Limited Dynamic Range Research Articles

ACE-MEF: Adaptive Clarity Evaluation-Guided Network With Illumination Correction for Multi-Exposure Image Fusion

For a natural scene with nonuniform environment light, the captured visible images are always under- or over-exposed because of the limited dynamic range of digital imaging devices. Multi-exposure image fusion (MEF) is a mainstream and effective solution. For a local region that has friendly visual effect in one exposure setting but extremely bad-exposed in another, most existing MEF methods have the ability to transfer the scene detail information to the fused images. However, they will be affected by the over-high or -low light inevitably thus resulting in local visibility reduction. To address this issue, we propose an adaptive clarity evaluation-guided network with illumination correction for MEF in a coarse-to-fine manner, which is termed as ACE-MEF. To be specific, our ACE-MEF is mainly composed of two modules: clarity preservation network (CPN) and illumination adjustment network (IAN). Based on the adaptive clarity evaluation, CPN could be trained to coarsely preserve the environment light and texture details of the clearer regions in source images. Therefore, the need for labeled reference images that are time-consuming to obtain could be mitigated. By measuring the parameter maps of gamma function, IAN is able to refine and correct the local bad-exposed regions so that more details could be further revealed. Extensive experiments demonstrate that our method outperforms multiple state-of-the-art algorithms qualitatively and quantitatively.

Alternative normalization and analysis pipeline to address systematic bias in NanoString GeoMx Digital Spatial Profiling data

Spatial transcriptomics is a novel technique that provides RNA-expression data with tissue-contextual annotations. Quality assessments of such techniques using end-user generated data are often lacking. Here, we evaluated data from the NanoString GeoMx Digital Spatial Profiling (DSP) platform and standard processing pipelines. We queried 72 ROIs from 12 glioma samples, performed replicate experiments of eight samples for validation, and evaluated five external datasets. The data consistently showed vastly different signal intensities between samples and experimental conditions that resulted in biased analysis. We evaluated the performance of alternative normalization strategies and show that quantile normalization can adequately address the technical issues related to the differences in data distributions. Compared to bulk RNA sequencing, NanoString DSP data show a limited dynamic range which underestimates differences between conditions. Weighted gene co-expression network analysis allowed extraction of gene signatures associated with tissue phenotypes from ROI annotations. Nanostring GeoMx DSP data therefore require alternative normalization methods and analysis pipelines.

Multievent Histogramming TDC With Pre–Post Weighted Histogramming Filter for CMOS LiDAR Sensors

In this study, we have proposed a CMOS light detection and ranging (LiDAR) sensor with high immunity for background noise (BGN) and inter-LiDAR interference (IF). To mitigate pileup distortions that cause signal loss due to large BGN, we implemented multievent weighted histogramming time-to-digital converter (TDC) with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4\times4$ </tex-math></inline-formula> macro-pixels. The 11-bit two-step TDC and mixed-signal accumulator (MA) were utilized to implement histogramming with area efficiency. To overcome the limited dynamic range of histogram accumulation while preserving the maximum detectable range (MDR), a pre–post weighted histogram filter was implemented to detect only time-correlated time-of-flight (TOF) out of BGN from both sunlight and increased dark noise while enhancing sensitivity through higher excess voltage of single-photon avalanche diodes (SPADs). In addition, the infinite IF canceling (IIC) scheme that employs pulse-position modulation and time-difference modulation was designed to be compatible with the pre–post weighted histogram filter. For the IIC, the ON-chip SPAD-based random number generator provides a random code. A theoretical analysis of LiDAR performance such as MDR was introduced to prove the effectiveness of the pre-post weighted histogram filter. The fabricated sensor demonstrated 8.08-cm accuracy for a range of 32 m under high BGN (105-klx sunlight and 48.72-kcps dark-count rate with increased excess bias voltage) through the pre–post weighted histogram filter and IF immunity through the IIC scheme.

Measuring the Beam Energy in Proton Therapy Facilities Using ATLAS IBL Pixel Detectors

The accurate measurement of the beam range in the frame of quality assurance (QA) is a requirement for clinical use of a proton therapy machine. Conventionally used detectors mostly estimate the range by measuring the depth dose distribution of the protons. In this paper, we use pixel detectors designed for individual particle tracking in the high-radiation environment of the ATLAS experiment at LHC. The detector measures the deposited energy in the sensor for individual protons. Due to the limited dynamic energy range of the readout chip, several ways to measure the proton energy or range are examined. A staircase phantom is placed on the detector to perform an energy calibration relative to the NIST PSTAR stopping power database. In addition, track length measurements are performed using the detector aligned parallel with the beam axis to investigate the Linear Energy Transfer (LET) per pixel along the trajectory of individual protons. In this proof-of-principle study, we show that this radiation hardness detector can successfully be used to determine the initial proton energy for protons impinging on the sensor with an energy below 44 MeV after the range shifters. It becomes clear that an improvement of the energy resolution of the readout chip is required for clinical use.

