Observer Bias Research Articles

Reducing Interference Bias in Online Marketplace Experiments Using Cluster Randomization: Evidence from a Pricing Meta-experiment on Airbnb

Online marketplace designers frequently run randomized experiments to measure the impact of proposed product changes. However, given that marketplaces are inherently connected, total average treatment effect (TATE) estimates obtained through individual-level randomized experiments may be biased because of violations of the stable unit treatment value assumption, a phenomenon we refer to as “interference bias.” Cluster randomization (i.e., the practice of randomizing treatment assignment at the level of “clusters” of similar individuals) is an established experiment design technique for countering interference bias in social networks, but it is unclear ex ante if it will be effective in marketplace settings. In this paper, we use a meta-experiment or “experiment over experiments” conducted on Airbnb to both provide empirical evidence of interference bias in online marketplace settings and assess the viability of cluster randomization as a tool for reducing interference bias in marketplace TATE estimates. Results from our meta-experiment indicate that at least 20% of the TATE estimate produced by an individual-level randomized evaluation of the platform fee increase we study is attributable to interference bias and eliminated through the use of cluster randomization. We also find suggestive, nonstatistically significant evidence that interference bias in seller-side experiments is more severe in demand-constrained geographies and that the efficacy of cluster randomization at reducing interference bias increases with cluster quality. This paper was accepted by Chris Forman, information systems. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2020.01157 .

Evaluation of Wintertime Precipitation Estimates and Forecasts in the Mountains of Colorado

Abstract Wintertime precipitation poses many observational and forecasting challenges, especially in the complex topography of the western United States where radar beam blockage and difficulty siting in situ observations yields more sparse observations than in the eastern United States. Uncertainty in western U.S. winter precipitation is known to be high, so much so that some studies have found model simulated precipitation to produce similar or better large-scale estimates of annual precipitation than gridded observational products during climatologically anomalous years. This study evaluates high-resolution gridded precipitation estimates from Multi-Radar Multi-Sensor (MRMS) and Stage IV as well as forecasts from NOAA’s High-Resolution Rapid Refresh (HRRR) model in the Colorado Rocky Mountains. Gridded precipitation estimates and forecasts are compared with in situ SNOTEL measurements for two seasons of wintertime precipitation. The influence of forecast length, lead time, and model elevation on seasonal precipitation predictions from the HRRR are investigated. Additional comparisons are made with the relatively dense network of observations deployed in Colorado’s East River Watershed during the Study of Precipitation, the Lower Atmosphere and Surface for Hydrometeorology (SPLASH) campaign. Gridded products and forecasts are found to underestimate cold-season precipitation by 25%–65% relative to in situ and aircraft measurements, with longer forecast periods and lead times (6–24 h) having smaller biases (25%–30%) than shorter forecast periods and lead times (55%–65%). The assessment of multiple years of observations indicates that these biases are related more to the data and methods used to create the gridded products and forecasts than to precipitation characteristics. Significance Statement In the mountainous western United States, it is very challenging to both observe and forecast wintertime precipitation, yet snowfall plays an important role in providing the region’s annual water supply. This study aims to increase our understanding of the biases in observations and forecasts of snowfall in the Colorado Rocky Mountains, which can in turn impact forecasts of water availability for the ensuing warm season. In this study we find high-resolution gridded precipitation estimates and forecasts to underestimate cold-season precipitation when compared with in situ observing stations, with longer-range forecasts (e.g., daily) being the least biased. These findings were consistent over two years of study and have broad implications for the hydrologic modeling and water management communities.

Large deformation diffeomorphic mapping of 3D shape variation reveals two distinct mandible and head capsule morphs in Atta vollenweideri leaf-cutter worker ants.

