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At least 163 records · Page 9

2017 National Household Travel Survey - Arizona Add-On

# 2017 National Household Travel Survey – Arizona Add-On The Arizona add-on survey supplements the 2017 National Household Travel Survey (NHTS) with additional household samples and detailed travel behavior for an assigned travel day. ## Data Collection Agency The Federal Highway Administration conducted the NHTS and corresponding add-on surveys. ## Methodology This survey documents the demographic, attitudinal, and travel behavior for all members of 2,987 households, as collected from April 2016 to April 2017. Daily travel details provide insight into work and school commutes, non-emergency medical trips, shopping trips, and even how travel differs in the summer and on weekends as compared to a typical weekday when school is in session. When statistically weighted to adjust for survey biases, the data demographically represents all Americans and is appropriate for analysis at the national and census region levels. ## Survey Records Survey records include a total of 6,081 participants. ## Transportation Data The NHTS Arizona add-on data package contains a demographic and socioeconomic composition of 6,081 people from 2,987 households in Arizona, as well as detailed information on the travel behavior of each household for a designated 24-hour period. The survey logged more than 258,000 vehicle miles of travel by participants during 19,779 trips. For details on available data and variable definitions, see the [data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/data-elements-2017.xlsx?sfvrsn=ad09b18c_3). Transportation data are available as zipped files. [Download Winzip](http://www.winzip.com/downwz.htm).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

2022 Kauai Ground Transportation Survey

# 2022 Kauai Ground Transportation Survey The 2022 Kauai Ground Transportation Survey was designed to improve understanding of the transportation preferences of residents and visitors of the Hawaiian island of Kauai. For residents of Kauai, the survey focused on mode-choice decisions for commute, leisure, and shopping activities and whether they would consider using alternative modes instead of driving if such options were available. For visitors, the survey focused on whether they would consider alternatives to rental cars if such options were available. Ultimately, survey results will inform the development of sustainable transportation strategies for the island. ## Data Collection Agency NREL and the Kauai Office of Economic Development conducted the study as part of the U.S. Department of Energy's Energy Transitions Initiative Partnership Project. ## Survey Methodology The survey was conducted July—October 2022. Respondents were recruited via direct email invitation, a press release on the Kauai County website and Facebook page, and posters at community outreach events. ## Survey Records, Data, and Documentation Survey records include 1,437 participants.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

2012 California Household Travel Survey Supplement

# 2012 California Household Travel Survey Supplement The 2012 California Household Travel Survey Supplement focused on gathering specific travel information from residents for the development of next-generation, activity-based models. Called the "Augment Survey," it supplemented the [2010–2012 California Household Travel Survey](https://www.nrel.gov/transportation/secure-transportation-data/tsdc-california-travel-survey). ## Data Collection Agency The Southern California Association of Governments (SCAG) hired Abt-SRBI, Inc. to conduct the survey. ## Methodology Travel data were collected from households via in-vehicle (625 vehicles) and wearable (244 participants) global positioning system (GPS) devices. ## Drive Cycle Processing and Filtering NREL has developed a GPS data filtration routine to filter erroneous data points in individual drive cycles sourced from GPS devices mounted in vehicles. Second-by-second drive cycle data collected from GPS-instrumented vehicles during this survey have passed through NREL's drive cycle processing and filtering routines. ## Survey Records Study records include 473 households. ## Transportation Data The SCAG data set contains data from 473 households that participated in one or more areas of study. Of these, 141 completed the wearable GPS portion of the study and 332 completed the vehicle GPS portion. There was no overlap between households participating in the two study areas (wearable and vehicle GPS). For details on available travel survey data and variable definitions, see the [data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/caltrans_scag_data_dictionary.pdf?sfvrsn=6ec36d7a_1). NREL-generated drive cycle data are also available for this survey. For details on available data and variable definitions, see the [drive cycle data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/drive_cycles_data_dictionary.pdf?sfvrsn=7de7e888_1). Transportation data are available as zipped files. [Download Winzip](http://www.winzip.com/downwz.htm).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

