The stellar masses and specific star-formation rates of submillimetre galaxies

Establishing the stellar masses (M*), and hence specific star-formation rates (sSFRs) of submillimetre galaxies (SMGs) is crucial for determining their role in the cosmic galaxy/star formation. However, there is as yet no consensus over the typical M* of SMGs. Specifically, even for the same set of SMGs, the reported average M* have ranged over an order of magnitude, from ~5x10^10 Mo to ~5x10^11 Mo. Here we study how different methods of analysis can lead to such widely varying results. We find that, contrary to recent claims in the literature, potential contamination of IRAC 3-8 um photometry from hot dust associated with an active nucleus is not the origin of the published discrepancies in derived M*. Instead, we expose in detail how inferred M* depends on assumptions made in the photometric fitting, and quantify the individual and cumulative effects of different choices of initial mass function, different brands of evolutionary synthesis models, and different forms of assumed star-formation history. We review current observational evidence for and against these alternatives as well as clues from the hydrodynamical simulations, and conclude that, for the most justifiable choices of these model inputs, the average M* of SMGs is ~2x10^11 Mo. We also confirm that this number is perfectly reasonable in the light of the latest measurements of their dynamical masses, and the evolving M* function of the overall galaxy population. M* of this order imply that the average sSFR of SMGs is comparable to that of other star-forming galaxies at z>2, at 2-3 Gyr^-1. This supports the view that, while rare outliers may be found at any M*, most SMGs simply form the top end of the main-sequence of star-forming galaxies at these redshifts. Conversely, this argues strongly against the viewpoint that SMGs are extreme pathological objects, of little relevance in the cosmic history of star-formation.Comment: Accepted to A&A. 13 pages, 5 figures, 3 tables. Main changes: 1) investigation that the main-sequence does not change the location as much as SMGs when changing SFHs; 2) a new table added with all stellar mass estimates for individual SMGs (machine-readable version in the source file). V3: missing references adde

CANDELS: The progenitors of compact quiescent galaxies at z~2

We combine high-resolution HST/WFC3 images with multi-wavelength photometry to track the evolution of structure and activity of massive (log(M*) > 10) galaxies at redshifts z = 1.4 - 3 in two fields of the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS). We detect compact, star-forming galaxies (cSFGs) whose number densities, masses, sizes, and star formation rates qualify them as likely progenitors of compact, quiescent, massive galaxies (cQGs) at z = 1.5 - 3. At z > 2 most cSFGs have specific star-formation rates (sSFR = 10^-9 yr^-1) half that of typical, massive SFGs at the same epoch, and host X-ray luminous AGN 30 times (~30%) more frequently. These properties suggest that cSFGs are formed by gas-rich processes (mergers or disk-instabilities) that induce a compact starburst and feed an AGN, which, in turn, quench the star formation on dynamical timescales (few 10^8 yr). The cSFGs are continuously being formed at z = 2 - 3 and fade to cQGs by z = 1.5. After this epoch, cSFGs are rare, thereby truncating the formation of new cQGs. Meanwhile, down to z = 1, existing cQGs continue to enlarge to match local QGs in size, while less-gas-rich mergers and other secular mechanisms shepherd (larger) SFGs as later arrivals to the red sequence. In summary, we propose two evolutionary scenarios of QG formation: an early (z > 2), fast-formation path of rapidly-quenched cSFGs that evolve into cQGs that later enlarge within the quiescent phase, and a slow, late-arrival (z < 2) path for SFGs to form QGs without passing through a compact state.Comment: Submitted to the Astrophysical Journal Letters, 6 pages, 4 figure

The properties of (sub)millimetre-selected galaxies as revealed by CANDELS HST WFC3/IR imaging in GOODS-South

We have exploited the HST CANDELS WFC3/IR imaging to study the properties of (sub-)mm galaxies in GOODS-South. After using the deep radio and Spitzer imaging to identify galaxy counterparts for the (sub-)mm sources, we have used the new CANDELS data in two ways. First, we have derived improved photometric redshifts and stellar masses, confirming that the (sub-)mm galaxies are massive (=2.2x10^11 M_solar) galaxies at z=1-3. Second, we have exploited the depth and resolution of the WFC3/IR imaging to determine the sizes and morphologies of the galaxies at rest-frame optical wavelengths, fitting two-dimensional axi-symmetric Sersic models. Crucially, the WFC3/IR H-band imaging enables modelling of the mass-dominant galaxy, rather than the blue high-surface brightness features which often dominate optical (rest-frame UV) images of (sub-)mm galaxies, and can confuse visual morphological classification. As a result of this analysis we find that >95% of the rest-frame optical light in almost all of the (sub-)mm galaxies is well-described by either a single exponential disk, or a multiple-component system in which the dominant constituent is disk-like. We demonstrate that this conclusion is consistent with the results of high-quality ground-based K-band imaging, and explain why. The massive disk galaxies which host luminous (sub-)mm emission are reasonably extended (r_e=4 kpc), consistent with the sizes of other massive star-forming disks at z~2. In many cases we find evidence of blue clumps within the sources, with the mass-dominant disk becoming more significant at longer wavelengths. Finally, only a minority of the sources show evidence for a major galaxy-galaxy interaction. Taken together, these results support the view that most (sub-)mm galaxies at z~2 are simply the most extreme examples of normal star-forming galaxies at that era.Comment: 30 pages, 9 figure