Ants are crucial ecosystem engineers, and their ecological success is facilitated by a division of labour among sterile "workers". In some ant lineages, workers have undergone further morphological differentiation, resulting in differences in body size, shape, or both. Distinguishing between changes in size and shape is not trivial. Traditional approaches based on allometry reduce complex 3D shapes into simple linear, areal, or volume metrics; modern approaches using geometric morphometrics typically rely on landmarks, introducing observer bias and a trade-off between effort and accuracy. Here, we use a landmark-free method based on large deformation diffeomorphic metric mapping (LDDMM) to assess the co-variation of size and 3D shape in the mandibles and head capsules of Atta vollenweideri leaf-cutter ants, a species exhibiting extreme worker size-variation. Body mass varied by more than two orders of magnitude, but a shape atlas created via LDDMM on μ-CT-derived 3D mesh files revealed only two distinct head capsule and mandibles shapes-one for the minims (body mass < 1 mg) and one for all other workers. We discuss the functional significance of the identified 3D shape variation, and its implications for the evolution of extreme polymorphism in Atta.

Comparative Effectiveness of Partial Gland Cryoablation Versus Robotic Radical Prostatectomy for Cancer Control

Background and objectiveThere is an absence of high-level evidence comparing oncologic endpoints for partial gland ablation, and most series used prostate-specific antigen (PSA) rather than biopsy endpoints. Our aim was to compare oncologic outcomes between partial gland cryoablation (PGC) and radical prostatectomy (RP) for prostate cancer. MethodsThis was a retrospective, single-center analysis of subjects treated with PGC (n = 98) or RP (n = 536) between January 2017 and December 2022 as primary treatment for intermediate-risk (Gleason grade group [GG] 2–3) prostate cancer. Oncologic endpoints included protocol surveillance biopsies after PGC in comparison to serial PSA testing after RP. The primary outcome was treatment failure, defined as a need for any salvage treatment or development of metastatic disease. Treatment failure and survival analyses were conducted using Cox proportional-hazard regression and Kaplan Meier survival curves. Key findings and limitationsAfter applying the inclusion/exclusion criteria, the PGC (n = 75) and RP (n = 298) groups were compared. PGC patients were significantly older (71 vs 64 yr; p < 0.001) but there were no differences in PSA, biopsy GG, or treatment year between the groups. The PGC group had higher rates of treatment failures at 24 mo (33% vs 11%; p < 0.001) and 48 mo (43% vs 14%; p < 0.001). One PGC patient (2.1%) and one RP patient (0.7%) had developed metastases by 48-mo follow-up (p = 0.4). On adjusted analysis, PGC was associated with a higher risk of treatment failure (hazard ratio 4.6, 95% confidence interval 2.7–7.9; p < 0.001). Limitations include observational biases associated with the retrospective study design. ConclusionsThis is the first comparative effectiveness study of cancer control outcomes for PGC versus RP. The results demonstrate an almost fivefold higher risk of treatment failure with PGC during short-term follow-up. Patient summaryWe compared cancer control outcomes for patients with intermediate-risk prostate cancer treated with partial prostate gland cryoablation versus radical prostatectomy. We found that partial gland cryoablation had an almost fivefold higher risk of treatment failure. Men with prostate cancer should be counseled regarding this difference in treatment failure.

Single arketamine in treatment resistant depression: Presentation of 3 cases with regard to sick-leave duration

BackgroundKetamine is the prototypal rapid-acting antidepressant (RAAD) for TRD with approved indication for esketamine-nasal spray (ESK-NS). Distinctly, arketamine (R-KET) demonstrates enhanced antidepressant effects and neuroplasticity changes compared to esketamine, with fewer dissociative side effects and abuse potential. This study focuses on R-ketamine's therapeutic implications, safety, and tolerability in TRD treatment. MethodsWe report on a year-long follow-up of three TRD patients post-single IV R-KET administration. The study, conducted under the clinical trial PCN-101 (NCT05414422), observed these subjects for healthcare resource utilization and social support system impact. Participants, adults diagnosed with recurrent major depressive disorder without psychotic features, were observed in a year-long follow-up period for safety. ResultsCase analyses revealed significant symptom reduction and improved social and vocational functioning, with reduced sick leaves and hospitalizations post-treatment. However, one case developed a substance use disorder, underscoring the need for vigilant monitoring. The study highlights R-KET's transformative potential in managing depression, indicating a shift in TRD treatment strategies towards early, aggressive interventions. ConclusionsDespite promising findings, the study faces limitations due to its small sample size, lack of randomization, and potential observational design biases. The results, while aligning with existing ketamine research, require cautious interpretation and warrant further investigation with larger, more robust studies. This exploration of R-KET's role in home-based TRD treatment opens avenues for future research, particularly focusing on its long-term effectiveness and safety in diverse patient populations. This study is registered in clinicaltrials.gov: NCT06232291.

