arXiv subjects

D. J. Schlegel

Publications and source records attributed to D. J. Schlegel.

At least 19 recordsLinked to original sources

arXiv2026

The DESI Y1 RR Lyrae catalog II: The metallicity dependency of pulsational properties, the shape of the RR Lyrae instability strip, and metal rich RR Lyrae

RR Lyrae stars (RRLs) are valuable probes of both Milky Way assembly and stellar-evolution physics. Using a sample 6,240 RRLs obtained in the first year of the Dark Energy Spectroscopic Instrument (DESI) survey, we investigate the metallicity of RRLs and its correlation with their pulsation properties. We find that (1) a clear correlation between period and [Fe/H] reinforces the view that the long-standing Oosterhoff dichotomy arises from the scarcity of intermediate-metallicity Galactic globular clusters hosting sizeable RRL samples; (2) high-amplitude short-period and small-amplitude short-period variables are comparatively metal-rich, with mean [Fe/H] = $-1.39 \pm 0.27$ and $-1.30 \pm 0.28$, respectively; (3) in double-mode pulsators (RRd) the metallicity declines smoothly with increasing fundamental-mode period, and anomalous RRd stars occupy a remarkably narrow [Fe/H] range relative to classical RRd stars; (4) this spectroscopic sample let us, for the first time, place empirical constraints on the metallicity-dependent topology of the instability strip using phase-corrected effective temperatures and a large number of RRLs, where we observe an instability strip that moves towards cooler $T_{\rm eff}$ with declining [Fe/H] with a width roughly consistent with stellar-evolution models; and (5) a subset of metal-rich RRLs exhibits orbits consistent with disk membership and halo kinematics. Our results confirm the tantalizing potential of DESI for Galactic and stellar astrophysics and highlight the importance of the even larger samples of RRLs and data-processing improvements forthcoming in future DESI data releases.

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arXiv2026

DESI Strong Lens Foundry III: Keck Spectroscopy for Strong Lenses Discovered Using Residual Neural Networks

We present spectroscopic data of strong lenses and their source galaxies using the Keck Near-Infrared Echellette Spectrometer (NIRES) and the Dark Energy Spectroscopic Instrument (DESI), providing redshifts necessary for nearly all strong-lensing applications with these systems, especially the extraction of physical parameters from lensing modeling. These strong lenses were found in the DESI Legacy Imaging Surveys using Residual Neural Networks (ResNet) and followed up by our Hubble Space Telescope program, with all systems displaying unambiguous lensed arcs. With NIRES, we target eight lensed sources at redshifts difficult to measure in the optical range and determine the source redshifts for six, between $z_s$ = 1.675 and 3.332. DESI observed one of the remaining source redshifts, as well as an additional source redshift within the six systems. The two systems with non-detections by NIRES were observed for a considerably shorter 600s at high airmass. Combining NIRES infrared spectroscopy with optical spectroscopy from our DESI Strong Lensing Secondary Target Program, these results provide the complete lens and source redshifts for six systems, a resource for refining automated strong lens searches in future deep- and wide-field imaging surveys and addressing a range of questions in astrophysics and cosmology.

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arXiv2025

DESI Strong Lens Foundry V: A Sample of HST-Observed Strong Lenses Modeled with GIGA-Lens

We present six galaxy-scale strong lenses with HST imaging modeled using GIGA-Lens. This is Paper V of the DESI Strong Lens Foundry series. These systems were discovered in the DESI Legacy Imaging Surveys using ML/AI methods and confirmed with DESI, Keck/NIRES, and VLT/MUSE spectroscopy. They span $z_d = 0.39 - 1.1$ and $z_s = 1.4 - 3.3$. This is the first HST strong lens sample modeled with full forward modeling -- all lens and source parameters sampled simultaneously in a single inference -- with explicit convergence validation using both $\widehat{R}$ and effective sample size (ESS) for each system. All inferred parameters satisfy $\widehat{R} < 1.1$ and ${\rm ESS} \gtrsim 10,000$, demonstrating that GIGA-Lens achieves statistically robust inference even for some of the most complex galaxy-scale lenses known. These results pave the way for scaling to much larger, high-resolution strong lens samples from HST, Euclid, JWST, and Roman. Convergence-validated modeling will be critical for key science goals, including constraining the mass-density profile of galaxies, detecting low-mass dark matter (sub)halos, and delivering precise and accurate cosmological constraints.