An efficient deep neural network accelerator using controlled ferroelectric domain dynamics

The current work reports an efficient deep neural network (DNN) accelerator, where analog synaptic weight elements are controlled by ferroelectric (FE) domain dynamics. An integrated device-to-algorithm framework for benchmarking novel synaptic devices is used. In poly(vinylidene fluoride-trifluoroethylene)-based ferroelectric tunnel junctions (FTJs), analog conductance states are measured using a custom pulsing protocol, and associated control circuits and array architectures for DNN training are simulated. Our results show that precise control of polarization switching dynamics in multi-domain polycrystalline FE thin films can produce considerable weight-update linearity in metal–ferroelectric–semiconductor (MFS) tunnel junctions. Ultrafast switching and low junction currents in these devices offer extremely energy-efficient operation. Via an integrated platform of hardware development, characterization and modeling, we predict the available conductance range, where linearity is expected under identical potentiating and depressing pulses for efficient DNN training and inference tasks. As an example, an analog crossbar-based DNN accelerator with MFS junctions as synaptic weight elements showed >93% training accuracy on a large MNIST handwritten digit dataset while, for cropped images, >95% accuracy is achieved. One observed challenge is the rather limited dynamic conductance range while operating under identical potentiating and depressing pulses below 1 V. Investigation is underway to improve the FTJ dynamic conductance range, maintaining the weight-update linearity under an identical pulse scheme.

CNTnC and fYTnC2, Genetically Encoded Green Calcium Indicators Based on Troponin C from Fast Animals

NTnC-like green fluorescent genetically encoded calcium indicators (GECIs) with two calcium ion binding sites were constructed using the insertion of truncated troponin C (TnC) from Opsanus tau into green fluorescent proteins (GFPs). These GECIs are small proteins containing the N- and C-termini of GFP; they exert a limited effect on the cellular free calcium ion concentration; and in contrast to calmodulin-based calcium indicators they lack undesired interactions with intracellular proteins in neurons. The available TnC-based NTnC or YTnC GECIs had either an inverted response and high brightness but a limited dynamic range or a positive response and fast kinetics in neurons but lower brightness and an enhanced but still limited dF/F dynamic range. Here, we solved the crystal structure of NTnC at 2.5 Å resolution. Based on this structure, we developed positive NTnC2 and inverted iNTnC2 GECIs with a large dF/F dynamic range in vitro but very slow rise and decay kinetics in neurons. To overcome their slow responsiveness, we swapped TnC from O. tau in NTnC2 with truncated troponin C proteins from the muscles of fast animals, namely, the falcon, hummingbird, cheetah, bat, rattlesnake, and ant, and then optimized the resulting constructs using directed molecular evolution. Characterization of the engineered variants using purified proteins, mammalian cells, and neuronal cultures revealed cNTnC GECI with truncated TnC from Calypte anna (hummingbird) to have the largest dF/F fluorescence response and fast dissociation kinetics in neuronal cultures. In addition, based on the insertion of truncated TnCs from fast animals into YTnC2, we developed fYTnC2 GECI with TnC from Falco peregrinus (falcon). The purified proteins cNTnC and fYTnC2 had 8- and 6-fold higher molecular brightness and 7- and 6-fold larger dF/F responses to the increase in Ca2+ ion concentration than YTnC, respectively. cNTnC GECI was also 4-fold more photostable than YTnC and fYTnC2 GECIs. Finally, we assessed the developed GECIs in primary mouse neuronal cultures stimulated with an external electric field; in these conditions, cNTnC had a 2.4-fold higher dF/F fluorescence response than YTnC and fYTnC2 and was the same or slightly slower (1.4-fold) than fYTnC2 and YTnC in the rise and decay half-times, respectively.

