Rubin Observatory Legacy Survey Research Articles

Tomographic cross correlations between galaxy surveys and the CMB gravitational lensing potential. Effect of the redshift bin mismatch

Upcoming surveys of the large-scale structure of our Universe will employ a large coverage area of about half of the sky and will significantly increase the observational depth. With these surveys, we will be able to cross-correlate cosmic microwave background (CMB) gravitational lensing and galaxy surveys divided into narrow redshift bins to map the evolution of the cosmological parameters with redshift. We study the effect of the redshift bin mismatch of objects that is due to photometric redshift errors in tomographic cross-correlation measurements. We used the code FLASK to create Monte Carlo simulations of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) and Planck CMB lensing convergence. We simulated log-normal fields and divided galaxies into nine redshift bins with the Gaussian and modified Lorentzian photometric redshift errors. To estimate the parameters, we used angular power spectra of CMB lensing and galaxy density contrast fields and the maximum likelihood estimation method. We show that even with simple Gaussian errors with a standard deviation of $ the galaxy auto-power spectra in tomographic bins are offset by $2-15<!PCT!>$. The estimated cross-power spectra between galaxy clustering and CMB lensing are also biased, with smaller deviations $<5<!PCT!>$. As a result, the $ $ parameter deviates between $0.2-1.2\ due to the redshift bin mismatch of the objects. We propose a computationally fast and robust method based on the scattering matrix approach to correct for the redshift bin mismatch of the objects. The estimates of the parameters in tomographic studies such as the linear galaxy bias, the cross-correlation amplitude, and $ $ are biased due to the redshift bin mismatch of the objects. The biases in these parameters are alleviated with our scattering matrix approach.

B-modes from galaxy cluster alignments in future surveys

Intrinsic alignment (IA) of source galaxies represents an important contaminant for upcoming cosmic shear surveys. In particular, it is expected on general grounds that IA contains a B-mode while the weak lensing signal does not. Thus, a detection of B-modes offers the possibility to study directly the IA signal of the sources. Galaxy clusters exhibit strong IA and are therefore a natural candidate to look for a B-mode signal. We forecast the signal-to-noise ratio (SNR) for B-modes from IA of galaxy clusters in the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). We use a perturbative model for the IA multipoles based on the Effective Field Theory of Intrinsic Alignments (EFT of IA), which has recently been validated against N-body simulations. We forecast SNR ≈12 and find that this detectability is not significantly impacted by different analysis choices. Lastly, we also apply our forecast to clusters in the redMaPPer SDSS and DESY1 samples. We find SNR ≈5 and SNR ≈3, respectively, suggesting a detection is within reach, provided accurate redshift information is available.

Expected Impact of Glints from Space Debris in the LSST

We examine the simple model put forth in a recent note by Loeb regarding the brightness of space debris in the size range of 1–10 cm and their impact on the Rubin Observatory Legacy Survey of Space and Time (LSST) transient object searches. Their main conclusion was that “image contamination by untracked space debris might pose a bigger challenge [than large commercial satellite constellations in Low-Earth orbit].” Following corrections and improvements to this model, we calculate the apparent brightness of tumbling low-Earth orbit (LEO) debris of various sizes, and we briefly discuss the likely impact and potential mitigations of glints from space debris in LSST. We find the majority of the difference in predicted signal-to-noise ratio (S/N), about a factor of 6, arises from the defocus of LEO objects due to the large Simonyi Survey Telescope primary mirror and finite range of the debris. The largest change from the Loeb estimates is that 1–10 cm debris in LEO pose no threat to LSST transient object alert generation because their S/N for detection will be much lower than estimated by Loeb due to defocus. We find that only tumbling LEO debris larger than 10 cm or with significantly greater reflectivity, which give 1 ms glints, might be detected with high confidence (S/N > 5). We estimate that only one in five LSST exposures low on the sky during twilight might be affected. More slowly tumbling objects of larger size can give flares in brightness that are easily detected; however, these will not be cataloged by the LSST Science Pipelines because of the resulting long streak.

