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1. The Zwicky Transient Facility Type Ia supernova survey: first data release and results

   https://doi.org/10.1093/mnras/stab3093  

   Dhawan, S. ; Goobar, A. ; Smith, M. ; Johansson, J. ; Rigault, M. ; Nordin, J. ; Biswas, R. ; Goldstein, D. ; Nugent, P. ; Kim, Y-L ; et al ( November 2021 , Monthly Notices of the Royal Astronomical Society)

   Type Ia supernovae (SNe Ia) in the nearby Hubble flow are excellent distance indicators in cosmology. The Zwicky Transient Facility (ZTF) has observed a large sample of SNe from an untargeted, rolling survey, reaching 20.8, 20.6, and 20.3 mag in g r, and i band, respectively. With an FoV of 47 deg2, ZTF discovered > 3000 SNe Ia in a little over 2.5 yr. Here, we report on the sample of 761 spectroscopically classified SNe Ia from the first year of operations (DR1). The sample has a median redshift $\bar{z} =$ 0.057, nearly a factor of 2 higher than the current low-z sample. Our sample has a total of 934 spectra, of which 632 were obtained with the robotic SEDm on Palomar P60. We assess the potential for precision cosmology for a total of 305 SNe with redshifts from host galaxy spectra. The sample is already comparable in size to the entire combined literature low-z anchor sample. The median first detection is 13.5 d before maximum light, about 10 d earlier than the median in the literature. Furthermore, six SNe from our sample are at DL < 80 Mpc, for which host galaxy distances can be obtained in the JAMES WEBB SPACE TELESCOPE era, such that we have calibrator and Hubble flow SNe observed with the same instrument. In the entire duration of ZTF-I, we have observed nearly 50 SNe for which we can obtain calibrator distances, key for per cent level distance scale measurements.

    

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2. Cosmological Results from the RAISIN Survey: Using Type Ia Supernovae in the Near Infrared as a Novel Path to Measure the Dark Energy Equation of State

   https://doi.org/10.3847/1538-4357/ac755b  

   Jones, D. O. ; Mandel, K. S. ; Kirshner, R. P. ; Thorp, S. ; Challis, P. M. ; Avelino, A. ; Brout, D. ; Burns, C. ; Foley, R. J. ; Pan, Y. -C. ; et al ( July 2022 , The Astrophysical Journal)

   Type Ia supernovae (SNe Ia) are more precise standardizable candles when measured in the near-infrared (NIR) than in the optical. With this motivation, from 2012 to 2017 we embarked on the RAISIN program with the Hubble Space Telescope (HST) to obtain rest-frame NIR light curves for a cosmologically distant sample of 37 SNe Ia (0.2 ≲z≲ 0.6) discovered by Pan-STARRS and the Dark Energy Survey. By comparing higher-zHST data with 42 SNe Ia atz< 0.1 observed in the NIR by the Carnegie Supernova Project, we construct a Hubble diagram from NIR observations (with only time of maximum light and some selection cuts from optical photometry) to pursue a unique avenue to constrain the dark energy equation-of-state parameter,w. We analyze the dependence of the full set of Hubble residuals on the SN Ia host galaxy mass and find Hubble residual steps of size ∼0.06-0.1 mag with 1.5σ−2.5σsignificance depending on the method and step location used. Combining our NIR sample with cosmic microwave background constraints, we find 1 +w= −0.17 ± 0.12 (statistical + systematic errors). The largest systematic errors are the redshift-dependent SN selection biases and the properties of the NIR mass step. We also use these data to measureH₀= 75.9 ± 2.2 km s⁻¹Mpc⁻¹from stars with geometric distance calibration in the hosts of eight SNe Ia observed in the NIR versusH₀= 71.2 ± 3.8 km s⁻¹Mpc⁻¹using an inverse distance ladder approach tied to Planck. Using optical data, we find 1 +w= −0.10 ± 0.09, and with optical and NIR data combined, we find 1 +w= −0.06 ± 0.07; these shifts of up to ∼0.11 inwcould point to inconsistency in the optical versus NIR SN models. There will be many opportunities to improve this NIR measurement and better understand systematic uncertainties through larger low-zsamples, new light-curve models, calibration improvements, and eventually by building high-zsamples from the Roman Space Telescope.

