Reconsidering photometric estimation of local star formation environment and its correlation with Type Ia Supernova luminosity

Recent studies on the environmental dependence of Type Ia supernova (SN Ia) luminosity focus on the local environment where the SN exploded, considering that this is more directly linked to the SN progenitors. However, there is a debate about the local environmental, specifically local star formation rate (SFR), dependence of the SN Ia luminosity. A recent study claims that the dependence is insignificant ($0.051 \pm 0.020$ mag; $2.6\sigma$), based on the local SFR measurement by fitting local $ugrizy$ photometry data. However, we find that this photometric local SFR measurement is inaccurate. We argue this based on the theoretical background of SFR measurement and the methodology used to make that claim with their local $ugrizy$ photometry data, especially due to a limited range of extinction parameters used when fitting the data. Therefore, we re-analyse the same host galaxies with the same fitting code, but with more physically motivated extinction treatments and global $ugriz$ photometry of host galaxies. We estimate global stellar mass and SFR. Then, local star formation environments are inferred by using the method which showed that SNe Ia in globally passive galaxies have locally passive environments, while those in globally star-forming low-mass galaxies have locally star-forming environments. We find that there is significant local environmental dependence of SN Ia luminosities: SNe Ia in locally star-forming environments are $0.072\pm0.021$ mag ($3.4\sigma$) fainter than those in locally passive environments, even though SN Ia luminosities have been further corrected by the BBC method that reduces the size of the dependence.Comment: 10 pages, 7 figures, 3 tables and 1 appendix table containing data we used; accepted for publication in MNRA

Lessons Learned in ATCO2: 5000 hours of Air Traffic Control Communications for Robust Automatic Speech Recognition and Understanding

Voice communication between air traffic controllers (ATCos) and pilots is critical for ensuring safe and efficient air traffic control (ATC). This task requires high levels of awareness from ATCos and can be tedious and error-prone. Recent attempts have been made to integrate artificial intelligence (AI) into ATC in order to reduce the workload of ATCos. However, the development of data-driven AI systems for ATC demands large-scale annotated datasets, which are currently lacking in the field. This paper explores the lessons learned from the ATCO2 project, a project that aimed to develop a unique platform to collect and preprocess large amounts of ATC data from airspace in real time. Audio and surveillance data were collected from publicly accessible radio frequency channels with VHF receivers owned by a community of volunteers and later uploaded to Opensky Network servers, which can be considered an "unlimited source" of data. In addition, this paper reviews previous work from ATCO2 partners, including (i) robust automatic speech recognition, (ii) natural language processing, (iii) English language identification of ATC communications, and (iv) the integration of surveillance data such as ADS-B. We believe that the pipeline developed during the ATCO2 project, along with the open-sourcing of its data, will encourage research in the ATC field. A sample of the ATCO2 corpus is available on the following website: https://www.atco2.org/data, while the full corpus can be purchased through ELDA at http://catalog.elra.info/en-us/repository/browse/ELRA-S0484. We demonstrated that ATCO2 is an appropriate dataset to develop ASR engines when little or near to no ATC in-domain data is available. For instance, with the CNN-TDNNf kaldi model, we reached the performance of as low as 17.9% and 24.9% WER on public ATC datasets which is 6.6/7.6% better than "out-of-domain" but supervised CNN-TDNNf model.Comment: Manuscript under revie

ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications

Personal assistants, automatic speech recognizers and dialogue understanding systems are becoming more critical in our interconnected digital world. A clear example is air traffic control (ATC) communications. ATC aims at guiding aircraft and controlling the airspace in a safe and optimal manner. These voice-based dialogues are carried between an air traffic controller (ATCO) and pilots via very-high frequency radio channels. In order to incorporate these novel technologies into ATC (low-resource domain), large-scale annotated datasets are required to develop the data-driven AI systems. Two examples are automatic speech recognition (ASR) and natural language understanding (NLU). In this paper, we introduce the ATCO2 corpus, a dataset that aims at fostering research on the challenging ATC field, which has lagged behind due to lack of annotated data. The ATCO2 corpus covers 1) data collection and pre-processing, 2) pseudo-annotations of speech data, and 3) extraction of ATC-related named entities. The ATCO2 corpus is split into three subsets. 1) ATCO2-test-set corpus contains 4 hours of ATC speech with manual transcripts and a subset with gold annotations for named-entity recognition (callsign, command, value). 2) The ATCO2-PL-set corpus consists of 5281 hours of unlabeled ATC data enriched with automatic transcripts from an in-domain speech recognizer, contextual information, speaker turn information, signal-to-noise ratio estimate and English language detection score per sample. Both available for purchase through ELDA at http://catalog.elra.info/en-us/repository/browse/ELRA-S0484. 3) The ATCO2-test-set-1h corpus is a one-hour subset from the original test set corpus, that we are offering for free at https://www.atco2.org/data. We expect the ATCO2 corpus will foster research on robust ASR and NLU not only in the field of ATC communications but also in the general research community.Comment: Manuscript under review; The code will be available at https://github.com/idiap/atco2-corpu

SN Zwicky: uncovering a population of gravitational lens galaxies with magnified "standard candles"