2017 National Household Travel Survey - Arizona Add-On

# 2017 National Household Travel Survey – Arizona Add-On The Arizona add-on survey supplements the 2017 National Household Travel Survey (NHTS) with additional household samples and detailed travel behavior for an assigned travel day. ## Data Collection Agency The Federal Highway Administration conducted the NHTS and corresponding add-on surveys. ## Methodology This survey documents the demographic, attitudinal, and travel behavior for all members of 2,987 households, as collected from April 2016 to April 2017. Daily travel details provide insight into work and school commutes, non-emergency medical trips, shopping trips, and even how travel differs in the summer and on weekends as compared to a typical weekday when school is in session. When statistically weighted to adjust for survey biases, the data demographically represents all Americans and is appropriate for analysis at the national and census region levels. ## Survey Records Survey records include a total of 6,081 participants. ## Transportation Data The NHTS Arizona add-on data package contains a demographic and socioeconomic composition of 6,081 people from 2,987 households in Arizona, as well as detailed information on the travel behavior of each household for a designated 24-hour period. The survey logged more than 258,000 vehicle miles of travel by participants during 19,779 trips. For details on available data and variable definitions, see the [data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/data-elements-2017.xlsx?sfvrsn=ad09b18c_3). Transportation data are available as zipped files. [Download Winzip](http://www.winzip.com/downwz.htm).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

2022 Kauai Ground Transportation Survey

# 2022 Kauai Ground Transportation Survey The 2022 Kauai Ground Transportation Survey was designed to improve understanding of the transportation preferences of residents and visitors of the Hawaiian island of Kauai. For residents of Kauai, the survey focused on mode-choice decisions for commute, leisure, and shopping activities and whether they would consider using alternative modes instead of driving if such options were available. For visitors, the survey focused on whether they would consider alternatives to rental cars if such options were available. Ultimately, survey results will inform the development of sustainable transportation strategies for the island. ## Data Collection Agency NREL and the Kauai Office of Economic Development conducted the study as part of the U.S. Department of Energy's Energy Transitions Initiative Partnership Project. ## Survey Methodology The survey was conducted July—October 2022. Respondents were recruited via direct email invitation, a press release on the Kauai County website and Facebook page, and posters at community outreach events. ## Survey Records, Data, and Documentation Survey records include 1,437 participants.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

2012 California Household Travel Survey Supplement

# 2012 California Household Travel Survey Supplement The 2012 California Household Travel Survey Supplement focused on gathering specific travel information from residents for the development of next-generation, activity-based models. Called the "Augment Survey," it supplemented the [2010–2012 California Household Travel Survey](https://www.nrel.gov/transportation/secure-transportation-data/tsdc-california-travel-survey). ## Data Collection Agency The Southern California Association of Governments (SCAG) hired Abt-SRBI, Inc. to conduct the survey. ## Methodology Travel data were collected from households via in-vehicle (625 vehicles) and wearable (244 participants) global positioning system (GPS) devices. ## Drive Cycle Processing and Filtering NREL has developed a GPS data filtration routine to filter erroneous data points in individual drive cycles sourced from GPS devices mounted in vehicles. Second-by-second drive cycle data collected from GPS-instrumented vehicles during this survey have passed through NREL's drive cycle processing and filtering routines. ## Survey Records Study records include 473 households. ## Transportation Data The SCAG data set contains data from 473 households that participated in one or more areas of study. Of these, 141 completed the wearable GPS portion of the study and 332 completed the vehicle GPS portion. There was no overlap between households participating in the two study areas (wearable and vehicle GPS). For details on available travel survey data and variable definitions, see the [data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/caltrans_scag_data_dictionary.pdf?sfvrsn=6ec36d7a_1). NREL-generated drive cycle data are also available for this survey. For details on available data and variable definitions, see the [drive cycle data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/drive_cycles_data_dictionary.pdf?sfvrsn=7de7e888_1). Transportation data are available as zipped files. [Download Winzip](http://www.winzip.com/downwz.htm).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

