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DOE OSTI2022

XXL-HSC: An updated catalogue of high-redshift ( z ≥ 3.5) X-ray AGN in the XMM-XXL northern field

X-rays offer a reliable method to identify active galactic nuclei (AGNs). However, in the high-redshift Universe, X-ray AGNs are poorly sampled due to their relatively low space density and the small areas covered by X-ray surveys. In addition to wide-area X-ray surveys, it is important to have deep optical data in order to locate the optical counterparts and determine their redshifts. Here, in this work, we built a high-redshift (z ≥ 3.5) X-ray-selected AGN sample in the XMM-XXL northern field using the most updated [0.5–2 keV] catalogue along with a plethora of new spectroscopic and multi-wavelength catalogues, including the deep optical Subaru Hyper Suprime-Cam (HSC) data, reaching magnitude limits i ~ 26 mag. We selected all the spectroscopically confirmed AGN and complement this sample with high-redshift candidates that are HSC g- and r-band dropouts. To confirm the dropouts, we derived their photometric redshifts using spectral energy distribution techniques. We obtained a sample of 54 high-z sources (28 with spec-z), the largest in this field so far (almost three times larger than in previous studies), and we estimated the possible contamination and completeness. We calculated the number counts (log N-log S) in different redshift bins and compared our results with previous studies and models. We provide the strongest high-redshift AGN constraints yet at bright fluxes (f 0.5 – 2 keV > 10 –15 erg s –1 cm –2 ). The samples of z ≥ 3.5, z ≥ 4, and z ≥ 5 are in agreement with an exponential decline model similar to that witnessed at optical wavelengths. Our work emphasises the importance of using wide-area X-ray surveys with deep optical data to uncover high-redshift AGNs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Modelling the Milky Way – I. Method and first results fitting the thick disc and halo with DES-Y3 data

ABSTRACT We present a technique to fit the stellar components of the Galaxy by comparing Hess Diagrams (HDs) generated from trilegal models to real data. We apply this technique, which we call mwfitting, to photometric data from the first 3 yr of the Dark Energy Survey (DES). After removing regions containing known resolved stellar systems such as globular clusters, dwarf galaxies, nearby galaxies, the Large Magellanic Cloud, and the Sagittarius Stream, our main sample spans a total area of ∼2300 deg2. We further explore a smaller subset (∼1300 deg2) that excludes all regions with known stellar streams and stellar overdensities. Validation tests on synthetic data possessing similar properties to the DES data show that the method is able to recover input parameters with a precision better than 3 per cent. We fit the DES data with an exponential thick disc model and an oblate double power-law halo model. We find that the best-fitting thick disc model has radial and vertical scale heights of 2.67 ± 0.09 kpc and 925 ± 40 pc, respectively. The stellar halo is fit with a broken power-law density profile with an oblateness of 0.75 ± 0.01, an inner index of 1.82 ± 0.08, an outer index of 4.14 ± 0.05, and a break at 18.52 ± 0.27 kpc from the Galactic centre. Several previously discovered stellar overdensities are recovered in the residual stellar density map, showing the reliability of mwfitting in determining the Galactic components. Simulations made with the best-fitting parameters are a promising way to predict Milky Way star counts for surveys such as the LSST and Euclid.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

The Dark Energy Camera Plane Survey 2 (DECaPS2): More Sky, Less Bias, and Better Uncertainties

Abstract Deep optical and near-infrared imaging of the entire Galactic plane is essential for understanding our Galaxy’s stars, gas, and dust. The second data release of the Dark Energy Camera (DECam) Plane Survey extends the five-band optical and near-infrared survey of the southern Galactic plane to cover 6.5% of the sky, ∣ b ∣ ≤ 10°, and 6° > ℓ > −124°, complementary to coverage by Pan-STARRS1. Typical single-exposure effective depths, including crowding effects and other complications, are 23.5, 22.6, 22.1, 21.6, and 20.8 mag in g , r , i , z , and Y bands, respectively, with around 1″ seeing. The survey comprises 3.32 billion objects built from 34 billion detections in 21,400 exposures, totaling 260 hr open shutter time on the DECam at Cerro Tololo. The data reduction pipeline features several improvements, including the addition of synthetic source injection tests to validate photometric solutions across the entire survey footprint. A convenient functional form for the detection bias in the faint limit was derived and leveraged to characterize the photometric pipeline performance. A new postprocessing technique was applied to every detection to debias and improve uncertainty estimates of the flux in the presence of structured backgrounds, specifically targeting nebulosity. The images and source catalogs are publicly available at http://decaps.skymaps.info/ .