Structure Determination Feasibility of Three-Dimensional Electron Diffraction in Case of Limited Data

During the last two decades, three-dimensional electron diffraction (3D ED) has undergone a renaissance, starting with the introduction of precession (Precession Electron Diffraction Tomography, PEDT) that led to variations on the idea of collecting as much of the diffraction space as possible in order to solve crystal structures from sub-micron sized crystals. The most popular of these acquisition methods is based on the continuous tilting/rotation of the crystal (so-called Microcrystal Electron Diffraction, MicroED) akin to the oscillating crystal method in X-ray crystallography, which was enabled by the increase of sensitivity and acquisition speed in electron detectors. While 3D ED data is more complex than the equivalent X-ray data due to the higher proportion of dynamical scattering, the same basic principles of what is required in terms of data quality and quantity in order to solve a crystal structure apply; high completeness, high data resolution and good signal-to-noise statistics on measured reflection intensities. However, it may not always be possible to collect data in these optimum conditions, the most common limitations being the tilt range of the goniometer stage, often due to a small pole piece gap or the use of a non-tomography holder, or the position of the sample on the TEM grid, which may be too close to a grid bar and then the specimen of interest becomes occluded during tilting. Other factors that can limit the quality of the acquired data include the limited dynamic range of the detector, which can result on truncated intensities, or the sensitivity of the crystal to the electron beam, whereby the crystallinity of the particle is changing under the illumination of the beam. This limits the quality and quantity of the measured intensities and makes structure analysis of such data challenging. Under these circumstances, traditional approaches may fail to elucidate crystal structures, and global optimization methods may be used here as an alternative powerful tool. In this context, this work presents a systematic study on the application of a global optimization method to crystal structure determination from 3D ED data. The results are compared with known structure models and crystal phases obtained from traditional ab initio structure solution methods demonstrating how this strategy can be reliably applied to the analysis of partially complete 3D ED data.

Empirical constraints on the turbulence in QSO host nebulae from velocity structure function measurements

ABSTRACT We present the first empirical constraints on the turbulent velocity field of the diffuse circumgalactic medium around four luminous quasi-stellar objects (QSOs) at z ≈ 0.5–1.1. Spatially extended nebulae of ≈50–100 physical kpc in diameter centred on the QSOs are revealed in [O ii] $\lambda \lambda \, 3727,3729$ and/or [O iii] $\lambda \, 5008$ emission lines in integral field spectroscopic observations obtained using Multi-Unit Spectroscopic Explorer on the Very Large Telescope. We measure the second- and third-order velocity structure functions (VSFs) over a range of scales, from ≲5 kpc to ≈20–50 kpc, to quantify the turbulent energy transfer between different scales in these nebulae. While no constraints on the energy injection and dissipation scales can be obtained from the current data, we show that robust constraints on the power-law slope of the VSFs can be determined after accounting for the effects of atmospheric seeing, spatial smoothing, and large-scale bulk flows. Out of the four QSO nebulae studied, one exhibits VSFs in spectacular agreement with the Kolmogorov law, expected for isotropic, homogeneous, and incompressible turbulent flows. The other three fields exhibit a shallower decline in the VSFs from large to small scales. However, with a limited dynamic range in the spatial scales in seeing-limited data, no constraints can be obtained for the VSF slopes of these three nebulae. For the QSO nebula consistent with the Kolmogorov law, we determine a turbulence energy cascade rate of ≈0.2 cm2 s−3. We discuss the implication of the observed VSFs in the context of QSO feeding and feedback in the circumgalactic medium.

Adaptive Linearization for the Sub-Nyquist Photonic Receiver Based on Deep Learning

Due to the nonlinear and aliasing effects, the sub-Nyquist photonic receiver for radio frequency (RF) signals with large instantaneous bandwidth suffers limited dynamic range and noise performance. We designated a deep residual network (Resnet) to realize adaptive linearization across 40 GHz bandwidth. In contrast to conventional linearization methods, the deep learning method achieves the suppression of multifactorial spurious distortions and the noise floor simultaneously. It does not require an accurate calculation of the nonlinear transfer function or prior signal information. The experiments demonstrated that the proposed Resnet could improve the spur-free dynamic range (SFDR) and the signal-to-noise ratio (SNR) significantly by testing with single-tone signals, dual-tone signals, wireless communication signals, and modulated radar signals.

Non-uniform illumination underwater image enhancement via events and frame fusion.