Enriched haloes at redshift z=2z=2 with no star-formation: Implications for accretion and wind scenarios

[Abridged] In order to understand which process (e.g. galactic winds, cold accretion) is responsible for the cool (T~10^4 K) halo gas around galaxies, we embarked on a program to study the star-formation properties of galaxies selected by their MgII absorption signature in quasar spectra. Specifically, we searched for the H-alpha line emission from galaxies near very strong z=2 MgII absorbers (with rest-frame equivalent width EW>2 \AA) because these could be the sign-posts of outflows or inflows. Surprisingly, we detect H-alpha from only 4 hosts out of 20 sight-lines (and 2 out of the 19 HI-selected sight-lines), despite reaching a star-formation rate (SFR) sensitivity limit of 2.9 M/yr (5-sigma) for a Chabrier initial mass function. This low success rate is in contrast with our z=1 survey where we detected 66%\ (14/21) of the MgII hosts. Taking into account the difference in sensitivity between the two surveys, we should have been able to detect >11.4 of the 20 z=2 hosts whereas we found only 4 galaxies. Interestingly, all the z=2 detected hosts have observed SFR greater than 9 M/yr, well above our sensitivity limit, while at z=1 they all have SFR less than 9 M/yr, an evolution that is in good agreement with the evolution of the SFR main sequence. Moreover, we show that the z=2 undetected hosts are not hidden under the quasar continuum after stacking our data and that they also cannot be outside our surveyed area. Hence, strong MgII absorbers could trace star-formation driven winds in low-mass halos (Mhalo < 10^{10.6} Msun). Alternatively, our results imply that z=2 galaxies traced by strong MgII absorbers do not form stars at a rate expected (3--10 M/yr) for their (halo or stellar) masses, supporting the existence of a transition in accretion efficiency at Mhalo ~ 10^{11} Msun. This scenario can explain both the detections and the non-detections.Comment: 14 pages, 4 fig.; MNRAS in press, minor corrections to match proof

Smart railways: AI-based track-side monitoring for wheel flat identification

The wheel flat detection in trains using Artificial Intelligence (AI) has emerged as a critical advancement in railway maintenance and safety practices. AI systems can effectively identify geometric deformation in wheel rotation patterns, indicative of potential wheel flat damage, resorting to wayside monitoring systems and machine learning algorithms. This study aims to propose an unsupervised learning algorithm to identify and localize railway wheel flats, which considers three stages: (i) wheel flat detection to distinguish a healthy wheel from a damaged one using outlier analysis, achieving 100 percent accuracy; (ii) localizing the damage to pinpoint the location of the defective wheel through the Hidden Markov Model (HMM); (iii) classification of wheel damage based on its severity using k-means clustering technique. The unsupervised learning algorithm is validated with artificial data attained from a virtual wayside monitoring system related to freight train passages with healthy wheels and defective wheels with single and multiple defects. The proposed methodology demonstrated efficiency and robustness for wheel flat detection, localization, and damage severity classification regardless of the number of defective wheels and their position

A deep neural network-based approach for seizure activity recognition of epilepsy sufferers

Epilepsy is one of the most frequent neurological illnesses caused by epileptic seizures and the second most prevalent neurological ailment after stroke, affecting millions of people worldwide. People with epileptic disease are considered a category of people with disabilities. It significantly impairs a person’s capacity to perform daily tasks, especially those requiring focusing or remembering. Electroencephalogram (EEG) signals are commonly used to diagnose people with epilepsy. However, it is tedious, time-consuming, and subjected to human errors. Several machine learning techniques have been applied to recognize epilepsy previously, but they have some limitations. This study proposes a deep neural network (DNN) machine learning model to determine the existing limitations of previous studies by improving the recognition efficiency of epileptic disease. A public dataset is used in this study and classified into training and testing sets. Experiments were performed to evaluate the DNN model with different dataset classification ratios (80:20), (70:30), (60:40), and (50:50) for training and testing, respectively. Results were evaluated by using different performance metrics including validations, and comparison processes that allow the assessment of the model’s effectiveness. The experimental results showed that the overall efficiency of the proposed model is the highest compared with previous works, with an accuracy rate of 97%. Thus, this study is more accurate and efficient than the existing seizure detection approaches. DNN model has great potential for recognizing epileptic patient activity using a numerical EEG dataset offering a data-driven approach to improve the accuracy and reliability of seizure detection systems for the betterment of patient care and management of epilepsy