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1. Offsets between X-Ray and Radio Components in X-Ray Jets: The AtlasX

   https://doi.org/10.3847/1538-4365/aca321  

   Reddy, Karthik ; Georganopoulos, Markos ; Meyer, Eileen T. ; Keenan, Mary ; Kollmann, Kassidy E. ( February 2023 , The Astrophysical Journal Supplement Series)

   The X-ray emission mechanism of powerful extragalactic jets—which has important implications for their environmental impacts—is poorly understood. The X-ray/radio positional offsets in the individual features of jets provide important clues. Extending previous work in Reddy et al., we present a detailed comparison between X-ray maps, deconvolved using the Low-count Image Reconstruction and Analysis tool, and radio maps of 164 components from 77 Chandra-detected X-ray jets. We detect 94 offsets (57%), with 58 new detections. In FR II–type jet knots, the X-rays peak and decay before the radio in about half the cases, disagreeing with the predictions of one-zone models. While a similar number of knots lack statistically significant offsets, we argue that projection and distance effects result in offsets below the detection level. Similar deprojected offsets imply that X-rays could be more compact than radio for most knots, and we qualitatively reproduce this finding with a “moving-knot” model. The bulk Lorentz factor (Γ) derived for knots under this model is consistent with previous radio-based estimates, suggesting that kiloparsec-scale jets are only mildly relativistic. An analysis of the X-ray/radio flux ratio distributions does not support the commonly invoked mechanism of X-ray production from inverse Compton scattering of the cosmic microwave background, but does show a marginally significant trend of declining flux ratio as a function of the distance from the core. Our results imply the need for multi-zone models to explain the X-ray emission from powerful jets. We provide an interactive list of our X-ray jet sample athttp://astro.umbc.edu/Atlas-X.

    

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2. An Extensive Hubble Space Telescope Study of the Offset and Host Light Distributions of Type I Superluminous Supernovae

   https://doi.org/10.3847/1538-4357/ad12be  

   Hsu, Brian ; Blanchard, Peter K. ; Berger, Edo ; Gomez, Sebastian ( January 2024 , The Astrophysical Journal)

   We present an extensive Hubble Space Telescope rest-frame UV imaging study of the locations of Type I superluminous supernovae (SLSNe) within their host galaxies. The sample includes 65 SLSNe with detected host galaxies in the redshift rangez≈ 0.05–2. Using precise astrometric matching with SN images, we determine the distributions of the physical and host-normalized offsets relative to the host centers, as well as the fractional flux distribution relative to the underlying UV light distributions. We find that the host-normalized offsets of SLSNe roughly track an exponential disk profile, but exhibit an overabundance of sources with large offsets of 1.5–4 times their hosts' half-light radii. The SLSNe normalized offsets are systematically larger than those of long gamma-ray bursts (LGRBs), and even Type Ib/c and Type II SNe. Furthermore, we find from a Monte Carlo procedure that about${37}_{-8}^{+6}\%$of SLSNe occur in the dimmest regions of their host galaxies, with a median fractional flux value of 0.16, in stark contrast to LGRBs and Type Ib/c and Type II SNe. We do not detect any significant trends in the locations of SLSNe as a function of redshift, or as a function of explosion and magnetar engine parameters inferred from modeling of their optical light curves. The significant difference in SLSN locations compared to LGRBs (and normal core-collapse SNe) suggests that at least some of their progenitors follow a different evolutionary path. We speculate that SLSNe arise from massive runaway stars from disrupted binary systems, with velocities of ∼10²km s⁻¹.

    

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3. An Improved Ensemble of Land Surface Air Temperatures Since 1880 Using Revised Pair-Wise Homogenization Algorithms Accounting for Autocorrelation

   https://doi.org/10.1175/JCLI-D-23-0338.1  

   Chan, Duo ; Gebbie, Geoffrey ; Huybers, Peter ( April 2024 , Journal of Climate)

   Land surface air temperatures (LSAT) inferred from weather station data differ among major research groups. The estimate by NOAA’s monthly Global Historical Climatology Network (GHCNm) averages 0.02°C cooler between 1880 and 1940 than Berkeley Earth’s and 0.14°C cooler than the Climate Research Unit estimates. Such systematic offsets can arise from differences in how poorly documented changes in measurement characteristics are detected and adjusted. Building upon an existing pairwise homogenization algorithm used in generating the fourth version of NOAA’s GHCNm(V4), PHA₀, we propose two revisions to account for autocorrelation in climate variables. One version, PHA₁, makes minimal modification to PHA₀by extending the threshold used in breakpoint detection to be a function of LSAT autocorrelation. The other version, PHA₂, uses penalized likelihood to detect breakpoints through optimizing a model-selection problem globally. To facilitate efficient optimization for series with more than 1000 time steps, a multiparent genetic algorithm is proposed for PHA₂. Tests on synthetic data generated by adding breakpoints to CMIP6 simulations and realizations from a Gaussian process indicate that PHA₁and PHA₂both similarly outperform PHA₀in recovering accurate climatic trends. Applied to unhomogenized GHCNmV4, both revised algorithms detect breakpoints that correspond with available station metadata. Uncertainties are estimated by perturbing algorithmic parameters, and an ensemble is constructed by pooling 50 PHA₁- and 50 PHA₂-based members. The continental-mean warming in this new ensemble is consistent with that of Berkeley Earth, despite using different homogenization approaches. Relative to unhomogenized data, our homogenization increases the 1880–2022 trend by 0.16 [0.12, 0.19]°C century⁻¹(95% confidence interval), leading to continental-mean warming of 1.65 [1.62, 1.69]°C over 2010–22 relative to 1880–1900.