2017 National Household Travel Survey - Arizona Add-On

# 2017 National Household Travel Survey – Arizona Add-On The Arizona add-on survey supplements the 2017 National Household Travel Survey (NHTS) with additional household samples and detailed travel behavior for an assigned travel day. ## Data Collection Agency The Federal Highway Administration conducted the NHTS and corresponding add-on surveys. ## Methodology This survey documents the demographic, attitudinal, and travel behavior for all members of 2,987 households, as collected from April 2016 to April 2017. Daily travel details provide insight into work and school commutes, non-emergency medical trips, shopping trips, and even how travel differs in the summer and on weekends as compared to a typical weekday when school is in session. When statistically weighted to adjust for survey biases, the data demographically represents all Americans and is appropriate for analysis at the national and census region levels. ## Survey Records Survey records include a total of 6,081 participants. ## Transportation Data The NHTS Arizona add-on data package contains a demographic and socioeconomic composition of 6,081 people from 2,987 households in Arizona, as well as detailed information on the travel behavior of each household for a designated 24-hour period. The survey logged more than 258,000 vehicle miles of travel by participants during 19,779 trips. For details on available data and variable definitions, see the [data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/data-elements-2017.xlsx?sfvrsn=ad09b18c_3). Transportation data are available as zipped files. [Download Winzip](http://www.winzip.com/downwz.htm).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

2022 Kauai Ground Transportation Survey

# 2022 Kauai Ground Transportation Survey The 2022 Kauai Ground Transportation Survey was designed to improve understanding of the transportation preferences of residents and visitors of the Hawaiian island of Kauai. For residents of Kauai, the survey focused on mode-choice decisions for commute, leisure, and shopping activities and whether they would consider using alternative modes instead of driving if such options were available. For visitors, the survey focused on whether they would consider alternatives to rental cars if such options were available. Ultimately, survey results will inform the development of sustainable transportation strategies for the island. ## Data Collection Agency NREL and the Kauai Office of Economic Development conducted the study as part of the U.S. Department of Energy's Energy Transitions Initiative Partnership Project. ## Survey Methodology The survey was conducted July—October 2022. Respondents were recruited via direct email invitation, a press release on the Kauai County website and Facebook page, and posters at community outreach events. ## Survey Records, Data, and Documentation Survey records include 1,437 participants.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Carnivore Survey Results for the Lawrence Livermore National Laboratory Site 300

ECORP Consulting, Inc. conducted a suite of carnivore surveys at the Lawrence Livermore National Laboratory’s (LLNL) Experimental Test Site (Site 300; project site). The project site is located east of the City of Livermore and southwest of the City of Tracy, Alameda and San Joaquin Counties, California. Mesocarnivore surveys were conducted on the project site in 2002 in preparation for the 2005 LLNL Site-Wide Environmental Impact Statement (SWEIS; Clark et al. 2003). The 2002 surveys included a combination of spotlighting, camera stations, and scat detection dog surveys. Additional scat detection dog surveys for San Joaquin kit fox (Vulpes macrotis mutica) were performed on Site 300 and the adjacent California Department of Fish and Wildlife’s (CDFW) Corral Hollow Ecological Reserve in 2018 and 2020 (Woollett 2019, 2021). A variety of species, including American badger (Taxidea taxus), coyote (Canis latrans), and bobcat (Lynx rufus), were detected during the previous surveys; however, no San Joaquin kit fox or mountain lion (Puma concolor) were detected. In support of the preparation of the new SWEIS, ECORP performed carnivore surveys to document the carnivores and other animals present on Site 300, with a focus on determining if the federally listed (endangered) and state listed (threatened) San Joaquin kit fox, CDFW Species of Special Concern (SSC) American badger, and/or mountain lion are present on Site 300. American badgers are known to occur on the property and there have been a few sporadic sightings of mountain lions, but San Joaquin kit fox have not been documented on Site 300. Although mountain lion were not detected during the 2002 survey, it has been added as a focal species for this survey because the southern and central coast evolutionary significant units of mountain lions were recently advanced by CDFW as a Candidate species for listing as “threatened” under the California Endangered Species Act.