Absorption and scattering by aqueous media can attenuate light and cause underwater optical imagery difficulty. Artificial light sources are usually used to aid deep-sea imaging. Due to the limited dynamic range of standard cameras, artificial light sources often cause underwater images to be underexposed or overexposed. By contrast, event cameras have a high dynamic range and high temporal resolution but cannot provide frames with rich color characteristics. In this paper, we exploit the complementarity of the two types of cameras to propose an efficient yet simple method for image enhancement of uneven underwater illumination, which can generate enhanced images containing better scene details and colors similar to standard frames. Additionally, we create a dataset recorded by the Dynamic and Active-pixel Vision Sensor that includes both event streams and frames, enabling testing of the proposed method and frame-based image enhancement methods. The experimental results conducted on our dataset with qualitative and quantitative measures demonstrate that the proposed method outperforms the compared enhancement algorithms.

Enlarging the frequency dynamic range of DMZI sensors based on the switching control feedback system.

The feedback loop in dual Mach-Zehnder interferometer (DMZI) sensors stabilizes the system operating at the quadratic point for the highest sensitivity but requires the minimum measurable vibration frequency out of the feedback bandwidth, resulting in a limited dynamic range. In this paper, we point out that the feedback operation is unnecessary while vibration is occurring and propose a strategy to adaptively enable/disable the feedback phase compensation depending on the vibration state, lowering the minimum measurable vibration frequency tenfold. Moreover, the state variable employed enables direct extraction of vibration-related data, with no need of complicated postprocessing algorithms.

Neural Network Training on In-Memory-Computing Hardware With Radix-4 Gradients

Deep learning training involves a large number of operations, which are dominated by high dimensionality Matrix-Vector Multiplies (MVMs). This has motivated hardware accelerators to enhance compute efficiency, but where data movement and accessing are proving to be key bottlenecks. In-Memory Computing (IMC) is an approach with the potential to overcome this, whereby computations are performed in-place within dense 2-D memory. However, IMC fundamentally trades efficiency and throughput gains for dynamic-range limitations, raising distinct challenges for training, where compute precision requirements are seen to be substantially higher than for inferencing. This paper explores training on IMC hardware by leveraging two recent developments: (1) a training algorithm enabling aggressive quantization through a radix-4 number representation; (2) IMC leveraging compute based on precision capacitors, whereby analog noise effects can be made well below quantization effects. Energy modeling calibrated to a measured silicon prototype implemented in 16nm CMOS shows that energy savings of over 400x can be achieved with full quantizer adaptability, where all training MVMs can be mapped to IMC, and 3x can be achieved for two-level quantizer adaptability, where two of the three training MVMs can be mapped to IMC.

Performance enhancement of coherent modulation imaging in the presence of missing data.

Coherent diffraction imaging (CDI) has become a powerful imaging modality in synchrotron x-ray imaging and electron microscopy communities. In the far-field geometry, image quality of CDI depends strongly on the performance of detector; specifically, the dynamic range, pixel size, and the absence of missing data. Coherent modulation imaging (CMI), an innovative variant of CDI, improves the algorithmic convergence by inserting a modulator upstream of the detector. Here, we explore the potential of CMI in eliminating nonideal effects of detector by modifying the modulus constraint to extrapolate the missing part of diffraction pattern. Nine folds of extrapolation in area of diffraction pattern have been shown feasible in experiment; while sixteen folds in simulation. For image quality measured by Structural Similarity (SSIM), our method shows a maximum of 32% improvement over the traditional method. Our method provides a way to alleviate the effects of beamstop, gaps between modules, limited dynamic range, and limited detector size for CMI.

An application of a cavity-based beam arrival time measurement system: Beam energy measurement

A high-resolution cavity-based beam arrival/flight time measurement system has been established at SXFEL (Shanghai soft X-ray free-electron laser facility). During the development of the system, a correlation between the energy jitter and the beam flight time jitter was found which inspires and motivates us to further explore the possibility of using the beam arrival/flight time measurement system to measure the beam energy. On the one hand, beam energy is one of the most fundamental and critical parameters for accelerator facilities. Only precise handling of the beam energy enables precise control of the FEL (free-electron laser) radiation wavelength. On the other hand, there are only a few methods for beam energy measurement. The profile-based beam energy measurement method is commonly used but is interceptive. The BPM-based beam energy measurement scheme is non-interceptive but has a limited dynamic range and requires initial beam position calibration. This paper reports a promising application of a cavity-based high-resolution beam arrival/flight time measurement system: non-intercepting wide-range beam energy measurement. A brief introduction of the method principle and system implementation has been covered in this article. Moreover, a beam-based preliminary test has been performed and the test results show a linear correlation between the beam energy and the beam flight time under the current system configuration. The measured linear factor is 0.692 ps/MeV. In addition, the relative bunch energy measurement resolution is 0.0001.