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Dark Energy Survey Deep Field photometric redshift performance and training incompleteness assessment

Context. The determination of accurate photometric redshifts (photo-zs) in large imaging galaxy surveys is key for cosmological studies. One of the most common approaches are machine learning techniques. These methods require a spectroscopic or reference sample to train the algorithms. Attention has to be paid to the quality and properties of these samples since they are key factors in the estimation of reliable photo-zs. Aims. The goal of this work is to calculate the photo-zs for the Y3 DES Deep Fields catalogue using the DNF machine learning algorithm. Moreover, we want to develop techniques to assess the incompleteness of the training sample and metrics to study how incompleteness affects the quality of photometric redshifts. Finally, we are interested in comparing the performance obtained with respect to the EAzY template fitting approach on Y3 DES Deep Fields catalogue. Methods. We have emulated -- at brighter magnitude -- the training incompleteness with a spectroscopic sample whose redshifts are known to have a measurable view of the problem. We have used a principal component analysis to graphically assess incompleteness and to relate it with the performance parameters provided by DNF. Finally, we have applied the results about the incompleteness to the photo-z computation on Y3 DES Deep Fields with DNF and estimated its performance. Results. The photo-zs for the galaxies on DES Deep Fields have been computed with the DNF algorithm and added to the Y3 DES Deep Fields catalogue. They are available at https://des.ncsa.illinois.edu/releases/y3a2/Y3deepfields. Some techniques have been developed to evaluate the performance in the absence of "true" redshift and to assess completeness. We have studied... (Partial abstract)

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Baryon acoustic oscillations in the projected cross-correlation function between the eBOSS DR16 quasars and photometric galaxies from the DESI Legacy Imaging Surveys

ABSTRACT We search for the baryon acoustic oscillations in the projected cross-correlation function binned into transverse comoving radius between the SDSS-IV DR16 eBOSS quasars and a dense photometric sample of galaxies selected from the DESI Legacy Imaging Surveys. We estimate the density of the photometric sample of galaxies in this redshift range to be about 2900 deg−2, which is deeper than the official DESI emission line galaxy selection, and the density of the spectroscopic sample is about 20 deg−2. In order to mitigate the systematics related to the use of different imaging surveys close to the detection limit, we use a neural network approach that accounts for complex dependences between the imaging attributes and the observed galaxy density. We find that we are limited by the depth of the imaging surveys that affects the density and purity of the photometric sample and its overlap in redshift with the quasar sample, which thus affects the performance of the method. When cross-correlating the photometric galaxies with quasars in the range 0.6 ≤ z ≤ 1.2, the cross-correlation function can provide better constraints on the comoving angular distance DM (6 per cent precision) compared to the constraint on the spherically averaged distance DV (9 per cent precision) obtained from the autocorrelation. Although not yet competitive, this technique will benefit from the arrival of deeper photometric data from upcoming surveys that will enable it to go beyond the current limitations we have identified in this work.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

The Binary INformation from Open Clusters Using SEDs (BINOCS) Project: Reliable Photometric Mass Determinations of Binary Star Systems in Clusters

We introduce a new binary detection technique, Binary INformation from Open Clusters using SEDs (binocs), which we show is able to determine reliable stellar multiplicity and masses over a much larger mass range than current approaches. This new technique determines accurate component masses of binary and single systems of the open clusters’ main sequence by comparing observed magnitudes from multiple photometric filters to synthetic star spectral energy distributions (SEDs), allowing us to systematically probe the binary population for low-mass stars in clusters for eight well-studied open clusters. We provide new deep, infrared photometric catalogs (1.2–8.0 μm) for the key open clusters NGC 1960 (M36), NGC 2099 (M37), NGC 2420, and NGC 2682 (M67), using observations from NOAO/NEWFIRM and Spitzer/IRAC. Using these deep multiwavelength catalogs, the binocs method is applied to these clusters to determine accurate component masses for unresolved cluster binaries. We explore binary fractions as a function of cluster age, Galactic location, and metallicity.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