Reliable Identification of Binary Supermassive Black Holes from Rubin Observatory Time-domain Monitoring

Periodic signatures in time-domain observations of quasars have been used to search for binary supermassive black holes (SMBHs). These searches, across existing time-domain surveys, have produced several hundred candidates. The general stochastic variability of quasars, however, can masquerade as a false-positive periodic signal, especially when monitoring cadence and duration are limited. In this work, we predict the detectability of binary SMBHs in the upcoming Rubin Observatory Legacy Survey of Space and Time (LSST). We apply computationally inexpensive sinusoidal curve fits to millions of simulated LSST Deep Drilling Field light curves of both single, isolated quasars and binary quasars. The period and phase of simulated binary signals can generally be disentangled from quasar variability. Binary amplitude is overestimated and poorly recovered for two-thirds of potential binaries due to quasar accretion variability. Quasars with strong intrinsic variability can obscure a binary signal too much for recovery. We also find that the most luminous quasars mimic current binary candidate light curves and their properties: The false-positive rates are 60% for these quasars. The reliable recovery of binary period and phase for a wide range of input binary LSST light curves is promising for multi-messenger characterization of binary SMBHs. However, pure electromagnetic detections of binaries using photometric periodicity with amplitude greater than 0.1 mag will result in samples that are overwhelmed by false positives. This paper represents an important and computationally inexpensive way forward for understanding the true and false-positive rates for binary candidates identified by Rubin.

Slitless spectrophotometry with forward modelling: Principles and application to measuring atmospheric transmission

Context. In the next decade, many optical surveys will aim to answer the question of the nature of dark energy by measuring its equation-of-state parameter at the per mill level. This requires trusting the photometric calibration of the survey with a precision never reached so far on many sources of systematic uncertainties. The measurement of the on-site atmospheric transmission for each exposure, or for each season or for the full survey on average, can help reach the per mill precision for the magnitudes. Aims. This work aims at proving the ability to use slitless spectroscopy for standard-star spectrophotometry and its use to monitor on-site atmospheric transmission as needed, for example, by the Vera C. Rubin Observatory Legacy Survey of Space and Time supernova cosmology program. We fully deal with the case of a disperser in the filter wheel, which is the configuration chosen in the Rubin Auxiliary Telescope. Methods. The theoretical basis of slitless spectrophotometry is at the heart of our forward-model approach to extract spectroscopic information from slitless data. We developed a publicly available software called Spectractor, which implements each ingredient of the model and finally performs a fit of a spectrogram model directly on image data to obtain the spectrum. Results. We show through simulations that our model allows us to understand the structure of spectrophotometric exposures. We also demonstrate its use on real data by solving specific issues and illustrating that our procedure allows the improvement of the model describing the data. Finally, we discuss how this approach can be used to directly extract atmospheric transmission parameters from the data and thus provide the base for on-site atmosphere monitoring. We show the efficiency of the procedure in simulations and test it on the limited available data set.

Latent Stochastic Differential Equations for Modeling Quasar Variability and Inferring Black Hole Properties

Quasars are bright and unobscured active galactic nuclei (AGN) thought to be powered by the accretion of matter around supermassive black holes at the centers of galaxies. The temporal variability of a quasar’s brightness contains valuable information about its physical properties. The UV/optical variability is thought to be a stochastic process, often represented as a damped random walk described by a stochastic differential equation (SDE). Upcoming wide-field telescopes such as the Rubin Observatory Legacy Survey of Space and Time (LSST) are expected to observe tens of millions of AGN in multiple filters over a ten year period, so there is a need for efficient and automated modeling techniques that can handle the large volume of data. Latent SDEs are machine learning models well suited for modeling quasar variability, as they can explicitly capture the underlying stochastic dynamics. In this work, we adapt latent SDEs to jointly reconstruct multivariate quasar light curves and infer their physical properties such as the black hole mass, inclination angle, and temperature slope. Our model is trained on realistic simulations of LSST ten year quasar light curves, and we demonstrate its ability to reconstruct quasar light curves even in the presence of long seasonal gaps and irregular sampling across different bands, outperforming a multioutput Gaussian process regression baseline. Our method has the potential to provide a deeper understanding of the physical properties of quasars and is applicable to a wide range of other multivariate time series with missing data and irregular sampling.