Abstract 4928: REFINE Method: Novel strategy for signal enhancement

Abstract Fragmentomics has emerged as a powerful tool in the detection of early cancer malignancies. One of the challenges for fragmentomics is the low signal to noise ratio in early stage cancer samples. Fragmentomic sequencing data is also inherently noisy and susceptible to batch effects. Common strategies to address these challenges include using normalization methods, such as frequency or Z-score normalization, to scale data. However, these existing methods struggle to differentiate true signals from noise and are susceptible to experimental bias. To address the need for a strategy that removes noise and batch effects, Genece Health is employing a novel normalization technique, the REFINE method, to improve the results of fragmentomic cancer detection. Whole genome sequencing (Illumina) fragmentomic data were generated at an average 2.5x coverage from 3,669 normal and 1,346 cancer samples. Six different cancer types were represented: colorectal, esophageal, liver, lung, ovarian, and pancreatic. Fragment end-motifs and fragment sizes (FEMS) data were then extracted from the sequence data. REFINE first establishes a baseline FEMS signal from a representative and random panel of normals (PoN) comprised of 100 healthy samples. This baseline, which represents the normal background FEMS noise in healthy donors, is subsequently decomposed using truncated singular value decomposition. Next, REFINE then eliminates this decomposed baseline FEMS signal from the raw FEMS data in all other samples using linear regression. A convolutional neural network (CNN) was trained (2,206 normal and 1,000 cancer samples) using frequency normalized data versus REFINE normalized data using 5-fold cross validation. The performances of both models were then assessed using the remaining 1,463 normal with 346 cancer samples. With a training dataset specificity threshold of 90%, REFINE significantly improves the sensitivity performance of the CNN model &amp;gt;30% (from 50.4% to 83.0%). REFINE also improves the auROC from 0.753 to 0.944. We also show that increasing the size of the PoN to 812 samples and including hyper-parameter optimizations further improves the sensitivity to 85.9% (with an auROC of 0.955). The REFINE method demonstrates a dramatic enhancement in the detection of ctDNA from fragmentomic data, overcoming the limitations of conventional normalization techniques. REFINE effectively enhances the signal to noise ratio, facilitating more accurate cancer detection. Future work will focus on validating the REFINE method across diverse datasets and exploring its application in other areas of oncology. Citation Format: Mengchi Wang, Jin Mo Ahn, Junnam Lee, Dasom Kim, Eun-Hae Cho, Byung In Lee, Andrew Carson. REFINE Method: Novel strategy for signal enhancement [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4928.

Estimating Total Length of Partially Submerged Crocodylians from Drone Imagery

Understanding the demographic structure is vital for wildlife research and conservation. For crocodylians, accurately estimating total length and demographic class usually necessitates close observation or capture, often of partially immersed individuals, leading to potential imprecision and risk. Drone technology offers a bias-free, safer alternative for classification. We evaluated the effectiveness of drone photos combined with head length allometric relationships to estimate total length, and propose a standardized method for drone-based crocodylian demographic classification. We evaluated error sources related to drone flight parameters using standardized targets. An allometric framework correlating head to total length for 17 crocodylian species was developed, incorporating confidence intervals to account for imprecision sources (e.g., allometric accuracy, head inclination, observer bias, terrain variability). This method was applied to wild crocodylians through drone photography. Target measurements from drone imagery, across various resolutions and sizes, were consistent with their actual dimensions. Terrain effects were less impactful than Ground-Sample Distance (GSD) errors from photogrammetric software. The allometric framework predicted lengths within ≃11–18% accuracy across species, with natural allometric variation among individuals explaining much of this range. Compared to traditional methods that can be subjective and risky, our drone-based approach is objective, efficient, fast, cheap, non-invasive, and safe. Nonetheless, further refinements are needed to extend survey times and better include smaller size classes.

