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Abstract We present a JWST MIRI medium-resolution spectrometer spectrum (5–27μm) of the Type Ia supernova (SN Ia) SN 2021aefx at +415 days pastB-band maximum. The spectrum, which was obtained during the iron-dominated nebular phase, has been analyzed in combination with previous JWST observations of SN 2021aefx to provide the first JWST time series analysis of an SN Ia. We find that the temporal evolution of the [Coiii] 11.888μm feature directly traces the decay of⁵⁶Co. The spectra, line profiles, and their evolution are analyzed with off-center delayed-detonation models. Best fits were obtained with white dwarf (WD) central densities ofρ_c= 0.9−1.1 × 10⁹g cm⁻³, a WD mass ofM_WD= 1.33–1.35M_⊙, a WD magnetic field of ≈10⁶G, and an off-center deflagration-to-detonation transition at ≈0.5M_⊙seen opposite to the line of sight of the observer (−30°). The inner electron capture core is dominated by energy deposition fromγ-rays, whereas a broader region is dominated by positron deposition, placing SN 2021aefx at +415 days in the transitional phase of the evolution to the positron-dominated regime. The formerly “flat-tilted” profile at 9μm now has a significant contribution from [Niiv], [Feii], and [Feiii] and less from [Ariii], which alters the shape of the feature as positrons mostly excite the low-velocity Ar. Overall, the strength of the stable Ni features in the spectrum is dominated by positron transport rather than the Ni mass. Based on multidimensional models, our analysis is consistent with a single-spot, close-to-central ignition with an indication of a preexisting turbulent velocity field and excludes a multiple-spot, off-center ignition. 

In this work, we propose a novel approach for the real-time estimation of chip-level spatial power maps for commercial Google Coral M.2 TPU chips based on a machine-learning technique for the first time. The new method can enable the development of more robust runtime power and thermal control schemes to take advantage of spatial power information such as hot spots that are otherwise not available. Different from the existing commercial multi-core processors in which real-time performance-related utilization information is available, the TPU from Google does not have such information. To mitigate this problem, we propose to use features that are related to the workloads of running different deep neural networks (DNN) such as the hyperparameters of DNN and TPU resource information generated by the TPU compiler. The new approach involves the offline acquisition of accurate spatial and temporal temperature maps captured from an external infrared thermal imaging camera under nominal working conditions of a chip. To build the dynamic power density map model, we apply generative adversarial networks (GAN) based on the workload-related features. Our study shows that the estimated total powers match the manufacturer's total power measurements extremely well. Experimental results further show that the predictions of power maps are quite accurate, with the RMSE of only 4.98\rm mW/mm^2, or 2.6\% of the full-scale error. The speed of deploying the proposed approach on an Intel Core i7-10710U is as fast as 6.9ms, which is suitable for real-time estimation. 

ABSTRACT We report the All-Sky Automated Survey for SuperNovae discovery of the tidal disruption event (TDE) ASASSN-23bd (AT 2023clx) in NGC 3799, a LINER galaxy with no evidence of strong active galactic nucleus (AGN) activity over the past decade. With a redshift of z = 0.01107 and a peak ultraviolet (UV)/optical luminosity of (5.4 ± 0.4) × 1042 erg s−1, ASASSN-23bd is the lowest-redshift and least-luminous TDE discovered to date. Spectroscopically, ASASSN-23bd shows H α and He i emission throughout its spectral time series, there are no coronal lines in its near-infrared spectrum, and the UV spectrum shows nitrogen lines without the strong carbon and magnesium lines typically seen for AGN. Fits to the rising ASAS-SN light curve show that ASASSN-23bd started to brighten on MJD 59988$$^{+1}_{-1}$$, ∼9 d before discovery, with a nearly linear rise in flux, peaking in the g band on MJD $$60 \, 000^{+3}_{-3}$$. Scaling relations and TDE light curve modelling find a black hole mass of ∼106 M⊙, which is on the lower end of supermassive black hole masses. ASASSN-23bd is a dim X-ray source, with an upper limit of $$L_{0.3-10\, \mathrm{keV}} \lt 1.0\times 10^{40}$$ erg s−1 from stacking all Swift observations prior to MJD 60061, but with soft (∼0.1 keV) thermal emission with a luminosity of $$L_{0.3-2 \, \mathrm{keV}}\sim 4\times 10^{39}$$ erg s−1 in XMM-Newton observations on MJD 60095. The rapid (t < 15 d) light curve rise, low UV/optical luminosity, and a luminosity decline over 40 d of ΔL40 ≈ −0.7 dex make ASASSN-23bd one of the dimmest TDEs to date and a member of the growing ‘Low Luminosity and Fast’ class of TDEs. 

Abstract We present the second and final release of optical spectroscopy of Type Ia supernovae (SNe Ia) obtained during the first and second phases of the Carnegie Supernova Project (CSP-I and CSP-II). The newly released data consist of 148 spectra of 30 SNe Ia observed in the course of CSP-I and 234 spectra of 127 SNe Ia obtained during CSP-II. We also present 216 optical spectra of 46 historical SNe Ia, including 53 spectra of 30 SNe Ia observed by the Calán/Tololo Supernova Survey. We combine these observations with previously published CSP data and publicly available spectra to compile a large sample of measurements of spectroscopic parameters at maximum light, consisting of pseudo-equivalent widths and expansion velocities of selected features for 232 CSP and historical SNe Ia (including more than 1000 spectra). Finally, we review some of the strongest correlations between spectroscopic and photometric properties of SNe Ia. Specifically, we define two samples: one consisting of SNe Ia discovered by targeted searches (most of them CSP-I objects) and the other composed of SNe Ia discovered by untargeted searches, which includes most of the CSP-II objects. The analyzed correlations are similar for both samples. We find a larger incidence of SNe Ia belonging to the cool and broad-line Branch subtypes among the events discovered by targeted searches, shallow-silicon SNe Ia are present with similar frequencies in both samples, while core normal SNe Ia are more frequent in untargeted searches.