust kilonova candidates are yet to be found in un-triggered, wide-field optical surveys, that is, a search not requiring an initial gravitational wave or gamma-ray burst trigger. Here we present the first observing run for the Kilonova and Transients Program (KNTraP) using the Dark Energy Camera. The first KNTraP run ran for 11 nights, covering 31 fields at a nightly cadence in two filters. The program can detect transients beyond the LIGO/Virgo/KAGRA horizon, be agnostic to the merger orientation, avoid the Sun and/or Galactic plane, and produces high cadence multi-wavelength light curves. The data were processed nightly in real-time for rapid identification of transient candidates, allowing for follow-up of interesting candidates before they faded away. Three fast-rising candidates were identified in real-time, however none had the characteristics of the kilonova AT2017gfo associated with GW170817 or with the expected evolution for kilonovae from our fade-rate models. After the run, the data were reprocessed, then subjected to stringent filtering and model fitting to search for kilonovae offline. Multiple KNTraP runs (3+) are expected to detect kilonovae via this optical-only search method. No kilonovae were detected in this first KNTraP run using our selection criteria, constraining the KN rate to $R < 1.8\times10^{5}$ Gpc$^{-3}$ yr$^{-1}$.

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arXiv2024

A Fast-cadenced Search for Gamma-Ray Burst Orphan Afterglows with the Deeper, Wider, Faster Programme

The relativistic outflows that produce Long GRBs (LGRBs) can be described by a structured jet model where prompt $γ$-ray emission is restricted to a narrow region in the jet's core. Viewing the jet off-axis from the core, a population of afterglows without an associated GRB detection can be predicted. In this work, we conduct an archival search for these `orphan' afterglows (OAs) with minute-cadence, deep ($g\sim23$) data from the Dark Energy Camera (DECam) taken as part of the Deeper, Wider, Faster programme (DWF). We introduce a method to select fast-evolving OA candidates within DWF data that comprises a machine learning model, based on a realistic synthetic population of OAs. Using this classifier, we recover 51 OA candidates. Of these candidates, 42 are likely flare events from M-class stars. The remaining nine possess quiescent, coincident sources in archival data with angular profiles consistent with a star and are inconsistent with the expected population of LGRB host galaxies. We therefore conclude that these are likely Galactic events. We calculate an upper limit on the rate of OAs down to $g<22$ AB mag of 7.46\,deg$^{-2}$yr$^{-1}$ using our criteria and constrain possible jet structures. We also place an upper limit of the characteristic angle between the $γ$-ray emitting region and the jet's half opening angle. For a smooth power-law and a power-law with core jet model respectively, these values are $58.3^{\circ}$ and $56.6^{\circ}$, for a power-law index of 0.8 and $75.3^{\circ}$ and $76.8^{\circ}$ for a power-law index of 1.2.

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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.

astro-ph.CO↗