J-PLUS: Systematic impact of metallicity on photometric calibration with the stellar locus

Here, we present the photometric calibration of the twelve optical passbands for the Javalambre Photometric Local Universe Survey (J-PLUS) second data release (DR2), comprising 1088 pointings of two square degrees, and study the systematic impact of metallicity on the stellar locus technique. The [Fe/H] metallicity from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) for 146 184 high-quality calibration stars, defined with signal-to-noise ratio larger than ten in J-PLUS passbands and larger than three in Gaia parallax, was used to compute the metallicity-dependent stellar locus (ZSL). The initial homogenization of J-PLUS photometry, performed with a unique stellar locus, was refined by including the metallicity effect in colors via the ZSL. The variation of the average metallicity along the Milky Way produces a systematic offset in J-PLUS calibration. This effect is well above 1% for the bluer passbands and amounts 0.07, 0.07, 0.05, 0.03, and 0.02 mag in u, J0378, J0395, J0410, and J0430, respectively. We modeled this effect with the Milky Way location of the J-PLUS pointing, also providing an updated calibration for those observations without LAMOST information. The estimated accuracy in the calibration after including the metallicity effect is at 1% for the bluer J-PLUS passbands and below for the rest. Photometric calibration with the stellar locus technique is prone to significant systematic bias in the Milky Way for passbands bluer than λ = 4500 Å. The calibration method for J-PLUS DR2 reaches 1–2% precision and 1% accuracy for 12 optical filters within an area of 2176 square degrees.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

A declining major merger fraction with redshift in the local Universe from the largest-yet catalogue of major and minor mergers in SDSS

It is difficult to accurately identify galaxy mergers and it is an even larger challenge to classify them by their mass ratio or merger stage. In previous work we used a suite of simulated mergers to create a classification technique that uses linear discriminant analysis to identify major and minor mergers. Here, we apply this technique to 1.3 million galaxies from the SDSS DR16 photometric catalogue and present the probability that each galaxy is a major or minor merger, splitting the classifications by merger stages (early, late, post-coalescence). We present publicly available imaging predictor values and all of the above classifications for one of the largest-yet samples of galaxies. We measure the major and minor merger fraction (f merg ) and build a mass-complete sample of galaxies, which we bin as a function of stellar mass and redshift. For the major mergers, we find a positive slope of fmerg with stellar mass and negative slope of f merg with redshift between stellar masses of 10.5 < M * (log M ⊙ ) < 11.6 and redshifts of 0.03 < z < 0.19. We are able to reproduce an artificial positive slope of the major merger fraction with redshift when we do not bin for mass or craft a complete sample, demonstrating the importance of mass completeness and mass binning. Further, we determine that the positive trend of the major merger fraction with stellar mass is consistent with a hierarchical assembly scenario. The negative trend with redshift requires that an additional assembly mechanism, such as baryonic feedback, dominates in the local Universe.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

A Template-based Approach to the Photometric Classification of SN 1991bg-like Supernovae in the SDSS-II Supernova Survey

The use of SNe Ia to measure cosmological parameters has grown significantly over the past two decades. However, there exists a significant diversity in the SN Ia population that is not well understood. Overluminous SN 1991T-like and subluminous SN 1991bg-like objects are two characteristic examples of peculiar SNe. The identification and classification of such objects is an important step in studying what makes them unique from the remaining SN population. With the upcoming Vera C. Rubin Observatory promising on the order of a million new SNe over a 10 year survey, spectroscopic classifications will be possible for only a small subset of observed targets. As such, photometric classification has become an increasingly important concern in preparing for the next generation of astronomical surveys. Using observations from the Sloan Digital Sky Survey II (SDSS-II) SN Survey, we apply here an empirically based classification technique targeted at the identification of SN 1991bg-like SNe in photometric data sets. By performing dedicated fits to photometric data in the rest-frame redder and bluer bandpasses, we classify 16 previously unidentified 91bg-like SNe. Using SDSS-II host galaxy measurements, we find that these SNe are preferentially found in host galaxies with an older average stellar age than the hosts of normal SNe Ia. We also find that these SNe are found at a further physical distance from the center of their host galaxies. We find no statistically significant bias in host galaxy mass or specific star formation rate for these targets.

