Euclid will collect an enormous amount of data during the mission's lifetime, observing billions of galaxies in the extragalactic sky. Along with traditional template-fitting methods, numerous Machine Learning algorithms have been presented for computing their photometric redshifts and physical parameters (PP), requiring significantly less computing effort while producing equivalent performance measures. However, their performance is limited by the quality and amount of input information, to the point where the recovery of some well-established physical relationships between parameters might not be guaranteed. To forecast the reliability of Euclid photo-$z$s and PPs calculations, we produced two mock catalogs simulating Euclid photometry. We simulated the Euclid Wide Survey (EWS) and Euclid Deep Fields (EDF). We tested the performance of a template-fitting algorithm (Phosphoros) and four ML methods in recovering photo-$z$s, stellar masses, star-formation rates, and the SFMS. To mimic the Euclid processing as closely as possible, the models were trained with Phosphoros-recovered labels. For the EWS, we found that the best results are achieved with a Mixed Labels approach, training the models with Wide survey features and labels from the Phosphoros results on deeper photometry, i.e., with the best possible set of labels for a given photometry. This imposes a prior, helping the models to better discern cases in degenerate regions of feature space, i.e., when galaxies have similar magnitudes and colors but different redshifts and PPs, with performance metrics even better than those found with Phosphoros. We found no more than $3\%$ performance degradation using a COSMOS-like reference sample or removing $u$ band data, which will not be available until after data release DR1. The best results are obtained for the EDF, with appropriate recovery of photo-$z$, PPs, and the SFMS.
Although stable neutron stars (NS) can in principle exist down to masses Mns ~ 0.1Msun, standard models of stellar core-collapse predict a robust lower limit Mns >~ 1.2Msun, roughly commensurate with the Chandrasekhar mass Mch of the progenitor's iron core (electron fraction Ye ~ 0.5). However, this limit may be circumvented in sufficiently dense neutron-rich environments (Ye << 0.5) for which Mch ~ Ye^2 is reduced to < Msun. Such physical conditions could arise in the black hole accretion disks formed from the collapse of rapidly-rotating stars (``collapsars''), as a result of gravitational instabilities and cooling-induced fragmentation, similar to models for planet formation in protostellar disks. We confirm that the conditions to form sub-solar mass NS (ssNS) may be marginally satisfied in the outer regions of massive neutrino-cooled collapsar disks. If the disk fragments into multiple ssNS, their subsequent coalescence offers a channel for precipitating sub-solar mass LIGO/Virgo gravitational-wave mergers that does not implicate primordial black holes. The model makes several additional predictions: (1) ~Hz frequency Doppler modulation of the ssNS-merger gravitational wave signals due to the binary's orbital motion in the disk; (2) at least one additional gravitational wave event (coincident within <~ hours), from the coalescence of the ssNS-merger remnant(s) with the central black hole; (3) an associated gamma-ray burst and supernova counterpart, the latter boosted in energy and enriched with r-process elements from the NS merger(s) embedded within the exploding stellar envelope (``kilonovae inside a supernova'').
The recent inference of sulphur dioxide (SO$_2$) in the atmosphere of the hot ($\sim$1100 K), Saturn-mass exoplanet WASP-39b from near-infrared JWST observations suggests that photochemistry is a key process in high temperature exoplanet atmospheres. This is due to the low ($<$1 ppb) abundance of SO$_2$ under thermochemical equilibrium, compared to that produced from the photochemistry of H$_2$O and H$_2$S (1-10 ppm). However, the SO$_2$ inference was made from a single, small molecular feature in the transmission spectrum of WASP-39b at 4.05 $\mu$m, and therefore the detection of other SO$_2$ absorption bands at different wavelengths is needed to better constrain the SO$_2$ abundance. Here we report the detection of SO$_2$ spectral features at 7.7 and 8.5 $\mu$m in the 5-12 $\mu$m transmission spectrum of WASP-39b measured by the JWST Mid-Infrared Instrument (MIRI) Low Resolution Spectrometer (LRS). Our observations suggest an abundance of SO$_2$ of 0.5-25 ppm (1$\sigma$ range), consistent with previous findings. In addition to SO$_2$, we find broad water vapour absorption features, as well as an unexplained decrease in the transit depth at wavelengths longer than 10 $\mu$m. Fitting the spectrum with a grid of atmospheric forward models, we derive an atmospheric heavy element content (metallicity) for WASP-39b of $\sim$7.1-8.0 $\times$ solar and demonstrate that photochemistry shapes the spectra of WASP-39b across a broad wavelength range.