Recovered supernova Ia rate from simulated LSST images

The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will revolutionize time-domain astronomy by detecting millions of different transients. In particular, it is expected to increase the number of known type Ia supernovae (SN Ia) by a factor of 100 compared to existing samples up to redshift $ 1.2$. Such a high number of events will dramatically reduce statistical uncertainties in the analysis of the properties and rates of these objects. However, the impact of all other sources of uncertainty on the measurement of the SN Ia rate must still be evaluated. The comprehension and reduction of such uncertainties will be fundamental both for cosmology and stellar evolution studies, as measuring the SN Ia rate can put constraints on the evolutionary scenarios of different SN Ia progenitors. We used simulated data from the Dark Energy Science Collaboration (DESC) Data Challenge 2 (DC2) and LSST Data Preview 0 to measure the SN Ia rate on a 15 $deg^2$ region of the "wide-fast-deep" area. We selected a sample of SN candidates detected in difference images, associated them to the host galaxy with a specially developed algorithm, and retrieved their photometric redshifts. We then tested different light-curve classification methods, with and without redshift priors (albeit ignoring contamination from other transients, as DC2 contains only SN Ia). We discuss how the distribution in redshift measured for the SN candidates changes according to the selected host galaxy and redshift estimate. We measured the SN Ia rate, analyzing the impact of uncertainties due to photometric redshift, host-galaxy association and classification on the distribution in redshift of the starting sample. We find that we are missing 17<!PCT!> of the SN Ia, on average, with respect to the simulated sample. As 10<!PCT!> of the mismatch is due to the uncertainty on the photometric redshift alone (which also affects classification when used as a prior), we conclude that this parameter is the major source of uncertainty. We discuss possible reduction of the errors in the measurement of the SN Ia rate, including synergies with other surveys, which may help us to use the rate to discriminate different progenitor models.

Primordial non-Gaussianities with weak lensing: information on non-linear scales in the Ulagam full-sky simulations

Primordial non-Gaussianities (PNGs) are signatures in the density field that encode particle physics processes from the inflationary epoch. Such signatures have been extensively studied using the Cosmic Microwave Background, through constraining their amplitudes, fX NL, with future improvements expected from large-scale structure surveys; specifically, the galaxy correlation functions. We show that weak lensing fields can be used to achieve competitive and complementary constraints. This is shown via the Ulagam suite of N-body simulations, a subset of which evolves primordial fields with four types of PNGs. We create full-sky lensing maps and estimate the Fisher information from three summary statistics measured on the maps: the moments, the cumulative distribution function, and the 3-point correlation function. We find that the year 10 sample from the Rubin Observatory Legacy Survey of Space and Time (LSST) can constrain PNGs to σ(f NL eq) ≈ 110, σ(f NL or, lss) ≈ 120, σ(f NL loc) ≈ 40. For the former two, this is better than or comparable to expected galaxy clustering-based constraints from the Dark Energy Spectroscopic Instrument (DESI). The PNG information in lensing fields is on non-linear scales and at low redshifts (z ≲ 1.25), with a clear origin in the evolution history of massive halos. The constraining power degrades by ∼60% under scale cuts of ≳ 20 Mpc, showing there is still significant information on scales mostly insensitive to small-scale systematic effects (e.g., baryons). We publicly release the Ulagam suite to enable more survey-focused analyses.