Astronomy & Astrophysics↗
DOE OSTI2021

Z-Sequence: photometric redshift predictions for galaxy clusters with sequential random k-nearest neighbours

ABSTRACT We introduce Z-Sequence, a novel empirical model that utilizes photometric measurements of observed galaxies within a specified search radius to estimate the photometric redshift of galaxy clusters. Z-Sequence itself is composed of a machine learning ensemble based on the k-nearest neighbours algorithm. We implement an automated feature selection strategy that iteratively determines appropriate combinations of filters and colours to minimize photometric redshift prediction error. We intend for Z-Sequence to be a standalone technique but it can be combined with cluster finders that do not intrinsically predict redshift, such as our own DEEP-CEE. In this proof-of-concept study, we train, fine-tune, and test Z-Sequence on publicly available cluster catalogues derived from the Sloan Digital Sky Survey. We determine the photometric redshift prediction error of Z-Sequence via the median value of |Δ$z$|/(1 + $z$) (across a photometric redshift range of 0.05 ≤ $z$ ≤ 0.6) to be ∼0.01 when applying a small search radius. The photometric redshift prediction error for test samples increases by 30–50 per cent when the search radius is enlarged, likely due to line-of-sight interloping galaxies. Eventually, we aim to apply Z-Sequence to upcoming imaging surveys such as the Legacy Survey of Space and Time to provide photometric redshift estimates for large samples of as yet undiscovered and distant clusters.

Chan, Matthew C.↗
DOE OSTI2020

Reliable photometric membership (RPM) of galaxies in clusters – I. A machine learning method and its performance in the local universe

ABSTRACT We introduce a new method to determine galaxy cluster membership based solely on photometric properties. We adopt a machine learning approach to recover a cluster membership probability from galaxy photometric parameters and finally derive a membership classification. After testing several machine learning techniques (such as stochastic gradient boosting, model averaged neural network and k-nearest neighbours), we found the support vector machine algorithm to perform better when applied to our data. Our training and validation data are from the Sloan Digital Sky Survey main sample. Hence, to be complete to $M_r^* + 3$, we limit our work to 30 clusters with $z$phot-cl ≤ 0.045. Masses (M200) are larger than $\sim 0.6\times 10^{14} \, \mathrm{M}_{\odot }$ (most above $3\times 10^{14} \, \mathrm{M}_{\odot }$). Our results are derived taking in account all galaxies in the line of sight of each cluster, with no photometric redshift cuts or background corrections. Our method is non-parametric, making no assumptions on the number density or luminosity profiles of galaxies in clusters. Our approach delivers extremely accurate results (completeness, C $\sim 92{\rm{ per\ cent}}$ and purity, P $\sim 87{\rm{ per\ cent}}$) within R200, so that we named our code reliable photometric membership. We discuss possible dependencies on magnitude, colour, and cluster mass. Finally, we present some applications of our method, stressing its impact to galaxy evolution and cosmological studies based on future large-scale surveys, such as eROSITA, EUCLID, and LSST.

Lopes, Paulo A. A.↗
DOE OSTI2024

Velocity reconstruction in the era of DESI and Rubin/LSST. I. Exploring spectroscopic, photometric, and hybrid samples

Peculiar velocities of galaxies and halos can be reconstructed from their spatial distribution alone. This technique is analogous to the baryon acoustic oscillations reconstruction, using the continuity equation to connect density and velocity fields. The resulting reconstructed velocities can be used to measure imprints of galaxy velocities on the cosmic microwave background like the kinematic Sunyaev-Zel’dovich effect or the moving lens effect. As the precision of these measurements increases, characterizing the performance of the velocity reconstruction becomes crucial to allow unbiased and statistically optimal inference. In this paper, we quantify the relevant performance metrics: the variance of the reconstructed velocities and their correlation coefficient with the true velocities. We show that the relevant velocities to reconstruct for kSZ and moving lens are actually the halo—rather than galaxy—velocities. We quantify the impact of redshift-space distortions, photometric redshift errors, satellite galaxy fraction, incorrect cosmological parameter assumptions and smoothing scale on the reconstruction performance. Here, we also investigate hybrid reconstruction methods, where velocities inferred from spectroscopic samples are evaluated at the positions of denser photometric samples. We find that using exclusively the photometric sample is better than performing a hybrid analysis. The 2 Gpc/ℎ length simulations from abacussummit with realistic galaxy samples for DESI and Rubin LSST allow us to perform this analysis in a controlled setting. In the companion paper [B. Hadzhiyska, S. Ferraro, B. Ried Guachalla, and E. Schaan, companion paper, Phys. Rev. D 109, 103534 (2024).], we further include the effects of evolution along the light cone and give realistic performance estimates for DESI luminous red galaxies, emission line galaxies, and Rubin LSST-like samples.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Removing imaging systematics from galaxy clustering measurements with Obiwan: application to the SDSS-IV extended Baryon Oscillation Spectroscopic Survey emission-line galaxy sample

