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Gigantic jets (GJs) are a type of transient luminous event (TLE) which also includes sprites, elves, halos, and blue jets [Pasko2010, doi: 10.1029/2009JA014860]. However, GJs are unique in that they directly couple electric charge reservoirs in the troposphere (i.e. thunderclouds) with the lower ionosphere, allowing significant amounts of charge (100s of C) to flow between these regions. We do not understand how this affects the ionosphere and global electric circuit. Past observations are very limited, resulting from ground-based cameras getting lucky enough to capture an event while looking over a distant thunderstorm [Liu et al. 2015, doi: 10.1038/ncomms6995]. Additionally, GJ-producing storms are normally accompanied by substantial areas of stratiformclouds obscuring the view, and they tend to occur more often over the ocean. To solve this problem of limited detection capability, we have developed a pipeline that utilizes machine learning and sensor fusion of multiple sensing modalities (optical, VLF, ELF). Our pipeline can detect GJs over nearly a hemisphere and operate 24/7, potentially revolutionizing how GJs are detected and paving the way for other TLE and unique lightning event detection. Our pipeline begins by performing detection with data from the Geostationary Lightning Mapper (GLM), which is a staring optical imager in geostationary orbit that detects the 777.4 nm (OI) triplet from lightning leaders [Goodman et al. 2013, doi: 10.1016/j.atmosres.2013.01.006]. Gigantic jets have unique signatures in the GLM data from past studies [Boggs et al. 2019, doi: 10.1029/2019GL082278]. We have developed a supervised, ensemble machine learning classifier that detects potential gigantic jets in the GLM data. Considering we have an imbalanced dataset, we use data imbalance techniques such as Synthetic Minority Oversampling Technique (SMOTE) when training the classifier. Next, we combine data from multiple sensing modalities to vet the candidate GJs from the classifier in multiple stages. The first stage filters the candidate GJs to the stereo GLM region [Mach and Virts, 2021, doi: 10.1175/JTECH-D-21-0078.1], and calculates the stereo altitudes for all the events. GJs have stereo altitude sources consistently between 15-25 km altitude from the leader escaping the cloud top [Boggs et al. 2022, doi: 10.1126/sciadv.abl8731]. Next, we match the events spatiotemporally to GLD360 data to remove cloud-to-ground events. Subsequently, we use a statistical GOES ABI model (developed at GTRI) to filter out events that have differing parent storms from our truth database. Finally, we use a multi-parameter extremely low frequency (ELF) vetting model (developed by Duke) to filter out the remaining non-GJ events. After a few complete detection and vetting cycles, we have found tens of new events with a high degree of confidence. With further development of our pipeline and deployment to the entire GLM field-of-view (not limited to stereo region), we anticipate hundreds of new detections per year, significantly more than ground-based cameras, allowing for comprehensive studies relating gigantic jets to the other atmospheric phenomena 

The most active phases of star formation and black hole accretion are strongly affected by dust extinction, making far-infrared (FIR) observations the best way to disentangle and study the co-evolution of galaxies and super massive black holes. The plethora of fine-structure lines and emission features from dust and ionised and neutral atomic and warm molecular gas in the rest-frame mid-infrared (MIR) and FIR provide unmatched diagnostic opportunities to determine the properties of gas and dust, measure gas-phase metallicities, and map cold galactic outflows in even the most obscured galaxies. By combining multi-band photometric surveys with low- and high-resolution FIR spectroscopy, the PRobe far-Infrared Mission for Astrophysics (PRIMA), a 1.8 m diameter, cryogenically cooled FIR observatory currently at the conception stage, will revolutionise the field of galaxy evolution by taking advantage of this IR toolkit to find and study dusty galaxies across galactic time. In this work, we make use of the phenomenological simulation SPRITZand the Santa Cruz semi-analytical model to describe how a moderately deep multi-band PRIMA photometric survey can easily reach beyond previous IR missions to detect and study galaxies down to 10¹¹ L_⊙beyond cosmic noon and at least up toz = 4, even in the absence of gravitational lensing. By decomposing the spectral energy distribution (SED) of these photometrically selected galaxies, we show that PRIMA can be used to accurately measure the relative AGN power, the mass fraction contributed by polycyclic aromatic hydrocarbons (PAHs), and the total IR luminosity. At the same time, spectroscopic follow up with PRIMA will allow us to trace both the star formation and black hole accretion rates (SFRs and BHARs), the gas-phase metallicities, and the mass-outflow rates of cold gas in hundreds to thousands of individual galaxies toz = 2. 

Abstract Deep‐focus earthquakes at 350–660 km are presumably caused by olivine‐spinel phase transformation (PT). This cannot, however, explain the observed high seismic strain rate, which requires PT to complete within seconds, while metastable olivine does not transform for over a million years. Recent theory quantitatively describes how severe plastic deformations (SPD) can solve this dilemma but lacking experimental proof. Here, we introduce dynamic rotational diamond anvil cell with rough diamond anvils to impose SPD on San Carlos olivine. While olivine never transformed to spinel at room temperature, we obtained reversible olivine‐ringwoodite PT under SPD at 15–28 GPa within tens of seconds. The PT pressure reduces with increasing dislocation density, microstrain, plastic strain, and decreasing crystallite size. Results demonstrate a new strain‐induced PT mechanism compared to a pressure/temperature‐induced one. Combined with SPD during olivine subduction, this mechanism can accelerate olivine‐ringwoodite PT from millions of years to timescales relevant to earthquakes. 

