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We present the Keck Infrared Transient Survey, a NASA Key Strategic Mission Support program to obtain near-infrared (NIR) spectra of astrophysical transients of all types, and its first data release, consisting of 105 NIR spectra of 50 transients. Such a data set is essential as we enter a new era of IR astronomy with the James Webb Space Telescope (JWST) and the upcoming Nancy Grace Roman Space Telescope (Roman). NIR spectral templates will be essential to search JWST images for stellar explosions of the first stars and to plan an effective Roman SN Ia cosmology survey, both key science objectives for mission success. Between 2022 February and 2023 July, we systematically obtained 274 NIR spectra of 146 astronomical transients, representing a significant increase in the number of available NIR spectra in the literature. Here, we describe the first release of data from the 2022A semester. We systematically observed three samples: a flux-limited sample that includes all transients <17 mag in a red optical band (usually ZTFror ATLASobands); a volume-limited sample including all transients within redshiftz< 0.01 (D≈ 50 Mpc); and an SN Ia sample targeting objects at phases and light-curve parameters that had scant existing NIR data in the literature. The flux-limited sample is 39% complete (60% excluding SNe Ia), while the volume-limited sample is 54% complete and is 79% complete toz= 0.005. Transient classes observed include common Type Ia and core-collapse supernovae, tidal disruption events, luminous red novae, and the newly categorized hydrogen-free/helium-poor interacting Type Icn supernovae. We describe our observing procedures and data reduction usingPypeIt, which requires minimal human interaction to ensure reproducibility.

 

Context. SN 2020qlb (ZTF20abobpcb) is a hydrogen-poor superluminous supernova (SLSN-I) that is among the most luminous (maximum M g  = −22.25 mag) and that has one of the longest rise times (77 days from explosion to maximum). We estimate the total radiated energy to be > 2.1 × 10 51 erg. SN 2020qlb has a well-sampled light curve that exhibits clear near and post peak undulations, a phenomenon seen in other SLSNe, whose physical origin is still unknown. Aims. We discuss the potential power source of this immense explosion as well as the mechanisms behind its observed light curve undulations. Methods. We analyze photospheric spectra and compare them to other SLSNe-I. We constructed the bolometric light curve using photometry from a large data set of observations from the Zwicky Transient Facility (ZTF), Liverpool Telescope (LT), and Neil Gehrels Swift Observatory and compare it with radioactive, circumstellar interaction and magnetar models. Model residuals and light curve polynomial fit residuals are analyzed to estimate the undulation timescale and amplitude. We also determine host galaxy properties based on imaging and spectroscopy data, including a detection of the [O III] λ 4363, auroral line, allowing for a direct metallicity measurement. Results. We rule out the Arnett 56 Ni decay model for SN 2020qlb’s light curve due to unphysical parameter results. Our most favored power source is the magnetic dipole spin-down energy deposition of a magnetar. Two to three near peak oscillations, intriguingly similar to those of SN 2015bn, were found in the magnetar model residuals with a timescale of 32 ± 6 days and an amplitude of 6% of peak luminosity. We rule out centrally located undulation sources due to timescale considerations; and we favor the result of ejecta interactions with circumstellar material (CSM) density fluctuations as the source of the undulations. 

Abstract During the Zwicky Transient Facility (ZTF) Phase I operations, 78 hydrogen-poor superluminous supernovae (SLSNe-I) were discovered in less than 3 yr, constituting the largest sample from a single survey. This paper (Paper I) presents the data, including the optical/UV light curves and classification spectra, while Paper II in this series will focus on the detailed analysis of the light curves and modeling. Our photometry is primarily taken by ZTF in the g , r , and i bands, and with additional data from other ground-based facilities and Swift. The events of our sample cover a redshift range of z = 0.06 − 0.67, with a median and 1 σ error (16% and 84% percentiles) of z med = 0.265 − 0.135 + 0.143 . The peak luminosity covers −22.8 mag ≤ M g ,peak ≤ −19.8 mag, with a median value of − 21.48 − 0.61 + 1.13 mag. The light curves evolve slowly with a mean rest-frame rise time of t rise = 41.9 ± 17.8 days. The luminosity and timescale distributions suggest that low-luminosity SLSNe-I with a peak luminosity ∼−20 mag or extremely fast-rising events (<10 days) exist, but are rare. We confirm previous findings that slowly rising SLSNe-I also tend to fade slowly. The rest-frame color and temperature evolution show large scatters, suggesting that the SLSN-I population may have diverse spectral energy distributions. The peak rest-frame color shows a moderate correlation with the peak absolute magnitude, i.e., brighter SLSNe-I tend to have bluer colors. With optical and UV photometry, we construct the bolometric luminosity and derive a bolometric correction relation that is generally applicable for converting g , r -band photometry to the bolometric luminosity for SLSNe-I. 

The neural plausibility of backpropagation has long been disputed, primarily for its use of non-local weight transport — the biologically dubious requirement that one neuron instantaneously measure the synaptic weights of another. Until recently, attempts to create local learning rules that avoid weight transport have typically failed in the large-scale learning scenarios where backpropagation shines, e.g. ImageNet categorization with deep convolutional networks. Here, we investigate a recently proposed local learning rule that yields competitive performance with backpropagation and find that it is highly sensitive to metaparameter choices, requiring laborious tuning that does not transfer across network architecture. Our analysis indicates the underlying mathematical reason for this instability, allowing us to identify a more robust local learning rule that better transfers without metaparameter tuning. Nonetheless, we find a performance and stability gap between this local rule and backpropagation that widens with increasing model depth. We then investigate several non-local learning rules that relax the need for instantaneous weight transport into a more biologically-plausible "weight estimation" process, showing that these rules match state-of-the-art performance on deep networks and operate effectively in the presence of noisy updates. Taken together, our results suggest two routes towards the discovery of neural implementations for credit assignment without weight symmetry: further improvement of local rules so that they perform consistently across architectures and the identification of biological implementations for non-local learning mechanisms. 

Abstract The number of long gamma-ray bursts (GRBs) known to have occurred in the distant Universe (z > 5) is small (∼15); however, these events provide a powerful way of probing star formation at the onset of galaxy evolution. In this paper, we present the case for GRB 100205A being a largely overlooked high-redshift event. While initially noted as a high-z candidate, this event and its host galaxy have not been explored in detail. By combining optical and near-infrared Gemini afterglow imaging (at t < 1.3 d since burst) with deep late-time limits on host emission from the Hubble Space Telescope, we show that the most likely scenario is that GRB 100205A arose in the range 4 < z < 8. GRB 100205A is an example of a burst whose afterglow, even at ∼1 h post burst, could only be identified by 8-m class IR observations, and suggests that such observations of all optically dark bursts may be necessary to significantly enhance the number of high-redshift GRBs known.