Analysis of protected species observer data: Strengths, weaknesses, and application in the assessment of marine mammal responses to seismic surveys in the northern Gulf of Mexico 2002-2015.

Visual observation data collected by protected species observers (PSOs) is required per regulations stipulated in Notices to Lessees (NTLs) and geophysical survey Permits (Form BOEM-0328) issued to seismic operators in the Gulf of Mexico (GOM). Here, data collected by certified and trained PSOs during seismic surveys conducted between 2002-2015 were compiled and analyzed to assess utility in assessing marine mammal responses to seismic noise and effectiveness of required mitigation measures. A total of 3,886 agency-required bi-weekly PSO Effort and Sightings reports were analyzed comprising 598,319 hours of PSO visual effort and 15,117 visual sighting records of marine mammals. The observed closest point of approach (CPA) distance was statistically compared across five species groupings for four airgun activity levels (full, minimum source, ramp up, silent). Whale and dolphin detections were significantly farther from airgun array locations during full power operations versus silence, indicating some avoidance response to full-power operations. Dolphin CPA distances were also significantly farther from airguns operating at minimum source than silence. Blackfish were observed significantly farther from the airgun array during ramp up versus both full and minimum source activities. Blackfish were observed significantly closer to the airgun array during silent activities versus at full, minimum source, and ramp up activities. Beaked whales had the largest mean CPA for detection distance compared to all other species groups. Detection distances for beaked whales were not significantly differences between full and silent operations; however, the sample size was very low. Overall results are consistent with other studies indicating that marine mammals may avoid exposure to airgun sounds based on observed distance from the seismic source during specified source activities. There was geographic variability in sighting rates associated with specific areas of interest within the GOM. This study demonstrates that agency required PSO reports provide a robust and useful data set applicable to impact assessments; management, policy and regulatory decision making; and qualitative input for regional scientific, stock assessment and abundance studies. However, several improvements in content and consistency would facilitate finer-scale analysis of some topics (e.g., effort associated with specific activities, observer biases, sound field estimation) and support statistical comparisons that could provide further insight into marine mammal responses and mitigation efficacy.

Comparison of consistency in centiloid scale among different analytical methods in amyloid PET: the CapAIBL, VIZCalc, and Amyquant methods.

The Centiloid (CL) scale is a standardized measure for quantifying amyloid deposition in amyloid positron emission tomography (PET) imaging. We aimed to assess the agreement among 3 CL calculation methods: CapAIBL, VIZCalc, and Amyquant. This study included 192 participants (mean age: 71.5years, range: 50-87years), comprising 55 with Alzheimer's disease, 65 with mild cognitive impairment, 13 with non-Alzheimer's dementia, and 59 cognitively normal participants. All the participants were assessed using the three CL calculation methods. Spearman's rank correlation, linear regression, Friedman tests, Wilcoxon signed-rank tests, and Bland-Altman analysis were employed to assess data correlations, linear associations, method differences, and systematic bias, respectively. Strong correlations (rho = 0.99, p < .001) were observed among the CL values calculated using the three methods. Scatter plots and regression lines visually confirmed these strong correlations and met the validation criteria. Despite the robust correlations, a significant difference in CL value between CapAIBL and Amyquant was observed (36.1 ± 39.7 vs. 34.9 ± 39.4; p < .001). In contrast, no significant differences were found between CapAIBL and VIZCalc or between VIZCalc and Amyquant. The Bland-Altman analysis showed no observable systematic bias between the methods. The study demonstrated strong agreement among the three methods for calculating CL values. Despite minor variations in the absolute values of the Centiloid scores obtained using these methods, the overall agreement suggests that they are interchangeable.