The faintest solar coronal hard X-rays observed with FOXSI

Context. Solar nanoflares are small impulsive events releasing magnetic energy in the corona. If nanoflares follow the same physics as their larger counterparts, they should emit hard X-rays (HXRs) but with a rather faint intensity. A copious and continuous presence of nanoflares would result in a sustained HXR emission. These nanoflares could deliver enormous amounts of energy into the solar corona, possibly accounting for its high temperatures. To date, there has not been any direct observation of such persistent HXRs from the quiescent Sun. However, the quiet-Sun HXR emission was constrained in 2010 using almost 12 days of quiescent solar off-pointing observations by the Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI). These observations set 2σ upper limits at 3.4 × 10−2 photons s−1 cm−2 keV−1 and 9.5 × 10−4 photons s−1 cm−2 keV−1 for the 3–6 keV and 6–12 keV energy ranges, respectively. Aims. Observing faint HXR emission is challenging because it demands high sensitivity and dynamic range instruments. The Focusing Optics X-ray Solar Imager (FOXSI) sounding rocket experiment excels in these two attributes when compared with RHESSI. FOXSI completed its second and third successful flights (FOXSI-2 and -3) on December 11, 2014, and September 7, 2018, respectively. This paper aims to constrain the quiet-Sun emission in the 5–10 keV energy range using FOXSI-2 and -3 observations. Methods. To fully characterize the sensitivity of FOXSI, we assessed ghost ray backgrounds generated by sources outside of the field of view via a ray-tracing algorithm. We used a Bayesian approach to provide upper thresholds of quiet-Sun HXR emission and probability distributions for the expected flux when a quiet-Sun HXR source is assumed to exist. Results. We found a FOXSI-2 upper limit of 4.5 × 10−2 photons s−1 cm−2 keV−1 with a 2σ confidence level in the 5–10 keV energy range. This limit is the first-ever quiet-Sun upper threshold in HXR reported using ∼1 min observations during a period of high solar activity. RHESSI was unable to measure the quiet-Sun emission during active times due to its limited dynamic range. During the FOXSI-3 flight, the Sun exhibited a fairly quiet configuration, displaying only one aged nonflaring active region. Using the entire ∼6.5 min of FOXSI-3 data, we report a 2σ upper limit of ∼10−4 photons s−1 cm−2 keV−1 for the 5–10 keV energy range. Conclusions. The FOXSI-3 upper limits on quiet-Sun emission are similar to that previously reported, but FOXSI-3 achieved these results with only 5 min of observations or about 1/2600 less time than RHESSI. A possible future spacecraft using hard X-ray focusing optics like those in the FOXSI concept would allow enough observation time to constrain the current HXR quiet-Sun limits further, or perhaps even make direct detections. This is the first report of quiet-Sun HXR limits from FOXSI and the first science paper using FOXSI-3 observations.

An Energy-Efficient Optical Wireless OFDMA Scheme for Medical Body-Area Networks

The transfer of health monitoring data from multiple patients using wireless body-area networks requires the use of robust, and energy and bandwidth efficient multiple-access schemes. This paper considers the frequency-division multiple access for the wireless uplink to a fixed access point when using infrared signals to collect medical data from several patients inside an emergency waiting room. The conventional optical orthogonal scheme applies Hermitian symmetry to obtain real-valued signals, which implies increased computational complexity. We consider a new approach transmitting only the real part of a complex-valued signal, where no such constraint is imposed. Based on the proposed scheme, and taking into account the limited dynamic range of an infrared light-emitting diode, we study the performance of direct current biased and asymmetrically clipped schemes, and show their advantage in terms of energy efficiency and computational complexity, as compared with the conventional schemes. For instance, we show that by using asymmetric clipping, around 35mW less transmit power is needed to achieve a bit error rate of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathrm {10}^{-3}$ </tex-math></inline-formula> in the considered scenario. We also demonstrate the robustness of the proposed scheme against multiple access interference.