Abstract We presentCloudymodeling of infrared emission lines in the Wolf–Rayet (WR) nebula N76 caused by one of the most luminous and hottest WR stars in the low metallicity Small Magellanic Cloud. We use spatially resolved mid-infrared Spitzer/InfRared Spectrograph and far-infrared Herschel/PACS spectroscopy to establish the physical conditions of the ionized gas. The spatially resolved distribution of the emission allows us to constrain properties much more accurately than using spatially integrated quantities. We construct models with a range of constant hydrogen densities between n_H= 4–10 cm⁻³and a stellar wind-blown cavity of 10 pc, which reproduces the intensity and shape of most ionized gas emission lines, including the high ionization lines [Oiv] and [Nev], as well as [Siii], [Siv], [Oiii], and [Neiii]. Our models suggest that the majority of [Siii] emission (91%) is produced at the edge of the Hiiregion around the transition between ionized and atomic gas while very little of the [Cii] (<5%) is associated with the ionized gas. The physical conditions of N76 are characterized by a hot HII region with a maximum electron temperature ofT_e∼ 24,000 K, electron densities that range fromn_e∼ 4 to 12 cm⁻³, and high ionization parameters of $\log \left(U\right)\sim -1.15\mathrm{to}-1.77.$By analyzing a low-metallicity WR nebula with a single ionization source, this work gives valuable insights into the impact WR stars have on the galaxy-integrated ionized gas properties in nearby dwarf galaxies. 

We present a study of new 7.7–11.3 μm data obtained with theJames WebbSpace Telescope Mid-InfraRed Instrument in the starburst galaxy M 82. In particular, we focus on the dependency of the integrated CO(1–0) line intensity on the MIRI-F770W and MIRI-F1130W filter intensities to investigate the correlation between H₂content and the 7.7 and 11.3 μm features from polycyclic aromatic hydrocarbons (PAH) in M 82’s outflows. To perform our analysis, we identify CO clouds using the archival¹²CO(J = 1 − 0) NOEMA moment 0 map within 2 kpc from the center of M 82, with sizes ranging between ∼21 and 270 pc; then, we compute the CO-to-PAH relations for the 306 validated CO clouds. On average, the power-law slopes for the two relations in M 82 are lower than what is seen in local main-sequence spirals. In addition, there is a moderate correlation betweenI_{CO(1 − 0)} − I_7.7 μm/I_11.3 μmfor some of the CO cloud groups analyzed in this work. Our results suggest that the extreme conditions in M 82 translate into CO not tracing the full budget of molecular gas in smaller clouds, perhaps as a consequence of photoionization and/or emission suppression of CO molecules due to hard radiation fields from the central starburst. 

Gigantic jets are a type of transient luminous event (TLE, Pasko 2010, doi: 10.1029/2009JA014860) that escape the cloud top of a thunderstorm and propagate up to the lower ionosphere (80-100 km altitude), transferring tens to hundreds of Coulombs of charge. Due to rarity of observations, it is still not understood how they affect the lower ionosphere and what storm systems produce them. In this presentation we will provide an overview and present preliminary results from a multi-institutional collaborative project, which seeks to detect gigantic jets over hemispheric scales using a combination of orbital and ground-based sensors and machine learning. Our pipeline has the potential to detect significantly more gigantic jets (thousands) than current methods, which relies on using ground-based cameras. We will build a large database of gigantic jet detections, and correlate the events with a Very Low Frequency (VLF) remote sensing network (Cohen et al. 2009, doi: 10.1109/TGRS.2009.2028334) to understand how they perturb the lower ionosphere – in addition to other meteorological datasets. Our detection methodology primarily uses the Geostationary Lightning Mapper (GLM), which is a staring optical imager in geostationary orbit that detects the 777.4 nm (OI) triplet commonly emitted by lightning (Goodman et al. 2013, doi: 10.1016/j.atmosres.2013.01.006). Gigantic jets have been shown to have unique signatures in the GLM data from past studies (Boggs et al. 2019, doi: 10.1029/2019GL082278; Boggs et al. 2022, doi: 10.1126/sciadv.abl8731). Thus far, we have built a preliminary, supervised machine learning model that detects potential gigantic jets using GLM, and begun development on a series of vetting techniques to confirm the detections as real gigantic jets. The vetting techniques use a combination of low frequency (LF) and extremely low frequency (ELF) sferic data, in combination with stereo GLM measurements that provide optical source altitude. In addition, we will soon be able to calculate optical stereo sources with GLM on GOES-16 and the newly launched Lightning Imager on EUMETSAT, significantly expanding the stereo region of detection. When our detection methodology grows in maturity, we will deploy it to all past GLM data (2018-present) and share the database publicly, allowing other researchers to use this data for their own research.