54 ENVIRONMENTAL SCIENCES↗

BatAnalysis - A Comprehensive Python Pipeline for Swift BAT Survey Analysis

The Swift Burst Alert Telescope (BAT) is a coded-aperture gamma-ray instrument with a large field of view that primarily operates in survey mode when it is not triggering on transient events. The survey data consist of 80- channel detector plane histograms that accumulate photon counts over periods of at least 5 minutes. These histograms are processed on the ground and are used to produce the survey data set between 14 and 195 keV. Survey data comprise >90% of all BAT data by volume and allow for the tracking of long-term light curves and spectral properties of cataloged and uncataloged hard X-ray sources. Until now, the survey data set has not been used to its full potential due to the complexity associated with its analysis and the lack of easily usable pipelines. Here, we introduce the BatAnalysis Python package, a wrapper for HEASoftpy, which provides a modern, opensource pipeline to process and analyze BAT survey data. BatAnalysis allows members of the community to use BAT survey data in more advanced analyses of astrophysical sources, including pulsars, pulsar wind nebula, active galactic nuclei, and other known/unknown transient events that may be detected in the hard X-ray band. We outline the steps taken by the Python code and exemplify its usefulness and accuracy by analyzing survey data of the Crab Nebula, NGC 2992, and a previously uncataloged MAXI transient. The BatAnalysis package allows for ~18 yr of BAT survey data to be used in a systematic way to study a large variety of astrophysical sources.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

BatAnalysis - A Comprehensive Python Pipeline for Swift BAT Survey Analysis

The Swift Burst Alert Telescope (BAT) is a coded-aperture gamma-ray instrument with a large field of view that primarily operates in survey mode when it is not triggering on transient events. The survey data consist of 80-channel detector plane histograms that accumulate photon counts over periods of at least 5 minutes. These histograms are processed on the ground and are used to produce the survey data set between 14 and 195 keV. Survey data comprise >90% of all BAT data by volume and allow for the tracking of long-term light curves and spectral properties of cataloged and uncataloged hard X-ray sources. Until now, the survey data set has not been used to its full potential due to the complexity associated with its analysis and the lack of easily usable pipelines. Here, we introduce the BatAnalysis Python package, a wrapper for HEASoftpy, which provides a modern, open-source pipeline to process and analyze BAT survey data. BatAnalysis allows members of the community to use BAT survey data in more advanced analyses of astrophysical sources, including pulsars, pulsar wind nebula, active galactic nuclei, and other known/unknown transient events that may be detected in the hard X-ray band. We outline the steps taken by the Python code and exemplify its usefulness and accuracy by analyzing survey data of the Crab Nebula, NGC 2992, and a previously uncataloged MAXI transient. The BatAnalysis package allows for ~18 yr of BAT survey data to be used in a systematic way to study a large variety of astrophysical sources.

79 ASTRONOMY AND ASTROPHYSICS↗

Challenges and Practical Solutions for Conducting Structural MARSSIM Surveys in Elevated Radon Environments - 20413

The Multi-Agency Radiation Survey and Site Investigation Manual (MARSSIM) [1] provides comprehensive guidance for the development and implementation of radiological surveys conducted to demonstrate compliance with a dose- or risk-based cleanup goal during final status survey. The MARSSIM methodology follows an integrated strategy combining direct measurements and sampling to assess uniform residual contamination, and scanning surveys to identify localized areas of elevated activity. Building surveys assessing residual surface contamination present unique challenges from reliance on field measurements that may be subject to less than ideal environmental conditions. Additionally, limited characterization and excessive conservatism incorporated into the conceptual site model may result in derived concentration guideline levels (DCGL) and, as a consequence of relatively inefficient field instrumentation, corresponding attributable count rates generally comparable to background. The presence of elevated ambient radon concentrations and associated progeny deposition both exacerbate these intrinsic survey challenges and introduce a series of technical, logistic, and administrative obstacles that can wreak havoc on the statistical legitimacy of each component of the MARSSIM process. Short-lived radon progeny including polonium-218, lead-214, bismuth-214, and polonium-214 produce excessive alpha and beta radiation that may decrease instrument sensitivity through an increase in background count rates, and, at worst, may result in incorrect survey unit failure through the simulation of residual surface activity. Aside from the obvious solution of exhaustive radon mitigation, the primary objectives are to distinguish between radon progeny deposition and actual surface activity, and collect sufficient defensible data. The following is a technical discussion of the consequences of performing building MARSSIM surveys in the presence of elevated ambient radon atmospheres, conceptual examples, and an overview of practical strategies to minimize the effects of radon interference while collecting sufficient defensible data to complete the survey. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Robust Measurement of Stellar Streams around the Milky Way: Correcting Spatially Variable Observational Selection Effects in Optical Imaging Surveys