Machine Learning Assisted High-Sensitivity and Large-Dynamic-Range Curvature Sensor Based on No-Core Fiber and Hollow-Core Fiber

Simultaneously increasing the sensitivity and dynamic range of an optical fiber sensor is desired and yet challenging. In this article, we demonstrate an optical fiber curvature sensor based on a no-core fiber (NCF) cascaded with a hollow-core fiber (HCF), realizing simultaneously high sensitivity and a large dynamic range with the assistance of machine learning analysis. The sensor is fabricated by simply fusion splicing a section of NCF and HCF to two single-mode fibers (SMFs), forming the SMF-NCF-HCF-SMF hybrid structure. It is shown that the multimode interference in the NCF can increase the sensitivity of the device for curvature measurements, compared to the conventional SMF-HCF-SMF structure. However, the enhanced sensitivity poses a limitation on the dynamic range of the proposed curvature sensor. We propose the use of machine learning to analyze the measured spectra of the sensor device, achieving one-to-one mapping between the measured raw spectrum and the exerted curvature on the sensor, and thereby the issue of the limited dynamic range is resolved. The proposed strategy, enabling the co-existence of high sensitivity and a large dynamic range, is a highly generalizable technique and can be extended to other optical fiber sensors for measuring other physical, chemical, and biological quantities in different applications.

Cluster-type analogue memristor by engineering redox dynamics for high-performance neuromorphic computing

Memristors, or memristive devices, have attracted tremendous interest in neuromorphic hardware implementation. However, the high electric-field dependence in conventional filamentary memristors results in either digital-like conductance updates or gradual switching only in a limited dynamic range. Here, we address the switching parameter, the reduction probability of Ag cations in the switching medium, and ultimately demonstrate a cluster-type analogue memristor. Ti nanoclusters are embedded into densified amorphous Si for the following reasons: low standard reduction potential, thermodynamic miscibility with Si, and alloy formation with Ag. These Ti clusters effectively induce the electrochemical reduction activity of Ag cations and allow linear potentiation/depression in tandem with a large conductance range (~244) and long data retention (~99% at 1 hour). Moreover, according to the reduction potentials of incorporated metals (Pt, Ta, W, and Ti), the extent of linearity improvement is selectively tuneable. Image processing simulation proves that the Ti4.8%:a-Si device can fully function with high accuracy as an ideal synaptic model.

Pattern decorrelation in the mouse medial prefrontal cortex enables social preference and requires MeCP2

Sociability is crucial for survival, whereas social avoidance is a feature of disorders such as Rett syndrome, which is caused by loss-of-function mutations in MECP2. To understand how a preference for social interactions is encoded, we used in vivo calcium imaging to compare medial prefrontal cortex (mPFC) activity in female wild-type and Mecp2-heterozygous mice during three-chamber tests. We found that mPFC pyramidal neurons in Mecp2-deficient mice are hypo-responsive to both social and nonsocial stimuli. Hypothesizing that this limited dynamic range restricts the circuit’s ability to disambiguate coactivity patterns for different stimuli, we suppressed the mPFC in wild-type mice and found that this eliminated both pattern decorrelation and social preference. Conversely, stimulating the mPFC in MeCP2-deficient mice restored social preference, but only if it was sufficient to restore pattern decorrelation. A loss of social preference could thus indicate impaired pattern decorrelation rather than true social avoidance.

The MOSDEF-LRIS survey: connection between galactic-scale outflows and the properties of z ∼ 2 star-forming galaxies

ABSTRACT We investigate the conditions that facilitate galactic-scale outflows using a sample of 155 typical star-forming galaxies at z ∼ 2 drawn from the MOSFIRE Deep Evolution Field (MOSDEF) survey. The sample includes deep rest-frame UV spectroscopy from the Keck Low-Resolution Imaging Spectrometer (LRIS), which provides spectral coverage of several low-ionization interstellar (LIS) metal absorption lines and Lyα emission. Outflow velocities are calculated from the centroids of the LIS absorption and/or Lyα emission, as well as the highest velocity component of the outflow from the blue wings of the LIS absorption lines. Outflow velocities are found to be marginally correlated or independent of galaxy properties, such as star-formation rate (SFR) and star-formation rate surface density (ΣSFR). Outflow velocity scales with SFR as a power-law with index 0.24, which suggests that the outflows may be primarily driven by mechanical energy generated by supernovae explosions, as opposed to radiation pressure acting on dusty material. On the other hand, outflow velocity and ΣSFR are not significantly correlated, which may be due to the limited dynamic range of ΣSFR probed by our sample. The relationship between outflow velocity and ΣSFR normalized by stellar mass (ΣsSFR), as a proxy for gravitational potential, suggests that strong outflows (e.g. &amp;gt; 200 km s−1) become common above a threshold of log(ΣsSFR/$\rm {yr}^{-1}\ \rm {kpc}^{-2}$) ∼ −11.3, and that above this threshold, outflow velocity uncouples from ΣsSFR. These results highlight the need for higher resolution spectroscopic data and spatially resolved imaging to test the driving mechanisms of outflows predicted by theory.