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Register a new userPost-starburst galaxies: more than just an interesting curiosity
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Vivienne Wild
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From the VIMOS VLT DEEP Survey (VVDS) we select a sample of 16 galaxies with spectra which identify them as having recently undergone a strong starburst and subsequent fast quenching of star formation. These post-starburst galaxies lie in the redshift range 0.5<z<1.0 with masses >10^9.75Msun. They have a number density of 1x10^-4 per Mpc^3, almost two orders of magnitude sparser than the full galaxy population with the same mass limit. We compare with simulations to show that the galaxies are consistent with being the descendants of gas rich major mergers. Starburst mass fractions must be larger than ~5-10% and decay times shorter than ~10^8 years for post-starburst spectral signatures to be observed in the simulations. We find that the presence of black hole feedback does not greatly affect the evolution of the simulated merger remnants through the post-starburst phase. The multiwavelength spectral energy distributions of the post-starburst galaxies show that 5/16 have completely ceased the formation of new stars. These 5 galaxies correspond to a mass flux entering the red-sequence of rhodot(A->Q, PSB) = 0.0038Msun/Mpc^3/yr, assuming the defining spectroscopic features are detectable for 0.35Gyr. If the galaxies subsequently remain on the red sequence, this accounts for 38(+4/-11)% of the growth rate of the red sequence. Finally, we compare our high redshift results with a sample of galaxies with 0.05<z<0.1 observed in the SDSS and UKIDSS surveys. We find a very strong redshift evolution: the mass density of strong post-starburst galaxies is 230 times lower at z~0.07 than at z~0.7.
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We present partial results from our monitoring of the nuclear region of the starburst galaxy IC 694 (=Arp 299-A) at radio wavelengths, aimed at discovering recently exploded CCSNe, as well as to determine their rate of explosion, which carries crucial information on star formation rates and starburst scenarios at work. Two epochs of eEVN observations at 5.0 GHz, taken in 2008, revealed the presence of a rich cluster of compact radio emitting sources in the central 150 pc of the nuclear starburst in Arp 299A. The large brightness temperatures observed for the compact sources indicate a non-thermal origin for the observed radio emission, implying that most, if not all, of those sources were young radio supernovae (RSNe) and supernova remnants (SNRs). More recently, contemporaneous EVN observations at 1.7 and 5.0 GHz taken in 2009 have allowed us to shed light on the compact radio emission of the parsec-scale structure in the nucleus of Arp 299-A. Namely, our EVN observations have shown that one of the compact VLBI sources, A1, previously detected at 5.0 GHz, has a flat spectrum between 1.7 and 5.0 GHz and is the brightest source at both frequencies. The morphology, radio luminosity, spectral index and ratio of radio-to-X-ray emission of the A1-A5 region allowed us to identify A1-A5 with long-sought AGN in Arp 299-A. This finding may suggest that both starburst and AGN are frequently associated phenomena in mergers. Finally, we also note that component A0, identified as a young RSN, exploded at the mere distance of two parsecs from the putative AGN in Arp 299-A, which makes this supernova one of the closest to a central supermassive black hole ever detected.
Banhatti (2009) set down the procedure to derive cosmological number density n(z) from the differential distribution p(x) of the fractional luminosity volume relative to the maximum volume, x equiv V/Vm (0 leq x leq 1), using a small sample of 76 quasars for illustrative purposes. This procedure is here applied to a bigger sample of 286 quasars selected from Parkes half-Jansky flat-spectrum survey at 2.7 GHz (Drinkwater et al 1997). The values of n(z) are obtained for 8 values of redshift z from 0 to 3.5. The function n(z) can be interpreted in terms of redshift distribution obtained by integrating the radio luminosity function {rho}(P, z) over luminosities P for the survey limiting flux density S0 = 0.5 Jy. Keywords. V/Vm - luminosity-volume - cosmological number density - redshift distribution - luminosity function - quasars [Note: This somewhat modified version was submitted to MNRaS on 14 July 2016. It was (almost) rejected, except if thoroughly revised.]
An evolution strategy (ES) variant based on a simplification of a natural evolution strategy recently attracted attention because it performs surprisingly well in challenging deep reinforcement learning domains. It searches for neural network parameters by generating perturbations to the current set of parameters, checking their performance, and moving in the aggregate direction of higher reward. Because it resembles a traditional finite-difference approximation of the reward gradient, it can naturally be confused with one. However, this ES optimizes for a different gradient than just reward: It optimizes for the average reward of the entire population, thereby seeking parameters that are robust to perturbation. This difference can channel ES into distinct areas of the search space relative to gradient descent, and also consequently to networks with distinct properties. This unique robustness-seeking property, and its consequences for optimization, are demonstrated in several domains. They include humanoid locomotion, where networks from policy gradient-based reinforcement learning are significantly less robust to parameter perturbation than ES-based policies solving the same task. While the implications of such robustness and robustness-seeking remain open to further study, this works main contribution is to highlight such differences and their potential importance.
Post-starburst or E+A galaxies are rapidly transitioning from star-forming to quiescence. While the current star formation rate of post-starbursts is already at the level of early type galaxies, we recently discovered that many have large CO-traced molecular gas reservoirs consistent with normal star forming galaxies. These observations raise the question of why these galaxies have such low star formation rates. Here we present an ALMA search for the denser gas traced by HCN (1--0) and HCO+ (1--0) in two CO-luminous, quiescent post-starburst galaxies. Intriguingly, we fail to detect either molecule. The upper limits are consistent with the low star formation rates and with early-type galaxies. The HCN/CO luminosity ratio upper limits are low compared to star-forming and even many early type galaxies. This implied low dense gas mass fraction explains the low star formation rates relative to the CO-traced molecular gas and suggests the state of the gas in post-starburst galaxies is unusual, with some mechanism inhibiting its collapse to denser states. We conclude that post-starbursts galaxies are now quiescent because little dense gas is available, in contrast to the significant CO-traced lower density gas reservoirs that still remain.
Cross-modal attention mechanisms have been widely applied to the image-text matching task and have achieved remarkable improvements thanks to its capability of learning fine-grained relevance across different modalities. However, the cross-modal attention models of existing methods could be sub-optimal and inaccurate because there is no direct supervision provided during the training process. In this work, we propose two novel training strategies, namely Contrastive Content Re-sourcing (CCR) and Contrastive Content Swapping (CCS) constraints, to address such limitations. These constraints supervise the training of cross-modal attention models in a contrastive learning manner without requiring explicit attention annotations. They are plug-in training strategies and can be easily integrated into existing cross-modal attention models. Additionally, we introduce three metrics including Attention Precision, Recall, and F1-Score to quantitatively measure the quality of learned attention models. We evaluate the proposed constraints by incorporating them into four state-of-the-art cross-modal attention-based image-text matching models. Experimental results on both Flickr30k and MS-COCO datasets demonstrate that integrating these constraints improves the model performance in terms of both retrieval performance and attention metrics.
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