Galaxy bias in the era of LSST: perturbative bias expansions

Upcoming imaging surveys will allow for high signal-to-noise measurements of galaxy clustering at small scales. In this work, we present the results of the Rubin Observatory Legacy Survey of Space and Time (LSST) bias challenge, the goal of which is to compare the performance of different nonlinear galaxy bias models in the context of LSST Year 10 (Y10) data. Specifically, we compare two perturbative approaches, Lagrangian perturbation theory (LPT) and Eulerian perturbation theory (EPT) to two variants of Hybrid Effective Field Theory (HEFT), with our fiducial implementation of these models including terms up to second order in the bias expansion as well as nonlocal bias and deviations from Poissonian stochasticity. We consider a variety of different simulated galaxy samples and test the performance of the bias models in a tomographic joint analysis of LSST-Y10-like galaxy clustering, galaxy-galaxy-lensing and cosmic shear. We find both HEFT methods as well as LPT and EPT combined with non-perturbative predictions for the matter power spectrum to yield unbiased constraints on cosmological parameters up to at least a maximal scale of k max = 0.4 Mpc-1 for all samples considered, even in the presence of assembly bias. While we find that we can reduce the complexity of the bias model for HEFT without compromising fit accuracy, this is not generally the case for the perturbative models. We find significant detections of non-Poissonian stochasticity in all cases considered, and our analysis shows evidence that small-scale galaxy clustering predominantly improves constraints on galaxy bias rather than cosmological parameters. These results therefore suggest that the systematic uncertainties associated with current nonlinear bias models are likely to be subdominant compared to other sources of error for tomographic analyses of upcoming photometric surveys, which bodes well for future galaxy clustering analyses using these high signal-to-noise data.

Shedding light on low-surface-brightness galaxies in dark energy surveys with transformer models

Context. Low-surface-brightness galaxies (LSBGs), which are defined as galaxies that are fainter than the night sky, play a crucial role in our understanding of galaxy evolution and in cosmological models. Upcoming large-scale surveys, such as Rubin Observatory Legacy Survey of Space and Time and Euclid, are expected to observe billions of astronomical objects. In this context, using semiautomatic methods to identify LSBGs would be a highly challenging and time-consuming process, and automated or machine learning-based methods are needed to overcome this challenge. Aims. We study the use of transformer models in separating LSBGs from artefacts in the data from the Dark Energy Survey (DES) Data Release 1. Using the transformer models, we then search for new LSBGs from the DES that the previous searches may have missed. Properties of the newly found LSBGs are investigated, along with an analysis of the properties of the total LSBG sample in DES. Methods. We created eight different transformer models and used an ensemble of these eight models to identify LSBGs. This was followed by a single-component Sérsic model fit and a final visual inspection to filter out false positives. Results. Transformer models achieved an accuracy of ~94% in separating the LSBGs from artefacts. In addition, we identified 4083 new LSBGs in DES, adding an additional ~17% to the LSBGs already known in DES. This also increased the number density of LSBGs in DES to 5.5 deg−2. The new LSBG sample consists of mainly blue and compact galaxies. We performed a clustering analysis of the LSBGs in DES using an angular two-point auto-correlation function and found that LSBGs cluster more strongly than their high-surface-brightness counterparts. This effect is driven by the red LSBG. We associated 1310 LSBGs with galaxy clusters and identified 317 ultradiffuse galaxies among them. We found that these cluster LSBGs are getting bluer and larger in size towards the edge of the clusters when compared with those in the centre. Conclusions. Transformer models have the potential to be equivalent to convolutional neural networks as state-of-the-art algorithms in analysing astronomical data. The significant number of LSBGs identified from the same dataset using a different algorithm highlights the substantial impact of our methodology on our capacity to discover LSBGs. The reported number density of LSBGs is only a lower estimate and can be expected to increase with the advent of surveys with better image quality and more advanced methodologies.

Prospects for Connections to the Outer Halo of the Milky Way with LSST

Over its ten-year mission, Rubin Observatory's Legacy Survey of Space and Time will gather enough time series data to identify variable stars much fainter than previously observed. This makes ideal for finding RR Lyrae stars and mapping previously unexplored regions of our Galaxy's stellar halo. This project creates several mock data sets from both simulations and known satellite properties to model what might be found. We use these to examine the prospects for making connections between stellar structures already known around the Galaxy (satellites, streams and shells) and newly discovered stars. Such connections could be used to place constraints on the mass distribution in the outer regions of the Galaxy’s dark matter halo. Our results are encouraging, suggesting: that tens of percent of the known structures should have counterparts in the outer halo; and that several entirely new unbound structures could be discovered.