Quantifying exposure biases in early instrumental land surface air temperature observations

AbstractExposure biases are a pervasive non‐climatic change in land air temperature records which have been introduced as a result of changes in the way thermometers were sheltered from solar radiation and the elements over time. Exposure biases have not been widely accounted for in observational records, due to difficulties detecting/correcting the bias using traditional homogenisation techniques; therefore, exposure biases still contribute significant uncertainty to the early period in global temperature compilations. Here, an empirical approach to address the bias arising from the introduction of Stevenson‐type screens from the late‐19th century is presented. The approach consists of: (1) an empirical analysis of 54 parallel measurement series to identify the characteristics of the exposure bias in four exposure classes; (2) the development of bias‐estimation models based on an analysis of which variables influence the bias; and (3) the application of the models to an extended version of CRUTEM5 (CRUTEM5_ext), based on exposure metadata, to quantify and reduce the bias. Step one identified differences between the temperatures recorded in Stevenson screens and early exposures, which vary seasonally, diurnally, and with location and exposure class. The largest biases (in mean temperatures) were found in freestanding exposures (up to −0.78°C annually) and in summer, while the smallest biases were generally found in wall‐mounted exposures (near‐0°C annually) and in winter. Significant relationships between the bias and temperature, downward top of atmosphere and/or received shortwave downward solar radiation were found in each exposure class and led to the development of three regression‐based bias‐estimation models. Application of these models to 1,960 mid‐latitude stations in CRUTEM5_ext, resulted in small (≤0.016°C) positive adjustments to the Northern Hemisphere mid‐latitude mean before 1880, and larger (≤−0.1°C) negative adjustments to the Northern and Southern Hemisphere mid‐latitude means between 1882–1934 and 1856–1900, respectively. Larger adjustments were estimated regionally: up to −0.57°C annually and −0.79°C seasonally in individual grid cells.

Beyond crippling bias: Carcass‐location bias in roadkill studies

AbstractWildlife roadkill studies need to cope with a mismatch among recorded carcasses and actual road mortality, because of the existence of three biases: crippling, carcass‐persistence, and observer bias. Here, we focused on the often overlooked crippling bias, suggesting that it should be called carcass‐location bias and disentangling the related three possible outcomes for affected wildlife: injured animal escaping and dying away from road, animal rebounding off the road after vehicle collision, and animal retained by vehicle. Such outcomes can probably be affected by different species traits, and, in order to make a first evaluation of this hypothesis, we opportunistically collected 150 direct observations on the ultimate fate of roadkilled vertebrates. Approximately one third of them were affected by carcass‐location bias, so extremely difficult to be recorded through typical roadkill surveys, entailing a considerable and overlooked source of error for roadkill studies and mitigation actions based on them.

The impact of AGN X-ray selection on the AGN halo occupation distribution

The connection between active galactic nuclei (AGN) and their host dark matter halos provides powerful insights into how supermassive black holes (SMBHs) grow and coevolve with their host galaxies. Here we investigate the impact of observational AGN selection on the AGN halo occupation distribution (HOD) by forward-modeling AGN activity into cosmological N-body simulations. By assuming straightforward relationships between the SMBH mass, galaxy mass, and (sub)halo mass, as well as a uniform broken power law distribution of Eddington ratios, we find that luminosity-limited AGN samples result in biased HOD shapes. While AGN defined by an Eddington ratio threshold produce AGN fractions that are flat across halo mass (unbiased by definition), luminosity-limited AGN fractions peak around galaxy-group-sized halo masses and then decrease with increasing halo mass. With higher luminosities, the rise of the AGN fraction starts at higher halo masses, the peak is shifted towards higher halo masses, and the decline at higher halo masses is more rapid. These results are consistent with recent HOD constraints from AGN clustering measurements, which find (1) characteristic halo mass scales of $ Vir $h^ M_ odot and (2) a shallower rise of the number of satellite AGN with increasing halo mass than for the overall galaxy population. Thus the observational biases due to AGN selection can naturally explain the constant, characteristic halo mass scale inferred from large-scale AGN clustering amplitudes over a range of redshifts, as well as the measured inconsistencies between AGN and galaxy HODs. We conclude that AGN selection biases can have significant impacts on the inferred AGN HOD, and can therefore lead to possible misinterpretations of how AGN populate dark matter halos and the AGN-host galaxy connection.

Cognitive bias modification for perfectionism and intolerance of uncertainty: A randomized controlled trial.