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arXiv2025

DESI Strong Lens Foundry II: DESI Spectroscopy for Strong Lens Candidates

We present the Dark Energy Spectroscopic Instrument (DESI) Strong Lensing Secondary Target Program. This is a spectroscopic follow-up program for strong gravitational lens candidates found in the DESI Legacy Imaging Surveys footprint. Spectroscopic redshifts for the lenses and lensed source are crucial for lens modeling to obtain physical parameters. The spectroscopic catalog in this paper consists of 73 candidate systems from the DESI Early Data Release (EDR). We have confirmed 20 strong lensing systems and determined four to not be lenses. For the remaining systems, more spectroscopic data from ongoing and future observations will be presented in future publications. We discuss the implications of our results for lens searches with neural networks in existing and future imaging surveys as well as for lens modeling. This Strong Lensing Secondary Target Program is part of the DESI Strong Lens Foundry project, and this is Paper II of a series on this project.

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arXiv2025

Strong Lens Discoveries in DESI Legacy Imaging Surveys DR10 with Two Deep Learning Architectures

We have conducted a search for strong gravitational lensing systems in the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys Data Release 10 (DR10). This paper is the fourth in a series of searches (following Huang et al. 2020; Huang et al. 2021; Storfer et al. 2024, Paper I, II, & III respectively). This is the first catalog of lens candidates covering nearly the entirety of the extragalactic sky south of declination $δ\approx +32$ deg, all of it observed by the DECam, covering $\sim$14,000 $deg^2$. We impose a $z$-band magnitude cut of < 20 in AB magnitude. We deploy a Residual Neural Network and EfficientNet as an ensemble trained on a compilation of known lensing systems and high-grade candidates as well as nonlenses in the same footprint. The predictions from these two base models are aggregated using a meta-learner. After applying our ensemble to the survey data, we exclude known candidates and systems, and use our own visual inspection portal to rank images in the top 0.01 percentile of all neural network recommendations. We have found 811 new lens candidates. These include 484 new candidates in the Legacy Surveys DR9 footprint, all parts of which have been searched for strong lenses at least once before, either by our group or others. Combining the discoveries from this work with those from Paper I (335), II (1210), and III (1512), we have discovered a total of 3868 new candidates in the DESI Legacy Surveys.

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arXiv2025

DESI Strong Lens Foundry I: HST Observations and Modeling with GIGA-Lens

We present the Dark Energy Spectroscopic Instrument (DESI) Strong Lens Foundry. We discovered $\sim 3500$ new strong gravitational lens candidates in the DESI Legacy Imaging Surveys using residual neural networks (ResNet). We observed a subset (51) of our candidates using the Hubble Space Telescope (HST). All of them were confirmed to be strong lenses. We also briefly describe spectroscopic follow-up observations by DESI and Keck NIRES programs. From this very rich dataset, a number of studies will be carried out, including evaluating the quality of the ResNet search candidates and lens modeling. In this paper, we present our initial effort in these directions. In particular, as a demonstration, we present the lens model for DESI-165.4754-06.0423, with imaging data from HST, and lens and source redshifts from DESI and Keck NIRES, respectively. In this effort, we have applied a \emph{fully} forward-modeling Bayesian approach (GIGA-Lens), using \emph{multiple} GPUs, for the first time in both regards, to a strong lens with HST data, or any high resolution imaging.

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arXiv2024

New Strong Gravitational Lenses from the DESI Legacy Imaging Surveys Data Release 9

We have conducted a search for strong gravitational lensing systems in the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys Data Release 9. This is the third paper in a series. These surveys together cover $\sim$19,000 deg$^2$ visible from the Northern Hemisphere, reaching a z-band AB magnitude of $\sim$22.5. We use a deep residual neural network, trained on a compilation of known lensing systems and high-grade candidates as well as nonlenses in the same footprint. After applying our trained neural network to the survey data, we visually inspect and rank images with probabilities above a threshold which has been chosen to balance precision and recall. We have found 1895 lens candidates, of which 1512 are identified for the first time. Combining the discoveries from this work with those from Papers I (335) and II (1210), we have discovered a total of 3057 new candidates in the Legacy Surveys.