Intrinsic alignment from multiple shear estimates: a first application to data and forecasts for stage IV

ABSTRACT Without mitigation, the intrinsic alignment (IA) of galaxies poses a significant threat to achieving unbiased cosmological parameter constraints from precision weak lensing surveys. Here, we apply for the first time to data a method to extract the scale dependence of the IA contribution to galaxy–galaxy lensing, which takes advantage of the difference in alignment signal as measured by shear estimators with different sensitivities to galactic radii. Using data from Year 1 of the Dark Energy Survey, with shear estimators METACALIBRATION and IM3SHAPE, we investigate and address method systematics including non-trivial selection functions, differences in weighting between estimators, and multiplicative bias. We obtain a null detection of IA, which appears qualitatively consistent with existing work. We then forecast the application of this method to Rubin Observatory Legacy Survey of Space and Time (LSST) data and place requirements on a pair of shear estimators for detecting IA and constraining its 1-halo scale dependence. We find that for LSST Year 1, shear estimators should have at least a 40 per cent difference in IA amplitude, and the Pearson correlation coefficient of their shape noise should be at least ρ = 0.50, to ensure a 1σ detection of IA and a constraint on its 1-halo scale dependence with a signal-to-noise ratio greater than 1. For Year 10, a 1σ detection and constraint become possible for 20 per cent differences in alignment amplitude and ρ = 0.50.

Impact of blending on weak lensing measurements with the Vera C. Rubin Observatory

Upcoming deep optical surveys such as the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will scan the sky to unprecedented depths and detect billions of galaxies. This amount of detections will however cause the apparent superposition of galaxies on the images, called blending, and generate a new systematic error due to the confusion of sources. As consequences, the measurements of individual galaxies properties such as their redshifts or shapes will be impacted, and some galaxies will not be detected. However, galaxy shapes are key quantities, used to estimate masses of large scale structures, such as galaxy clusters, through weak gravitational lensing. This work presents a new catalog matching algorithm, called friendly, for the detection and characterization of blends in simulated LSST data for the Dark Energy Science Collaboration (DESC) Data Challenge 2. By identifying a specific type of blends, we show that removing them from the data may partially correct the amplitude of the excess surface mass density ΔΣ weak lensing profile that could be biased low by around 20% due to blending. This would result in impacting clusters weak lensing mass estimate and cosmology.

Are There Terrestrial Planets Lurking in the Outer Solar System?

Motivated by recent measurements of the free-floating-planet mass function at terrestrial masses, we consider the possibility that the solar system may have captured a terrestrial planet early in its history. We show that ∼1.2 captured free-floating planets with mass strictly greater than that of Mars may exist in the outer solar system, with a median predicted distance of ∼1400 au. If we consider a logarithmic bin centered on the mass of Mars, rather than a cutoff, we find that ∼2.7 captured free-floating planets with mass comparable to Mars may exist in the outer solar system. We derive an expectation value of ∼0.9 for the number of captured free-floating planets with mass comparable to that of Mars (∼1.4 for mass comparable to that of Mercury) that are currently brighter than the 10 yr coadded point-source detection limits of the Vera C. Rubin Observatory's Legacy Survey of Space and Time. Blind shift-and-stack searches could potentially enable the detection of such a planet if it is currently in the Southern sky. The theoretical argument presented here does not rely on the existence of posited patterns in the orbital elements of small bodies in and beyond the Kuiper Belt, in contrast with other hypothetical outer-solar-system planets motivated in recent years.