Observations of density variations in stellar streams are a promising probe of low-mass dark matter substructure in the Milky Way. However, survey systematics such as variations in seeing and sky brightness can also induce artificial fluctuations in the observed densities of known stellar streams. These variations arise because survey conditions affect both object detection and star–galaxy misclassification rates. To mitigate these effects, we use Balrog synthetic source injections in the Dark Energy Survey (DES) Y3 data to calculate detection rate variations and classification rates as functions of survey properties. We show that these rates are nearly separable with respect to survey properties and can be estimated with sufficient statistics from the synthetic catalogs. Applying these corrections reduces the standard deviation of relative detection rates across the DES footprint by a factor of 5, and our corrections significantly change the inferred linear density of the Phoenix stream when including faint objects. Additionally, for artificial streams with DES-like survey properties we are able to recover density power spectra with reduced bias. We also find that uncorrected power-spectrum results for Legacy Survey of Space and Time (LSST)-like data can be around 5 times more biased, highlighting the need for such corrections in future ground-based surveys.

79 ASTRONOMY AND ASTROPHYSICS↗

Learning From User Behavior: A Survey-Assist Algorithm for Longitudinal Mobility Data Collection

GPS-based travel surveys are widely used in mobility studies to gather crucial qualitative data, like purpose, transportation mode and replaced mode. However, survey response still poses a burden to users, especially in long-term mobility studies, leading to response fatigue. We explore a survey-assist strategy to ease this burden by a novel, user-level modeling approach that leverages past responses from each user to predict responses for new trips, without relying on external data sources like GIS data. We investigate three main algorithms for predicting responses: (i) clustering trips and extrapolating responses for similar trips, (ii) using random forest classification, and (iii) clustering that uses a hybrid algorithm to determine spatial structure, which is then fed as input to a classic random forest classifier. The clustering approach can flexibly predict responses for even complex qualitative survey questions; it achieved F-scores of 65%. The random forest pipeline uses architecture that restricts it to predicting three predetermined survey questions: trip purpose, mode, and replaced mode. However, it achieved F-scores of 78%. While the survey-assist approach has been implemented by several proprietary systems, to our knowledge, this is the first exploration in the academic literature. It follows that this is also the first rigorous evaluation of multiple algorithms that can implement the approach. The evaluation uses a large scale, publicly available, longitudinal dataset consisting of ~ 92k trips from 235 users over a period of roughly one and a half years. With this approach, travel surveys can be pre-filled with the predicted responses for each trip, thus streamlining the survey process for users. Combined with an active learning system that requests user input on low-confidence predictions, models can be updated and improved over time to better support the long-term collection of longitudinal qualitative data.

clustering↗

Comparative analysis of atmospheric parameters from high-resolution spectroscopic sky surveys: APOGEE, GALAH, Gaia -ESO

SDSS-IV APOGEE-2, GALAH, and Gaia-ESO are high-resolution, ground-based, multi-object spectroscopic surveys providing fundamental stellar atmospheric parameters and multiple elemental abundance ratios for hundreds of thousands of stars of the Milky Way. Data from these and other surveys contribute to investigations of the history and evolution of the Galaxy. We undertake a comparison between the most recent data releases of these surveys to investigate the accuracy and precision of derived parameters by placing the abundances on an absolute scale. We also discuss the correlations in parameter and abundance differences as a function of main parameters. Uncovering the variants provides a basis to continue the efforts of future sky surveys. Quality samples from the APOGEE-GALAH (15 537 stars), APOGEE-GES (804 stars), and GALAH-GES (441 stars) overlapping catalogs were collected. We investigated the mean variants between the surveys, and linear trends were also investigated. We compared the slope of correlations and mean differences with the reported uncertainties. The average and scatter of v rad , T eff , log g, [M/H], and v micro , along with numerous species of elemental abundances in the combined catalogs, show that in general there is a good agreement between the surveys. We find large radial velocity scatters ranging from 1.3 km s -1 to 4.4 km s -1 when comparing the three surveys. We observe some weak trends (e.g., in ΔT eff vs. Δlog g for the APOGEE-GES stars) and a clear correlation in the v micro - Δv micro planes in the APOGEE-GALAH common sample. For [α/H], [Ti/H] (APOGEE-GALAH giants), and [Al/H] (APOGEE-GALAH dwarfs) potential strong correlations are discovered as a function of the differences in the main atmospheric parameters, and we also find weak trends for other elements. In general we find good agreement between the three surveys within their respective uncertainties. However, there are certain regimes in which strong variants exist, which we discuss. There are still offsets larger than 0.1 dex in the absolute abundance scales.