   Accurately correcting for systematic errors in observational records of land surface air temperature (LSAT) is critical for quantifying historical warming. Existing LSAT estimates are subject to systematic offsets associated with processes including changes in instrumentation and station movement. This study improves a pairwise homogenization algorithm by accounting for the fact that climate signals are correlated over time. The revised algorithms outperform the original in identifying discontinuities and recovering accurate warming trends. Applied to monthly station temperatures, the revised algorithms adjust trends in continental mean LSAT since the 1880s to be 0.16°C century⁻¹greater relative to raw data. Our estimate is most consistent with that from Berkeley Earth and indicates lesser and greater warming than estimates from NOAA and the Met Office, respectively.

    

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4. Hotter than Expected: Hubble Space Telescope (HST)/WFC3 Phase-resolved Spectroscopy of a Rare Irradiated Brown Dwarf with Strong Internal Heat Flux

   https://doi.org/10.3847/1538-4357/acbfb3  

   Amaro, Rachael C. ; Apai, Dániel ; Zhou, Yifan ; Lew, Ben W. P. ; Casewell, Sarah L. ; Mayorga, L. C. ; Marley, Mark S. ; Tan, Xianyu ; Lothringer, Joshua D. ; Parmentier, Vivien ; et al ( May 2023 , The Astrophysical Journal)

   With infrared flux contrasts larger than typically seen in hot Jupiter, tidally locked white dwarf–brown dwarf binaries offer a superior opportunity to investigate atmospheric processes in irradiated atmospheres. NLTT5306 is such a system, with aM_BD= 52 ± 3M_Jupbrown dwarf, orbiting aT_eff= 7756 ± 35 K white dwarf with an ultra-short period of ∼102 minutes. We present Hubble Space Telescope/Wide Field Camera 3 spectroscopic phase curves of NLTT5306, consisting of 47 spectra from 1.1 to 1.7μm with an average signal-to-noise ratio ∼ 65 per wavelength. We extracted the phase-resolved spectra of the brown dwarf NLTT5306B, finding a small <100 K day–night temperature difference (∼5% of the average day-side temperature). Our best-fit model phase curves revealed a complex wavelength-dependence on amplitudes and relative phase offsets, suggesting longitudinal–vertical atmospheric structure. The night-side spectrum was well fit by a cloudy, nonirradiated atmospheric model while the day side was best matched by a cloudy, weakly irradiated model. Additionally, we created a simple radiative energy redistribution model of the atmosphere and found evidence for efficient day-to-night heat redistribution and a moderately high Bond albedo. We also discovered an internal heat flux much higher than expected given the published system age, leading to an age reassessment that resulted in NLTT5306B most likely being much younger. We find that NLTT5306B is the only known significantly irradiated brown dwarf where the global temperature structure is not dominated by external irradiation, but rather its own internal heat. Our study provides an essential insight into the drivers of global circulation and day-to-night heat transport as a function of irradiation, rotation rate, and internal heat.

    

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5. WattScale: A Data-driven Approach for Energy Efficiency Analytics of Buildings at Scale

   https://doi.org/10.1145/3406961  

   Iyengar, Srinivasan ; Lee, Stephen ; Irwin, David ; Shenoy, Prashant ; Weil, Benjamin ( January 2021 , ACM/IMS Transactions on Data Science)

   null (Ed.)

   Buildings consume over 40% of the total energy in modern societies, and improving their energy efficiency can significantly reduce our energy footprint. In this article, we present WattScale, a data-driven approach to identify the least energy-efficient buildings from a large population of buildings in a city or a region. Unlike previous methods such as least-squares that use point estimates, WattScale uses Bayesian inference to capture the stochasticity in the daily energy usage by estimating the distribution of parameters that affect a building. Further, it compares them with similar homes in a given population. WattScale also incorporates a fault detection algorithm to identify the underlying causes of energy inefficiency. We validate our approach using ground truth data from different geographical locations, which showcases its applicability in various settings. WattScale has two execution modes—(i) individual and (ii) region-based, which we highlight using two case studies. For the individual execution mode, we present results from a city containing >10,000 buildings and show that more than half of the buildings are inefficient in one way or another indicating a significant potential from energy improvement measures. Additionally, we provide probable cause of inefficiency and find that 41%, 23.73%, and 0.51% homes have poor building envelope, heating, and cooling system faults, respectively. For the region-based execution mode, we show that WattScale can be extended to millions of homes in the U.S. due to the recent availability of representative energy datasets. 

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