This article presents the application of a new tool, Obiwan, which uses image simulations to determine the selection function of a galaxy redshift survey and calculate three-dimensional (3D) clustering statistics. Obiwan relies on a forward model of the process by which images of the night sky are transformed into a 3D large-scale structure catalogue, and offers several advantages over more traditional map-based techniques – such as operating on individual exposures and adopting a maximum likelihood approach. The photometric pipeline automatically detects and models galaxies and then generates a catalogue of such galaxies with detailed information for each one of them, including their location, redshift, and so on. Systematic biases in the imaging data are therefore imparted into the catalogues and must be accounted for in any scientific analysis of their information content. Obiwan simulates this process for samples selected from the Legacy Surveys imaging data. This imaging data will be used to select target samples for the next-generation Dark Energy Spectroscopic Instrument (DESI) experiment. Here, we apply Obiwan to a portion of the SDSS-IV extended Baryon Oscillation Spectroscopic Survey emission-line galaxies (ELGs). Systematic biases in the data are clearly identified and removed. We compare the 3D clustering results to those obtained by the map-based approach applied to the complete eBOSS Data Release 16 (DR16) sample. We find the results are consistent, thereby validating the eBOSS DR16 ELG catalogues, which is used to obtain cosmological results.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

The clustering of DESI-like luminous red galaxies using photometric redshifts

ABSTRACT We present measurements of the redshift-dependent clustering of a DESI-like luminous red galaxy (LRG) sample selected from the Legacy Survey imaging data set, and use the halo occupation distribution (HOD) framework to fit the clustering signal. The photometric LRG sample in this study contains 2.7 million objects over the redshift range of 0.4 < z < 0.9 over 5655 deg2. We have developed new photometric redshift (photo-z) estimates using the Legacy Survey DECam and WISE photometry, with σNMAD = 0.02 precision for LRGs. We compute the projected correlation function using new methods that maximize signal-to-noise ratio while incorporating redshift uncertainties. We present a novel algorithm for dividing irregular survey geometries into equal-area patches for jackknife resampling. For a five-parameter HOD model fit using the MultiDark halo catalogue, we find that there is little evolution in HOD parameters except at the highest redshifts. The inferred large-scale structure bias is largely consistent with constant clustering amplitude over time. In an appendix, we explore limitations of Markov chain Monte Carlo fitting using stochastic likelihood estimates resulting from applying HOD methods to N-body catalogues, and present a new technique for finding best-fitting parameters in this situation. Accompanying this paper, we have released the Photometric Redshifts for the Legacy Surveys catalogue of photo-z’s obtained by applying the methods used in this work to the full Legacy Survey Data Release 8 data set. This catalogue provides accurate photometric redshifts for objects with z < 21 over more than 16 000 deg2 of sky.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

A composite likelihood approach for inference under photometric redshift uncertainty

ABSTRACT Obtaining accurately calibrated redshift distributions of photometric samples is one of the great challenges in photometric surveys like LSST, Euclid, HSC, KiDS, and DES. We present an inference methodology that combines the redshift information from the galaxy photometry with constraints from two-point functions, utilizing cross-correlations with spatially overlapping spectroscopic samples, and illustrate the approach on CosmoDC2 simulations. Our likelihood framework is designed to integrate directly into a typical large-scale structure and weak lensing analysis based on two-point functions. We discuss efficient and accurate inference techniques that allow us to scale the method to the large samples of galaxies to be expected in LSST. We consider statistical challenges like the parametrization of redshift systematics, discuss and evaluate techniques to regularize the sample redshift distributions, and investigate techniques that can help to detect and calibrate sources of systematic error using posterior predictive checks. We evaluate and forecast photometric redshift performance using data from the CosmoDC2 simulations, within which we mimic a DESI-like spectroscopic calibration sample for cross-correlations. Using a combination of spatial cross-correlations and photometry, we show that we can provide calibration of the mean of the sample redshift distribution to an accuracy of at least 0.002(1 + z), consistent with the LSST-Y1 science requirements for weak lensing and large-scale structure probes.