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arXiv2024

Survey Operations for the Dark Energy Spectroscopic Instrument

The Dark Energy Spectroscopic Instrument (DESI) survey is a spectroscopic survey of tens of millions of galaxies at $0 < z < 3.5$ covering 14,000 square degrees of the sky. In its first 1.1 years of survey operations, it has observed more than 14 million galaxies and 4 million stars. We describe the processes that govern DESI's observations of the 15,000 fields composing the survey. This includes the planning of each night's observations in the afternoon; automatic selection of fields to observe during the night; real-time assessment of field completeness on the basis of observing conditions during each exposure; reduction, redshifting, and quality assurance of each field of targets in the morning following observation; and updates to the list of future targets to observe on the basis of these results. We also compare the performance of the survey with historical expectations and find good agreement. Simulations of the weather and of DESI observations using the real field-selection algorithm show good agreement with the actual observations. After accounting for major unplanned shutdowns, the dark time survey is progressing about 7% faster than forecast, which is good agreement given approximations made in the simulations.

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arXiv2023

Finding Multiply-Lensed and Binary Quasars in the DESI Legacy Imaging Surveys

The time delay between multiple images of strongly lensed quasars is a powerful tool for measuring the Hubble constant (H0). To achieve H0 measurements with higher precision and accuracy using the time delay, it is crucial to expand the sample of lensed quasars. We conduct a search for strongly lensed quasars in the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys. The DESI Legacy Surveys comprise 19,000 deg2 of the extragalactic sky observed in three optical bands (g, r, and z), making it well suited for the discovery of new strongly lensed quasars. We apply an autocorrelation algorithm to ~5 million objects classified as quasars in the DESI Quasar Sample. These systems are visually inspected and ranked. Here, we present 436 new multiply lensed and binary quasar candidates, 65 of which have redshifts from Sloan Digital Sky Survey Data Release 16. We provide redshifts for an additional 18 candidates from the SuperNova Integral Field Spectrograph.

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arXiv2022

GIGA-Lens: Fast Bayesian Inference for Strong Gravitational Lens Modeling

We present GIGA-Lens: a gradient-informed, GPU-accelerated Bayesian framework for modeling strong gravitational lensing systems, implemented in TensorFlow and JAX. The three components, optimization using multi-start gradient descent, posterior covariance estimation with variational inference, and sampling via Hamiltonian Monte Carlo, all take advantage of gradient information through automatic differentiation and massive parallelization on graphics processing units (GPUs). We test our pipeline on a large set of simulated systems and demonstrate in detail its high level of performance. The average time to model a single system on four Nvidia A100 GPUs is 105 seconds. The robustness, speed, and scalability offered by this framework make it possible to model the large number of strong lenses found in current surveys and present a very promising prospect for the modeling of $\mathcal{O}(10^5)$ lensing systems expected to be discovered in the era of the Vera C. Rubin Observatory, Euclid, and the Nancy Grace Roman Space Telescope.

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arXiv2021

Lensing Without Borders. I. A Blind Comparison of the Amplitude of Galaxy-Galaxy Lensing Between Independent Imaging Surveys

Lensing Without Borders is a cross-survey collaboration created to assess the consistency of galaxy-galaxy lensing signals ($ΔΣ$) across different data-sets and to carry out end-to-end tests of systematic errors. We perform a blind comparison of the amplitude of $ΔΣ$ using lens samples from BOSS and six independent lensing surveys. We find good agreement between empirically estimated and reported systematic errors which agree to better than 2.3$σ$ in four lens bins and three radial ranges. For lenses with $z_{\rm L}>0.43$ and considering statistical errors, we detect a 3-4$σ$ correlation between lensing amplitude and survey depth. This correlation could arise from the increasing impact at higher redshift of unrecognised galaxy blends on shear calibration and imperfections in photometric redshift calibration. At $z_{\rm L}>0.54$ amplitudes may additionally correlate with foreground stellar density. The amplitude of these trends is within survey-defined systematic error budgets which are designed to include known shear and redshift calibration uncertainty. Using a fully empirical and conservative method, we do not find evidence for large unknown systematics. Systematic errors greater than 15% (25%) ruled out in three lens bins at 68% (95%) confidence at $z<0.54$. Differences with respect to predictions based on clustering are observed to be at the 20-30% level. Our results therefore suggest that lensing systematics alone are unlikely to fully explain the "lensing is low" effect at $z<0.54$. This analysis demonstrates the power of cross-survey comparisons and provides a promising path for identifying and reducing systematics in future lensing analyses.