We report the discovery of a very rare phenomenon, a multiply-imaged gravitationally lensed Type Ia supernova (SNe Ia), "SN Zwicky", a.k.a. SN 2022qmx, magnified nearly twenty-five times by a foreground galaxy. The system was identified as intrinsically bright thanks to the "standard candle" nature of SNe Ia. Observations with high-spatial resolution instruments resolved a system with four nearly simultaneous images, with an Einstein radius of only $\theta_E =0.167"$, corresponding to a lens mass of $8\cdot 10^9$ solar masses within a physical size below $0.8$ kiloparsecs. A smooth lens model fails to reproduce the image flux ratios, suggesting significant additional magnification from compact objects. Given the small image splitting and a relatively faint deflecting galaxy, the lensing system would not have been found through the angular separation technique generally used in large imaging surveys

A Large Fraction of Hydrogen-rich Supernova Progenitors Experience Elevated Mass Loss Shortly Prior to Explosion

Spectroscopic detection of narrow emission lines traces the presence of circumstellar mass distributions around massive stars exploding as core-collapse supernovae. Transient emission lines disappearing shortly after the supernova explosion suggest that the material spatial extent is compact and implies an increased mass loss shortly prior to explosion. Here, we present a systematic survey for such transient emission lines (Flash Spectroscopy) among Type II supernovae detected in the first year of the Zwicky Transient Facility survey. We find that at least six out of ten events for which a spectrum was obtained within two days of the estimated explosion time show evidence for such transient flash lines. Our measured flash event fraction (>30% at 95% confidence level) indicates that elevated mass loss is a common process occurring in massive stars that are about to explode as supernovae

The prevalence and influence of circumstellar material around hydrogen-rich supernova progenitors

Narrow transient emission lines (flash-ionization features) in early supernova (SN) spectra trace the presence of circumstellar material (CSM) around the massive progenitor stars of core-collapse SNe. The lines disappear within days after the SN explosion, suggesting that this material is spatially confined, and originates from enhanced mass loss shortly (months to a few years) prior to explosion. We performed a systematic survey of H-rich (Type II) SNe discovered within less than two days from explosion during the first phase of the Zwicky Transient Facility (ZTF) survey (2018-2020), finding thirty events for which a first spectrum was obtained within $< 2$ days from explosion. The measured fraction of events showing flash ionisation features ($>36\%$ at $95\%$ confidence level) confirms that elevated mass loss in massive stars prior to SN explosion is common. We find that SNe II showing flash ionisation features are not significantly brighter, nor bluer, nor more slowly rising than those without. This implies that CSM interaction does not contribute significantly to their early continuum emission, and that the CSM is likely optically thin. We measured the persistence duration of flash ionisation emission and find that most SNe show flash features for $\approx 5$ days. Rarer events, with persistence timescales $>10$ days, are brighter and rise longer, suggesting these may be intermediate between regular SNe II and strongly-interacting SNe IIn

Optical follow-up of the neutron star-black hole mergers S200105ae and S200115j

LIGO and Virgo’s third observing run revealed the first neutron star–black hole (NSBH) merger candidates in gravitational waves. These events are predicted to synthesize r-process elements1,2 creating optical/near-infrared ‘kilonova’ emission. The joint gravitational wave and electromagnetic detection of an NSBH merger could be used to constrain the equation of state of dense nuclear matter3, and independently measure the local expansion rate of the Universe4. Here, we present the optical follow-up and analysis of two of the only three high-significance NSBH merger candidates detected to date, S200105ae and S200115j, with the Zwicky Transient Facility5. The Zwicky Transient Facility observed ~48% of S200105ae and ~22% of S200115j’s localization probabilities, with observations sensitive to kilonovae brighter than −17.5 mag fading at 0.5 mag d−1 in the g- and r-bands; extensive searches and systematic follow-up of candidates did not yield a viable counterpart. We present state-of-the-art kilonova models tailored to NSBH systems that place constraints on the ejecta properties of these NSBH mergers. We show that with observed depths of apparent magnitude ~22 mag, attainable in metre-class, wide-field-of-view survey instruments, strong constraints on ejecta mass are possible, with the potential to rule out low mass ratios, high black hole spins and large neutron star radii

The Discovery of a Gravitationally Lensed Supernova Ia at Redshift 2.22

We present the discovery and measurements of a gravitationally lensed supernova (SN) behind the galaxy cluster MOO J1014+0038. Based on multi-band Hubble Space Telescope and Very Large Telescope (VLT) photometry of the supernova, and VLT spectroscopy of the host galaxy, we find a 97.5% probability that this SN is a SN Ia, and a 2.5% chance of a CC SN. Our typing algorithm combines the shape and color of the light curve with the expected rates of each SN type in the host galaxy. With a redshift of 2.2216, this is the highest redshift SN Ia discovered with a spectroscopic host-galaxy redshift. A further distinguishing feature is that the lensing cluster, at redshift 1.23, is the most distant to date to have an amplified SN. The SN lies in the middle of the color and light-curve shape distributions found at lower redshift, disfavoring strong evolution to z = 2.22. We estimate an amplification due to gravitational lensing of 2.8+0.6-0.5 (1.10 +- 0.23 mag)---compatible with the value estimated from the weak-lensing-derived mass and the mass-concentration relation from LambdaCDM simulations---making it the most amplified SN Ia discovered behind a galaxy cluster