This study investigated the efficacy of combined cognitive bias modification (CBM) on perfectionism and intolerance of uncertainty. Fifty-four university students scoring over 70.5 on the Brief Symptom Measure were randomly assigned to experimental and placebo control groups. The CBM intervention was administered online for 4 weeks. Assessments were given at baseline, after the 4-week intervention, and 1 month post-intervention. Results showed a statistically significant decrease in two dimensions of perfectionism, concern over mistakes and parental criticism, and intolerance of uncertainty of those in the experimental group, compared to those in the control group. The findings related to the interpretation of perfectionism revealed a significant interaction effect of time and direction of sentences for the experimental group. Lastly, the experimental group's interpretation bias scores for intolerance of uncertainty showed a statistically significant increase after the intervention compared to those in the control group. The study's findings provide preliminary support for the effectiveness of CBM on perfectionism and intolerance of uncertainty.

Perceptual decoupling or trigger happiness: the effect of response delays and shorter presentation times on a go-no-go task with a high go prevalence.

In the current investigation, we modified the high Go, low No-Go Sustained Attention to Response Task (SART). Some researchers argue a commission error, an inappropriate response to a No-Go stimulus, in the SART is due to the participant being inattentive, or perceptually decoupled, during stimulus onset. Response delays in the SART reduce commission errors. A response delay may therefore enable a participant who is initially inattentive to recouple their attention in time to appropriately perceive the stimulus and withhold a response to a No-Go stimulus. However, shortening stimulus display duration in the SART should limit the possibility of the participant identifying the stimulus later, if they are initially not attending the stimulus. A response delay should not reduce commission errors if stimulus duration is kept to the minimum duration enabling stimulus recognition. In two experiments, we shortened stimulus onset to offset duration and added response delays of varying lengths. In both experiments, even when stimulus duration was shortened, response delays notably reduced commission errors if the delay was greater than 250ms. In addition, using the Signal Detection Theory perspective in which errors of commission in the SART are due to a lenient response bias-trigger happiness, we predicted that response delays would result in a shift to a more conservative response bias in both experiments. These predictions were verified. The errors of commission in the SART may not be a measures of conscious awareness per se, but instead indicative of the level of participant trigger happiness-a lenient response bias.

Fires in the deep: The luminosity distribution of early-time gamma-ray-burst afterglows in light of the Gamow Explorer sensitivity requirements

Gamma-ray bursts (GRBs) are ideal probes of the Universe at high redshift ($z$), pinpointing the locations of the earliest star-forming galaxies and providing bright backlights with simple featureless power-law spectra that can be used to spectrally fingerprint the intergalactic medium and host galaxy during the period of reionization. Future missions such as Gamow Explorer (hereafter )\ are being proposed to unlock this potential by increasing the rate of identification of high-$z$ ($z&gt;5$) GRBs in order to rapidly trigger observations from $6-10$ m ground telescopes, the James Webb Space Telescope (JWST), and the upcoming Extremely Large Telescopes (ELTs). was proposed to the NASA 2021 Medium-Class Explorer (MIDEX) program as a fast-slewing satellite featuring a wide-field lobster-eye X-ray telescope (LEXT) to detect and localize GRBs with arcminute accuracy, and a narrow-field multi-channel photo-$z$ infrared telescope (PIRT) to measure their photometric redshifts for $&gt;80&lt;!PCT!&gt;$ of the LEXT detections using the Lyman-alpha dropout technique. We use a large sample of observed GRB afterglows to derive the PIRT sensitivity requirement. We compiled a complete sample of GRB optical--near-infrared (optical--NIR) afterglows from 2008 to 2021, adding a total of 66 new afterglows to our earlier sample, including all known high-$z$ GRB afterglows. This sample is expanded with over 2837 unpublished data points for 40 of these GRBs. We performed full light-curve and spectral-energy-distribution analyses of these afterglows to derive their true luminosity at very early times. We compared the high-$z$ sample to the comparison sample at lower redshifts. For all the light curves, where possible, we determined the brightness at the time of the initial finding chart of at different high redshifts and in different NIR bands. This was validated using a theoretical approach to predicting the afterglow brightness. We then followed the evolution of the luminosity to predict requirements for ground- and space-based follow-up. Finally, we discuss the potential biases between known GRB afterglow samples and those to be detected by We find that the luminosity distribution of high-$z$ GRB afterglows is comparable to those at lower redshift, and we therefore are able to use the afterglows of lower-$z$ GRBs as proxies for those at high $z$. We find that a PIRT sensitivity of $15\ (21 mag AB) in a 500 s exposure simultaneously in five NIR bands within 1000s of the GRB trigger will meet the mission requirements. Depending on the $z$ and NIR band, we find that between 75&lt;!PCT!&gt; and 85&lt;!PCT!&gt; of all afterglows at $z&gt;5$ will be recovered by at $5 detection significance, allowing the determination of a robust photo-$z$. As a check for possible observational biases and selection effects, we compared the results with those obtained through population-synthesis models, and find them to be consistent. and other high-$z$ GRB missions will be capable of using a relatively modest 0.3m onboard NIR photo-$z$ telescope to rapidly identify and report high-$z$ GRBs for further follow-up by larger facilities, opening a new window onto the era of reionization and the high-redshift Universe.