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arXiv2021

Finding Strong Gravitational Lenses in the DESI DECam Legacy Survey

We perform a semi-automated search for strong gravitational lensing systems in the 9,000 deg$^2$ Dark Energy Camera Legacy Survey (DECaLS), part of the DESI Legacy Imaging Surveys (Dey et al.). The combination of the depth and breadth of these surveys are unparalleled at this time, making them particularly suitable for discovering new strong gravitational lensing systems. We adopt the deep residual neural network architecture (He et al.) developed by Lanusse et al. for the purpose of finding strong lenses in photometric surveys. We compile a training set that consists of known lensing systems in the Legacy Surveys and DES as well as non-lenses in the footprint of DECaLS. In this paper we show the results of applying our trained neural network to the cutout images centered on galaxies typed as ellipticals (Lang et al.) in DECaLS. The images that receive the highest scores (probabilities) are visually inspected and ranked. Here we present 335 candidate strong lensing systems, identified for the first time.

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arXiv2021

Discovering New Strong Gravitational Lenses in the DESI Legacy Imaging Surveys

We have conducted a search for new strong gravitational lensing systems in the Dark Energy Spectroscopic Instrument Legacy Imaging Surveys' Data Release 8. We use deep residual neural networks, building on previous work presented in Huang et al. (2020). These surveys together cover approximately one third of the sky visible from the northern hemisphere, reaching a z band AB magnitude of ~22.5. We compile a training sample that consists of known lensing systems as well as non-lenses in the Legacy Surveys and the Dark Energy Survey. After applying our trained neural networks to the survey data, we visually inspect and rank images with probabilities above a threshold. Here we present 1210 new strong lens candidates.

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arXiv2019

unWISE Coadds: The Five-year Data Set

We present full-sky coadded maps created by uniformly combining the first five years of Wide-field Infrared Survey Explorer (WISE) and NEOWISE imaging at 3.4 microns (W1) and 4.6 microns (W2). By incorporating both pre-hibernation WISE exposures from 2010-2011 and the first four years (2013-2017) of post-hibernation exposures from the NEOWISE-Reactivation mission, we are able to provide W1/W2 coadds that span a 15 times longer time baseline and are substantially deeper than the standard AllWISE data products. Our new five-year "full-depth" coadds are now the deepest ever all-sky maps at 3-5 microns, permitting detection of sources ~2 times (~0.7 mag) fainter than AllWISE at 5 sigma significance. We additionally present an updated set of "time-resolved" W1/W2 coadds, which separately stack each of ~10 sky passes at each inertial sky location, enabling motion and variability measurements for faint infrared sources over a long ~7.5 year time baseline. We highlight new processing improvements relative to our previous "unWISE" coadd releases, focusing on astrometric calibration and artifact flagging. The deep WISE stacks presented here are already being used to perform target selection for the Dark Energy Spectroscopic Instrument (DESI), and our full-sky coadded WISE/NEOWISE products will be key precursor data sets for upcoming wide-field infrared missions including SPHEREx and NEOCam.

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arXiv2018

Time-resolved WISE/NEOWISE Coadds

We have used the first ~3 years of 3.4 micron (W1) and 4.6 micron (W2) observations from the WISE and NEOWISE missions to create a full-sky set of time-resolved coadds. As a result of the WISE survey strategy, a typical sky location is visited every six months and is observed during 12 or more exposures per visit, with these exposures spanning a ~1 day time interval. We have stacked the exposures within such ~1 day intervals to produce one coadd per band per visit -- that is, one coadd every six months at a given position on the sky in each of W1 and W2. For most parts of the sky we have generated six epochal coadds per band, with one visit during the fully cryogenic WISE mission, one visit during NEOWISE, and then, after a 33 month gap, four more visits during the NEOWISE-Reactivation mission phase. These coadds are suitable for studying long-timescale mid-infrared variability and measuring motions to ~1.3 magnitudes fainter than the single-exposure detection limit. In most sky regions, our coadds span a 5.5 year time period and therefore provide a >10x enhancement in time baseline relative to that available for the AllWISE catalog's apparent motion measurements. As such, the signature application of these new coadds is expected to be motion-based identification of relatively faint brown dwarfs, especially those cold enough to remain undetected by Gaia.

