We examine morphology-separated color–mass diagrams to study the quenching of star formation in ∼100,000 (z ∼ 0) Sloan Digital Sky Survey (SDSS) and ∼20,000 (z ∼ 1) Cosmic Assembly Near-Infrared Deep Extragalactic Legacy Survey (CANDELS) galaxies. To classify galaxies morphologically, we developed Galaxy Morphology Network (GaMorNet), a convolutional neural network that classifies galaxies according to their bulge-to-total light ratio. GaMorNet does not need a large training set of real data and can be applied to data sets with a range of signal-to-noise ratios and spatial resolutions. GaMorNet's source code as well as the trained models are made public as part of this work. We first trained GaMorNet on simulations of galaxies with a bulge and a disk component and then transfer learned using ∼25% of each data set to achieve misclassification rates of ≲5%. The misclassified sample of galaxies is dominated by small galaxies with low signal-to-noise ratios. Using the GaMorNet classifications, we find that bulge- and disk-dominated galaxies have distinct color–mass diagrams, in agreement with previous studies. For both SDSS and CANDELS galaxies, disk-dominated galaxies peak in the blue cloud, across a broad range of masses, consistent with the slow exhaustion of star-forming gas with no rapid quenching. A small population of red disks is found at high mass (∼14% of disks at z ∼ 0 and 2% of disks at z ∼ 1). In contrast, bulge-dominated galaxies are mostly red, with much smaller numbers down toward the blue cloud, suggesting rapid quenching and fast evolution across the green valley. This inferred difference in quenching mechanism is in agreement with previous studies that used other morphology classification techniques on much smaller samples at z ∼ 0 and z ∼ 1.

We compare the rise and decay timescales of ∼200 long-term (∼weeks-months) GeV and R-band outbursts and ∼25 short-term (∼hr-day) GeV flares in a sample of 10 blazars using light curves from the Fermi-LAT and the Yale/SMARTS monitoring project. We find that most of the long-term outbursts are symmetric, indicating that the observed variability is dominated by the crossing timescale of a disturbance, e.g., a shock. A larger fraction of short-term flares are asymmetric with an approximately equal fraction of longer and shorter decay than rise timescale. We employ the MUlti-ZOne Radiation Feedback (MUZORF) model to interpret the above results. We find that the outbursts with slow rise times indicate a gradual acceleration of the particles to GeV energy. A change in the bulk Lorentz factor of the plasma or the width of the shocked region can lead to an increase of the cooling time causing a faster rise than decay time. Parameters such as the luminosity or the distance of the broad line region (BLR) affects the cooling time strongly if a single emission mechanism, e.g., external Compton scattering of BLR photons is considered but may not if other mechanisms, e.g., synchrotron self-compton and external Compton scattering of the torus photon are included. This work carries out a systematic study of the symmetry of flares, which can be used to estimate relevant geometric and physical parameters of blazar jets in the context of the MUZORF model.

We introduce a novel machine-learning framework for estimating the Bayesian posteriors of morphological parameters for arbitrarily large numbers of galaxies. The Galaxy Morphology Posterior Estimation Network (GaMPEN) estimates values and uncertainties for a galaxy’s bulge-to-total-light ratio (L B /L T ), effective radius (R e ), and flux (F). To estimate posteriors, GaMPEN uses the Monte Carlo Dropout technique and incorporates the full covariance matrix between the output parameters in its loss function. GaMPEN also uses a spatial transformer network (STN) to automatically crop input galaxy frames to an optimal size before determining their morphology. This will allow it to be applied to new data without prior knowledge of galaxy size. Training and testing GaMPEN on galaxies simulated to match z < 0.25 galaxies in Hyper Suprime-Cam Wide g-band images, we demonstrate that GaMPEN achieves typical errors of 0.1 in L B /L T , 0.″17 (∼7%) in R e , and 6.3 × 104 nJy (∼1%) in F. GaMPEN's predicted uncertainties are well calibrated and accurate (<5% deviation)—for regions of the parameter space with high residuals, GaMPEN correctly predicts correspondingly large uncertainties. We also demonstrate that we can apply categorical labels (i.e., classifications such as highly bulge dominated) to predictions in regions with high residuals and verify that those labels are ≳97% accurate. To the best of our knowledge, GaMPEN is the first machine-learning framework for determining joint posterior distributions of multiple morphological parameters and is also the first application of an STN to optical imaging in astronomy.

We have investigated on-lattice diffusion limited aggregation (DLA) involving edge diffusion and compared the results with the standard DLA model. For both cases, we observe the existence of a crossover from the fractal to the compact regime as a function of sticking coefficient. However, our modified DLA model including edge diffusion shows an extended fractal growth regime like an earlier theoretical result using realistic growth models and physical parameters [Zhang et al., Phys. Rev. Lett. 73 (1994) 1829]. While the results of Zhang et al. showed the existence of the extended fractal growth regime only on triangular but not on square lattices, we find its existence on the square lattice. There is experimental evidence of this growth regime on a square lattice. The standard DLA model cannot characterize fractal morphology as the fractal dimension (Hausdorff dimension, DH) is insensitive to morphology. It also predicts DH = DP (the perimeter dimension). For the usual fractal structures, observed in growth experiments on surfaces, the perimeter dimension can differ significantly (DH ≠ DP) depending on the morphology. Our modified DLA model shows minor sensitivity to this difference.