Synthetic Detections of Interstellar Objects with the Rubin Observatory Legacy Survey of Space and Time

The discovery of two interstellar objects passing through the solar system, 1I/‘Oumuamua and 2I/Borisov, implies that a galactic population exists with a spatial number density of order ∼0.1 au−3. The forthcoming Rubin Observatory Legacy Survey of Space and Time (LSST) has been predicted to detect more asteroidal interstellar objects like 1I/‘Oumuamua. We apply recently developed methods to simulate a suite of galactic populations of interstellar objects with a range of assumed kinematics, albedos, and size–frequency distributions (SFDs). We incorporate these populations into the objectsInField algorithm, which simulates detections of moving objects by an arbitrary survey. We find that the LSST should detect between ∼0 and 70 asteroidal interstellar objects every year (assuming the implied number density), with sensitive dependence on the SFD slope and characteristic albedo of the host population. The apparent rate of motion on the sky—along with the associated trailing loss—appears to be the largest barrier to detecting interstellar objects. Specifically, a relatively large number of synthetic objects would be detectable by the LSST if not for their rapid sky motion (>0.°5 day−1). Therefore, algorithms that could successfully link and detect rapidly moving objects would significantly increase the number of interstellar object discoveries with the LSST (and in general). The mean diameter of detectable, inactive interstellar objects ranges from ∼50 to 600 m and depends sensitively on the SFD slope and albedo.

The Galactic Interstellar Object Population: A Framework for Prediction and Inference

The Milky Way is thought to host a huge population of interstellar objects (ISOs), numbering approximately 1015 pc−3 around the Sun, which are formed and shaped by a diverse set of processes ranging from planet formation to Galactic dynamics. We define a novel framework, first to predict the properties of this Galactic ISO population by combining models of processes across planetary and Galactic scales, and second to make inferences about the processes being modeled, by comparing the predicted population to what is observed. We predict the spatial and compositional distribution of the Galaxy’s population of ISOs by modeling the Galactic stellar population with data from the APOGEE survey and combining this with a protoplanetary disk chemistry model. Selecting the ISO water mass fraction as an example observable quantity, we evaluate its distribution both at the position of the Sun and averaged over the Galactic disk; our prediction for the solar neighborhood is compatible with the inferred water mass fraction of 2I/Borisov. We show that the well-studied Galactic stellar metallicity gradient has a corresponding ISO compositional gradient. We also demonstrate the inference part of the framework by using the current observed ISO composition distribution to constrain the parent star metallicity dependence of the ISO production rate. This constraint, and other inferences made with this framework, will improve dramatically as the Vera C. Rubin Observatory Legacy Survey of Space and Time progresses and more ISOs are observed. Finally, we explore generalizations of this framework to other Galactic populations, such as that of exoplanets.

Robustness of baryon acoustic oscillations measurements with photometric redshift uncertainties

ABSTRACT We investigate the robustness of baryon acoustic oscillations (BAO) measurements with a photometric galaxy sample using mock galaxy catalogues with various sizes of photometric redshift (photo-z) uncertainties. We first conduct the robustness of BAO measurements, assuming we have a perfect knowledge of photo-z uncertainties. We find that the BAO shift parameter α can be constrained in an unbiased manner even for 3 per cent photometric redshift uncertainties up to z ∼ 1. For instance, α = 1.006 ± 0.078 with 95 per cent confidence level is obtained from 3 per cent photo-z uncertainty data at z = 1.03 using the sample of M* ≥ 1010.25 M⊙ h−2. We also find that a sparse galaxy sample, e.g. <2 × 10−4 [h Mpc−1]3, causes additional noise in the covariance matrix calculation and can bias the constraint on α. Following this, we look into the scenario where incorrect photometric redshift uncertainties are assumed in the fitting model. We find that underestimating the photo-z uncertainty leads to a degradation in the constraining power on α. However, the constrained value of α is not biased. We also quantify the constraining power on Ωm0 assuming the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST)-like covariance and find that the 95 per cent confidence level is σ(Ωm0) ∼ 0.03–0.05 corresponding to the photo-z uncertainties of 1–3 per cent, respectively. Finally, we examine whether the skewness in the photometric redshift can bias the constraint on α and confirm that the constraint on α is unbiased, even assuming a Gaussian photo-z uncertainty in our model.