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arXiv2018

Another unWISE Update: The Deepest Ever Full-sky Maps at 3-5 microns

We have uniformly reprocessed ~140 terabytes of WISE and NEOWISE exposures to create the deepest ever full-sky maps at 3.4 microns (W1) and 4.6 microns (W2). Our coadds include ~4 years of observations and therefore feature ~4 times greater integer frame coverage than the AllWISE Atlas stacks. Our new, publicly available maps should find a wide range of applications, and in particular will enable the selection of luminous red galaxy and quasar targets for the Dark Energy Spectroscopic Instrument (DESI).

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arXiv2017

The Electromagnetic Counterpart of the Binary Neutron Star Merger LIGO/VIRGO GW170817. II. UV, Optical, and Near-IR Light Curves and Comparison to Kilonova Models

We present UV, optical, and NIR photometry of the first electromagnetic counterpart to a gravitational wave source from Advanced LIGO/Virgo, the binary neutron star merger GW170817. Our data set extends from the discovery of the optical counterpart at $0.47$ days to $18.5$ days post-merger, and includes observations with the Dark Energy Camera (DECam), Gemini-South/FLAMINGOS-2 (GS/F2), and the {\it Hubble Space Telescope} ({\it HST}). The spectral energy distribution (SED) inferred from this photometry at $0.6$ days is well described by a blackbody model with $T\approx 8300$ K, a radius of $R\approx 4.5\times 10^{14}$ cm (corresponding to an expansion velocity of $v\approx 0.3c$), and a bolometric luminosity of $L_{\rm bol}\approx 5\times10^{41}$ erg s$^{-1}$. At $1.5$ days we find a multi-component SED across the optical and NIR, and subsequently we observe rapid fading in the UV and blue optical bands and significant reddening of the optical/NIR colors. Modeling the entire data set we find that models with heating from radioactive decay of $^{56}$Ni, or those with only a single component of opacity from $r$-process elements, fail to capture the rapid optical decline and red optical/NIR colors. Instead, models with two components consistent with lanthanide-poor and lanthanide-rich ejecta provide a good fit to the data, the resulting "blue" component has $M_\mathrm{ej}^\mathrm{blue}\approx 0.01$ M$_\odot$ and $v_\mathrm{ej}^\mathrm{blue}\approx 0.3$c, and the "red" component has $M_\mathrm{ej}^\mathrm{red}\approx 0.04$ M$_\odot$ and $v_\mathrm{ej}^\mathrm{red}\approx 0.1$c. These ejecta masses are broadly consistent with the estimated $r$-process production rate required to explain the Milky Way $r$-process abundances, providing the first evidence that BNS mergers can be a dominant site of $r$-process enrichment.

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arXiv2017

The Electromagnetic Counterpart of the Binary Neutron Star Merger LIGO/Virgo GW170817. I. Dark Energy Camera Discovery of the Optical Counterpart

We present the Dark Energy Camera (DECam) discovery of the optical counterpart of the first binary neutron star merger detected through gravitational wave emission, GW170817. Our observations commenced 10.5 hours post-merger, as soon as the localization region became accessible from Chile. We imaged 70 deg$^2$ in the $i$ and $z$ bands, covering 93\% of the initial integrated localization probability, to a depth necessary to identify likely optical counterparts (e.g., a kilonova). At 11.4 hours post-merger we detected a bright optical transient located $10.6''$ from the nucleus of NGC\,4993 at redshift $z=0.0098$, consistent (for $H_0 = 70$\, km s$^{-1}$ Mpc$^{-1}$) with the distance of $40 \pm 8$\, Mpc reported by the LIGO Scientific Collaboration and the Virgo Collaboration (LVC). At detection the transient had magnitudes $i\approx 17.30$ and $z\approx 17.45$, and thus an absolute magnitude of $M_i = -15.7$, in the luminosity range expected for a kilonova. We identified 1,500 potential transient candidates. Applying simple selection criteria aimed at rejecting background events such as supernovae, we find the transient associated with NGC\,4993 as the only remaining plausible counterpart, and reject chance coincidence at the 99.5\% confidence level. We therefore conclude that the optical counterpart we have identified near NGC\,4993 is associated with GW170817. This discovery ushers in the era of multi-messenger astronomy with gravitational waves, and demonstrates the power of DECam to identify the optical counterparts of gravitational-wave sources.

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