Complete Selective Switching of Ferroelastic Domain Stripes in Multiferroic Thin Films by Tip Scanning

AbstractBiFeO3 is a rare single‐phase multiferroic which shows coupled ferroelectricity, ferroelasticity and antiferromagnetism at room temperature. Many fantastic properties of BiFeO3 are regulated by its domain structure. While (001) rhombohedral BiFeO3 thin films can possibly develop up‐to eight degenerate states of two‐variant 71° ferroelastic domain stripes, complete selective control of all possible switching paths between these states has not yet been achieved. Here, combining phase field simulations and scanning probe microscopy experiments, we demonstrated such a complete selective control. The tip bias and the built‐in field were shown to affect the volume fraction of the 71°, 109° and 180° switched domains in rhombohedral BiFeO3 thin films under single‐point loading. Meanwhile, tip scanning further broke the symmetry of the nucleated domains, leading to different switched domain patterns. We then grew BiFeO3 thin films with a specific two‐variant 71° ferroelastic domain state and showed that such a state could be deterministically switched into the other seven two‐variant domain states by collaborative controlling tip scanning and tip bias in scanning probe microscopy experiments. These results should deepen our current understanding on the domain switching kinetics in BiFeO3 thin films and indicate they are promising platforms for developing configurable electronic and spintronic devices.

A comparative study of the fundamental metallicity relation

Aims. We aim to investigate the influences on the evolution of the fundamental metallicity relation of different selection criteria. Methods. We used 5 487 star-forming galaxies at a median redshift of z ≈ 0.63 extracted from the VIMOS Public Extragalactic Redshift Survey (VIPERS) and 143 774 comparison galaxies in the local Universe from the GALEX-SDSS-WISE Legacy Catalog. We employed two families of methods: parametric and nonparametric. In the parametric approaches, we compared the fundamental metallicity relation projections plagued by observational biases on differently constructed control samples at various redshifts. Then, we compare the metallicity difference between different redshifts in stellar mass-star formation rate bins. In the nonparametric approach, we related the metallicity and the normalized specific star formation rate (sSFR). To compare galaxies with the same physical properties, we normalized the sSFR of our samples according to the median value at median redshift z ≈ 0.09. Then, the galaxies with the same distance from the star-forming main sequence at their respective redshifts were compared when the sSFR is normalized according to the expected values from their respective star-forming main sequences. Results. The methodologies implemented to construct fair, complete samples for studying the mass-metallicity relation and the fundamental metallicity relation produced consistent results showing a small but still statistically significant evolution of both relations up to z ≈ 0.63. In particular, we observed a systematic trend where the median metallicity of the sample at z = 0.63 is lower than that of the local sample at the same stellar mass and star formation rate. The average difference in the metallicity of the low and intermediate redshifts is approximately 1.8 times the metallicity standard deviation of the median of the intermediate redshift sample in stellar mass-star formation rate bins. We confirmed this result using the Kolmogorov-Smirnov test. When we applied the stellar mass-completeness criterion to catalogs, the metallicity difference in redshifts decreased to approximately 0.96 times the metallicity standard deviation of the median, and thus it was not statistically significant. The limited area of the FMR surface explored once the stellar mass-completeness criterion is applied might dominate this difference reduction, leaving out the area where the difference between the two samples is the highest. A careful reading of the results and their underlying selection criteria is crucial in studies of the mass-metallicity and fundamental metallicity relations. Conclusions. When studying the mass-metallicity and fundamental metallicity relations, we recommend using the nonparametric approach, which provides similar results compared to parametric prescriptions, is easier to use, and is clear to interpret. The nonparametric methodology provides a convenient way to compare physical properties, with a smaller impact on observational selection biases.