    

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3. Keck Infrared Transient Survey. I. Survey Description and Data Release 1

   https://doi.org/10.1088/1538-3873/ad1b39  

   Tinyanont, S. ; Foley, R. J. ; Taggart, K. ; Davis, K. W. ; LeBaron, N. ; Andrews, J. E. ; Bustamante-Rosell, M. J. ; Camacho-Neves, Y. ; Chornock, R. ; Coulter, D. A. ; et al ( January 2024 , Publications of the Astronomical Society of the Pacific)

   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.

    

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4. Search for the optical counterpart of the GW170814 gravitational wave event with the VLT Survey Telescope

   https://doi.org/10.1093/mnras/stz3536  

   Grado, A. ; Cappellaro, E. ; Covino, S. ; Getman, F. ; Greco, G. ; Limatola, L. ; Yang, S. ; Amati, L. ; Benetti, S. ; Branchesi, M. ; et al ( December 2019 , Monthly Notices of the Royal Astronomical Society)

   We report on the search for the optical counterpart of the gravitational event GW170814, which was carried out with the VLT Survey Telescope (VST) by the GRAvitational Wave Inaf TeAm. Observations started 17.5 h after the Laser Interferometer Gravitational-wave Observatory (LIGO) and Virgo alert and we covered an area of 99 deg2 that encloses $\sim 77{{\ \rm per\ cent}}$ and $\sim 59{{\ \rm per\ cent}}$ of the initial and refined localization probability regions, respectively. A total of six epochs were secured over nearly two months. The survey reached an average limiting magnitude of 22 AB mag in the r band. After assuming the model described in Perna, Lazzati & Farr, that derives as possible optical counterpart of a BBH (binary black hole) event a transient source declining in about one day, we have computed a survey efficiency of about $5{{\ \rm per\ cent}}$. This paper describes the VST observational strategy and the results obtained by our analysis pipelines developed to search for optical transients in multi-epoch images. We report the catalogue of the candidates with possible identifications based on light-curve fitting. We have identified two dozens of SNe, nine AGNs, and one QSO. Nineteen transients characterized by a single detection were not classified. We have restricted our analysis only to the candidates that fall into the refined localization map. None out of 39 left candidates could be positively associated with GW170814. This result implies that the possible emission of optical radiation from a BBH merger had to be fainter than r ∼ 22 (Loptical ∼ 1.4 × 1042 erg s−1) on a time interval ranging from a few hours up to two months after the gravitational wave event.

    

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5. Galaxy morphological classification in deep-wide surveys via unsupervised machine learning

   https://doi.org/10.1093/mnras/stz3006  

   Martin, G. ; Kaviraj, S. ; Hocking, A. ; Read, S. C. ; Geach, J. E. ( October 2019 , Monthly Notices of the Royal Astronomical Society)

   Galaxy morphology is a fundamental quantity, which is essential not only for the full spectrum of galaxy-evolution studies, but also for a plethora of science in observational cosmology (e.g. as a prior for photometric-redshift measurements and as contextual data for transient light-curve classifications). While a rich literature exists on morphological-classification techniques, the unprecedented data volumes, coupled, in some cases, with the short cadences of forthcoming ‘Big-Data’ surveys (e.g. from the LSST), present novel challenges for this field. Large data volumes make such data sets intractable for visual inspection (even via massively distributed platforms like Galaxy Zoo), while short cadences make it difficult to employ techniques like supervised machine learning, since it may be impractical to repeatedly produce training sets on short time-scales. Unsupervised machine learning, which does not require training sets, is ideally suited to the morphological analysis of new and forthcoming surveys. Here, we employ an algorithm that performs clustering of graph representations, in order to group image patches with similar visual properties and objects constructed from those patches, like galaxies. We implement the algorithm on the Hyper-Suprime-Cam Subaru-Strategic-Program Ultra-Deep survey, to autonomously reduce the galaxy population to a small number (160) of ‘morphological clusters’, populated by galaxies with similar morphologies, which are then benchmarked using visual inspection. The morphological classifications (which we release publicly) exhibit a high level of purity, and reproduce known trends in key galaxy properties as a function of morphological type at z < 1 (e.g. stellar-mass functions, rest-frame colours, and the position of galaxies on the star-formation main sequence). Our study demonstrates the power of unsupervised machine learning in performing accurate morphological analysis, which will become indispensable in this new era of deep-wide surveys.

    

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