An Evenly Spaced LSST Cadence for Rapidly Variable Stars

Abstract Stars exhibit a bewildering variety of rapidly variable behaviors ranging from explosive magnetic flares to stochastically changing accretion to periodic pulsations or rotation. The principal Rubin Observatory Legacy Survey of Space and Time (LSST) surveys will have cadences too sparse and irregular to capture many of these phenomena. We propose here an LSST microsurvey to observe a single Galactic field, rich in unobscured stars, in a continuous sequence of 30 s exposures for one long winter night in a single photometric band. The result will be a unique data set of ∼1 million regularly spaced stellar light curves. The light curves will constitute a comprehensive collection of late-type stellar flaring, but also other classes like short-period binary systems and cataclysmic variables, young stellar objects, and ultrashort-period exoplanets. An unknown variety of anomalous solar system, Galactic, and extragalactic variables and transients may also be present. A powerful array of statistical procedures can be applied to individual light curves from the long-standing fields of time series analysis, signal processing, and econometrics. Dozens of “features” describing the variability can be extracted and the ensemble of light curves can be subject to advanced machine-learning clustering procedures. This will give a unique, authoritative, objective taxonomy of the rapidly variable sky derived from identically cadenced light curves. This microsurvey is best performed early in the Rubin Observatory program, and the results can inform the wider community on the best approaches to variable star identification and classification from the sparse, irregular cadences that dominate the planned surveys.

What’s the Difference? The Potential for Convolutional Neural Networks for Transient Detection without Template Subtraction

We present a study of the potential for convolutional neural networks (CNNs) to enable separation of astrophysical transients from image artifacts, a task known as “real–bogus” classification, without requiring a template-subtracted (or difference) image, which requires a computationally expensive process to generate, involving image matching on small spatial scales in large volumes of data. Using data from the Dark Energy Survey, we explore the use of CNNs to (1) automate the real–bogus classification and (2) reduce the computational costs of transient discovery. We compare the efficiency of two CNNs with similar architectures, one that uses “image triplets” (templates, search, and difference image) and one that takes as input the template and search only. We measure the decrease in efficiency associated with the loss of information in input, finding that the testing accuracy is reduced from ∼96% to ∼91.1%. We further investigate how the latter model learns the required information from the template and search by exploring the saliency maps. Our work (1) confirms that CNNs are excellent models for real–bogus classification that rely exclusively on the imaging data and require no feature engineering task and (2) demonstrates that high-accuracy (>90%) models can be built without the need to construct difference images, but some accuracy is lost. Because, once trained, neural networks can generate predictions at minimal computational costs, we argue that future implementations of this methodology could dramatically reduce the computational costs in the detection of transients in synoptic surveys like Rubin Observatory's Legacy Survey of Space and Time by bypassing the difference image analysis entirely.

VarIabiLity seLection of AstrophysIcal sources iN PTF (VILLAIN)

Context. Light-curve variability is well-suited to characterising objects in surveys with high cadence and a long baseline. This is especially relevant in view of the large datasets to be produced by the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). Aims. We aim to determine variability parameters for objects in the Palomar Transient Factory (PTF) and explore differences between quasars (QSOs), stars, and galaxies. We relate variability and colour information in preparation for future surveys. Methods. We fit joint likelihoods to structure functions (SFs) of 71 million PTF light curves with a Markov chain Monte Carlo method. For each object, we assume a power-law SF and extract two parameters: the amplitude on timescales of one year, A, and a power-law index, γ. With these parameters and colours in the optical (Pan-STARRS1) and mid-infrared (WISE), we identify regions of parameter space dominated by different types of spectroscopically confirmed objects from SDSS. Candidate QSOs, stars, and galaxies are selected to show their parameter distributions. Results. QSOs show high-amplitude variations in the R band, and the highest γ values. Galaxies have a broader range of amplitudes and their variability shows relatively little dependency on timescale. With variability and colours, we achieve a photometric selection purity of 99.3% for QSOs. Even though hard cuts in monochromatic variability alone are not as effective as seven-band magnitude cuts, variability is useful in characterising object subclasses. Through variability, we also find QSOs that were erroneously classified as stars in the SDSS. We discuss perspectives and computational solutions in view of the upcoming LSST.