Predicting the Dominant Formation Mechanism of Multiplanetary Systems

Most, if not all, Sun-like stars host one or more planets, making multiplanetary systems commonplace in our Galaxy. We utilize hundreds of multiplanet simulations to explore the origin of such systems, focusing on their orbital architecture. The first set of simulations assumes in situ assembly of planetary embryos, while the second explores planetary migration. After applying observational biases to the simulations, we compare them to 250+ observed multiplanetary systems, including 13 systems with planets in the habitable zone. For all of the systems, we calculate two of the so-called statistical measures: the mass concentration (S c ) and orbital spacing (S s ). After analytic and empirical analyses, we find that the measures are related to first order with a power law: Sc∼Ssβ . The in situ systems exhibit steeper power-law relations relative to the migration systems. We show that different formation scenarios cover different regions in the S s –S c diagram with some overlap. Furthermore, we discover that observed systems with S s < 30 are likely dominated by the migration scenario, while those with S s ≥ 30 are likely dominated by the in situ scenario. We apply these criteria to determine that a majority (62%) of observed multiplanetary systems formed via migration, whereas most systems with currently observed habitable planets formed via in situ assembly. This work provides methods of leveraging the statistical measures (S s and S c ) to disentangle the formation history of observed multiplanetary systems based on their present-day architectures.

The DECam Ecliptic Exploration Project (DEEP). III. Survey Characterization and Simulation Methods

We present a detailed study of the observational biases of the DECam Ecliptic Exploration Project’s B1 data release and survey simulation software that enables direct statistical comparisons between models and our data. We inject a synthetic population of objects into the images, and then subsequently recover them in the same processing as our real detections. This enables us to characterize the survey’s completeness as a function of apparent magnitudes and on-sky rates of motion. We study the statistically optimal functional form for the magnitude, and develop a methodology that can estimate the magnitude and rate efficiencies for all survey’s pointing groups simultaneously. We have determined that our peak completeness is on average 80% in each pointing group, and our magnitude drops to 25% of this value at m 25 = 26.22. We describe the freely available survey simulation software and its methodology. We conclude by using it to infer that our effective search area for objects at 40 au is 14.8 deg2, and that our lack of dynamically cold distant objects means that there at most 8 × 103 objects with 60 < a < 80 au and absolute magnitudes H ≤ 8.

Uncovering drone intentions using control physics informed machine learning

Unmanned Autonomous Vehicle (UAV) or drones are increasingly used across diverse application areas. Uncooperative drones do not announce their identity/flight plans and can pose a potential risk to critical infrastructures. Understanding drone’s intention is important to assigning risk and executing countermeasures. Intentions are often intangible and unobservable, and a variety of tangible intention classes are often inferred as a proxy. However, inference of drone intention classes using observational data alone is inherently unreliable due to observational and learning bias. Here, we developed a control-physics informed machine learning (CPhy-ML) that can robustly infer across intention classes. The CPhy-ML couples the representation power of deep learning with the conservation laws of aerospace models to reduce bias and instability. The CPhy-ML achieves a 48.28% performance improvement over traditional trajectory prediction methods. The reward inference results outperforms conventional inverse reinforcement learning approaches, decreasing the root mean squared spectral norm error from 3.3747 to 0.3229.