79 ASTRONOMY AND ASTROPHYSICS↗

The Tianlai dish array low-z surveys forecasts

Here, we present the science case for surveys with the Tianlai dish array interferometer tuned to the [1300, 1400] MHz frequency range. Starting from a realistic generation of mock visibility data according to the survey strategy, we reconstruct maps of the sky and perform foreground subtraction. We estimate the level of residuals from imperfect subtraction, mostly due to mode mixing, i.e. distortions in the reconstructed 3D maps due to frequency-dependent instrument response. We show that a survey of the North Celestial Polar cap during a year of observations, covering an area of 150 deg 2 , would reach a sensitivity of $1.5-2 mK per 1 MHz x 0.25 2 deg 2 voxel and be marginally impacted by mode mixing. Tianlai would be able to detect ~10 nearby massive H i clumps as well as a very strong cross-correlation signal of 21 cm intensity maps with the North Celestial Cap Survey optical galaxies. We also studied the performance of a mid-latitude survey, covering ~ 1500 deg 2 overlapping the SDSS footprint. Despite a higher noise level for the mid-latitude survey, as well as significant distortions due to mode mixing, Tianlai would be able to detect a highly significant cross-correlation between the 21 cm signal and the Sloan spectroscopic galaxy sample. Using the extragalactic signals measured from either or both of these surveys, and comparing them with simulations such as those presented here will make it possible to assess the impact of various instrumental imperfections on the Tianlai dish array performance. This would pave the way for future intensity mapping surveys with higher sensitivity.

79 ASTRONOMY AND ASTROPHYSICS↗

The miniJPAS survey quasar selection – II. Machine learning classification with photometric measurements and uncertainties

Astrophysical surveys rely heavily on the classification of sources as stars, galaxies, or quasars from multiband photometry. Surveys in narrow-band filters allow for greater discriminatory power, but the variety of different types and redshifts of the objects present a challenge to standard template-based methods. In this work, which is part of a larger effort that aims at building a catalogue of quasars from the miniJPAS survey, we present a machine learning-based method that employs convolutional neural networks (CNNs) to classify point-like sources including the information in the measurement errors. We validate our methods using data from the miniJPAS survey, a proof-of-concept project of the Javalambre Physics of the Accelerating Universe Astrophysical Survey (J-PAS) collaboration covering ∼1 deg 2 of the northern sky using the 56 narrow-band filters of the J-PAS survey. Due to the scarcity of real data, we trained our algorithms using mocks that were purpose-built to reproduce the distributions of different types of objects that we expect to find in the miniJPAS survey, as well as the properties of the real observations in terms of signal and noise. We compare the performance of the CNNs with other well-established machine learning classification methods based on decision trees, finding that the CNNs improve the classification when the measurement errors are provided as inputs. The predicted distribution of objects in miniJPAS is consistent with the putative luminosity functions of stars, quasars, and unresolved galaxies. Our results are a proof of concept for the idea that the J-PAS survey will be able to detect unprecedented numbers of quasars with high confidence.

79 ASTRONOMY AND ASTROPHYSICS↗

LISA Galactic binaries in the Roman Galactic Bulge Time-Domain Survey

ABSTRACT Short-period Galactic white dwarf binaries detectable by Laser Interferometer Space Antenna are the only guaranteed persistent sources for multimessenger gravitational-wave astronomy. Large-scale surveys in the 2020s present an opportunity to conduct preparatory science campaigns to maximize the science yield from future multimessenger targets. The Nancy Grace Roman Space Telescope Galactic Bulge Time-Domain Survey will (in its Reference Survey design) image seven fields in the Galactic Bulge approximately 40 000 times each. Although the Reference Survey cadence is optimized for detecting exoplanets via microlensing, it is also capable of detecting short-period white dwarf binaries. In this paper, we present forecasts for the number of detached short-period binaries the Roman Galactic Bulge Time-Domain Survey will discover and the implications for the design of electromagnetic surveys. Although population models are highly uncertain, we find a high probability that the baseline survey will detect of the order of ∼5 detached white dwarf binaries. The Reference Survey would also have a ${\gtrsim} 20\,{\rm per\,cent}$ chance of detecting several known benchmark white dwarf binaries at the distance of the Galactic Bulge.

Astronomy & Astrophysics↗