(cosmology:) large-scale structure of Universe↗
DOE OSTI2025

Forecast for a growth-rate measurement using peculiar velocities from LSST supernovae

We investigate whether the cosmic growth-rate parameter fσ 8 can be measured using peculiar velocities (PVs) derived from type Ia supernovae (SNe Ia) in the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST). We produced simulations of different SN types using a realistic LSST observing strategy that incorporated noise, a photometric detection from the difference-image analysis (DIA) pipeline, and a PV field modeled from the Uchuu universe machine simulations. We tested three different observational scenarios that ranged from ideal conditions with spectroscopic host galaxy redshifts and spectroscopic SN typing to realistic photometric typing that resulted in a contamination with non-Ia SNe. Using a maximum likelihood technique, we showed that the LSST can measure fσ 8 with a precision of 10% in the redshift range 0.02 < z < 0.14 for our most realistic scenario. In three tomographic bins, the LSST will be able to constrain the growth-rate parameter with errors below 18% up to redshift z = 0.14. We also tested the contamination effect on the maximum likelihood method and found that for a contamination fraction below ∼2%, we recovered unbiased measurements. The results of this analysis highlight that the LSST SN sample is expected to complement traditional redshift-space distortion measurements at high redshift. This will provide a novel avenue for testing general relativity and different dark energy models.

Rosselli, D↗
DOE OSTI2020

Learning Spectral Templates for Photometric Redshift Estimation from Broadband Photometry

Estimating redshifts from broadband photometry is often limited by how accurately we can map the colors of galaxies to an underlying spectral template. Current techniques utilize spectrophotometric samples of galaxies or spectra derived from spectral synthesis models. Both of these approaches have their limitations: either the sample sizes are small and often not representative of the diversity of galaxy colors, or the model colors can be biased (often as a function of wavelength), which introduces systematics in the derived redshifts. In this paper, we learn the underlying spectral energy distributions from an ensemble of ∼100 K galaxies with measured redshifts and colors. We show that we are able to reconstruct emission and absorption lines at a significantly higher resolution than the broadband filters used to measure the photometry for a sample of 20 spectral templates. We find that our training algorithm reduces the fraction of outliers in the derived photometric redshifts by up to 28%, bias up to 91%, and scatter up to 25%, when compared to estimates using a standard set of spectral templates. We discuss the current limitations of this approach and its applicability for recovering the underlying properties of galaxies. Our derived templates and the code used to produce these results are publicly available in a dedicated Github repository: https://github.com/dirac-institute/photoz-template-learning.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Active anomaly detection for time-domain discoveries

Aims. We present the first evidence that adaptive learning techniques can boost the discovery of unusual objects within astronomical light curve data sets. Methods. Our method follows an active learning strategy where the learning algorithm chooses objects which can potentially improve the learner if additional information about them is provided. This new information is subsequently used to update the machine learning model, allowing its accuracy to evolve with each new information. For the case of anomaly detection, the algorithm aims to maximize the number of scientifically interesting anomalies presented to the expert by slightly modifying the weights of a traditional Isolation Forest (IF) at each iteration. In order to demonstrate the potential of such techniques, we apply the Active Anomaly Discovery (AAD) algorithm to 2 data sets: simulated light curves from the Photometric LSST Astronomical Time-Series Classification Challenge (PLAsTiCC) and real light curves from the Open Supernova Catalog. We compare the AAD results to those of a static IF. For both methods, we performed a detailed analysis for all objects with the ~2% highest anomaly scores. Results. We show that, in the real data scenario , AAD was able to identify ~80% more true anomalies than the IF. This result is the first evidence that AAD algorithms can play a central role in the search for new physics in the era of large scale sky surveys.

79 ASTRONOMY AND ASTROPHYSICS↗