The WIYN 3.5m Telescope at Kitt Peak National Observatory hosts a suite of optical and near infrared instruments, including an extreme precision, optical spectrograph, NEID, built for exoplanet radial velocity studies. In order to achieve sub ms precision, NEID has strict requirements on survey efficiency, stellar image positioning, and guiding performance, which have exceeded the native capabilities of the telescope's original pointing and tracking system. In order to improve the operational efficiency of the telescope we have developed a novel telescope pointing system, built on a recurrent neural network, that does not rely on the usual pointing models (TPoint or other quasi physical bases). We discuss the development of this system, how the intrinsic properties of the pointing problem inform our network design, and show preliminary results from our best models. We also discuss plans for the generalization of this framework, so that it can be applied at other sites.
Coronal jets are one of the most common eruptive activities in the solar atmosphere. They are related to rich physics processes, including but not limited to magnetic reconnection, flaring, instabilities, and plasma heating. Automated identification of off-limb coronal jets has been difficult due to their abundant nature, complex appearance, and relatively small size compared to other features in the corona. In this paper, we present an automated coronal jet identification algorithm (AJIA) that utilizes true and fake jets previously detected by a laborious semi-automated jet detection algorithm (SAJIA, Liu et al. 2023) as the input of an image segmentation neural network U-NET. It is found that AJIA could achieve a much higher (0.81) detecting precision than SAJIA (0.34), meanwhile giving the possibility of whether each pixel in an input image belongs to a jet. We demonstrate that with the aid of artificial neural networks, AJIA could enable fast, accurate, and real-time coronal jet identification from SDO/AIA 304 Åobservations, which are essential in studying the collective and long-term behavior of coronal jets and their relation with the solar activity cycles.
We present a stacked lensing analysis of 96 galaxy clusters selected by the thermal Sunyaev-Zel'dovich (SZ) effect in maps of the cosmic microwave background (CMB). We select foreground galaxy clusters with a $5\sigma$-level SZ threshold in CMB observations from the Atacama Cosmology Telescope, while we define background source galaxies for the lensing analysis with secure photometric redshift cuts in Year 3 data of the Subaru Hyper Suprime Cam survey. We detect the stacked lensing signal in the range of $0.1 < R\, [h^{-1}\mathrm{Mpc}] < 100$ in each of three cluster redshift bins, $0.092<z\le0.445$, $0.445<z\le0.695$, and $0.695<z\le1.180$, with 32 galaxy clusters in each bin. The cumulative signal-to-noise ratios of the lensing signal are $14.6$, $12.0$, and $6.6$, respectively. Using a halo-based forward model, we then constrain statistical relationships between the mass inferred from the SZ observation (i.e. SZ mass) and the total mass derived from our stacked lensing measurements. At the average SZ mass in the cluster sample ($2.1-2.4\times10^{14}\, h^{-1}M_\odot$), our likelihood analysis shows that the average total mass differs from the SZ counterpart by a factor of $1.3 \pm 0.2$, $1.6 \pm 0.2$, and $1.6 \pm 0.3$ ($68\%$) in the aforementioned redshift ranges, respectively. Our limits are consistent with previous lensing measurements, and we find that the cluster modeling choices can introduce a $1\sigma$-level difference in our parameter inferences.
The fifth Sloan Digital Sky Survey (SDSS-V) Local Volume Mapper (LVM) is a wide-field integral field unit (IFU) survey that uses an array of four 160 mm fixed telescopes with siderostats to minimize the number of moving parts. Individual telescope observes the science field or calibration field independently and is synchronized with the science exposure. We developed the LVM Acquisition and Guiding Package (LVMAGP) optimized telescope control software program for LVM observations, which can simultaneously control four focusers, three K-mirrors, one fiber selector, four mounts (siderostats), and seven guide cameras. This software is built on a hierarchical architecture and the SDSS framework and provides three key sequences: autofocus, field acquisition, and autoguide. We designed and fabricated a proto-model siderostat to test the telescope pointing model and LVMAGP software. The mirrors of the proto-model were designed as an isogrid open-back type, which reduced the weight by 46% and enabled reaching thermal equilibrium quickly. Additionally, deflection due to bolting torque, self-gravity, and thermal deformation was simulated, and the maximum scatter of the pointing model induced by the tilt of optomechanics was predicted to be $4'.4$, which can be compensated for by the field acquisition sequence. We performed a real sky test of LVMAGP with the proto-model siderostat and obtained field acquisition and autoguide accuracies of $0''.38$ and $1''.5$, respectively. It met all requirements except for the autoguide specification, which will be resolved by more precise alignment among the hardware components at Las Campanas Observatory.