New constraints on the free-streaming of warm dark matter from intermediate and small scale Lyman-α forest data

We present new measurements of the free-streaming of warm dark matter (WDM) from Lyman-α flux-power spectra. We use data from the medium resolution, intermediate redshift XQ-100 sample observed with the X-shooter spectrograph (z=3–4.2) and the high-resolution, high-redshift sample used in Viel et al. (2013) obtained with the HIRES/MIKE spectrographs (z=4.2 – 5.4 ). Based on further improved modelling of the dependence of the Lyman- α flux-power spectrum on the free-streaming of dark matter, cosmological parameters, as well as the thermal history of the intergalactic medium (IGM) with hydrodynamical simulations, we obtain the following limits, expressed as the equivalent mass of thermal relic WDM particles. The XQ-100 flux power spectrum alone gives a lower limit of 1.4 keV, the re-analysis of the HIRES/MIKE sample gives 4.1 keV while the combined analysis gives our best and significantly strengthened lower limit of 5.3 keV (all 2 σ C.L.). The further improvement in the joint analysis is partly due to the fact that the two data sets have different degeneracies between astrophysical and cosmological parameters that are broken when the data sets are combined, and more importantly on chosen priors on the thermal evolution. These results all assume that the temperature evolution of the IGM can be modeled as a power law in redshift. Allowing for a nonsmooth evolution of the temperature of the IGM with sudden temperature changes of up to 5000 K reduces the lower limit for the combined analysis to 3.5 keV. A WDM with smaller thermal relic masses would require, however, a sudden temperature jump of 5000 K or more in the narrow redshift interval z = 4.6 – 4.8 , in disagreement with observations of the thermal history based on high-resolution resolution Lyman- α forest data and expectations for photo-heating and cooling in the low density IGM at these redshifts.V. I. is supported by U.S. NSF Grant No. AST-1514734. V. I. also thanks M. McQuinn for useful discussions, and IAS, Princeton, for hospitality during his stay where part of this work was completed. M. V. and T. S. K. are supported by ERC-StG “cosmoIGM”. S. L. has been supported by FONDECYT grant number 1140838 and partially by PFB-06 CATA. V. D., M. V., S. C. acknowledge support from the PRIN INAF 2012 “The X-Shooter sample of 100 quasar spectra at z ∼ 3.5 : Digging into cosmology and galaxy evolution with quasar absorption lines. G. B. is supported by the NSF under award AST-1615814. S. L. E. acknowledges the receipt of an NSERC Discovery Grant. M. H. acknowledges support by ERC ADVANCED GRANT 320596 “The Emergence of Structure during the epoch of Reionization”. L. C. is supported by YDUN DFF 4090-00079. K. D. D. is supported by an NSF AAPF fellowship awarded under NSF grant AST-1302093. J. S. B. acknowledges the support of a Royal Society University Research Fellowship. Based on observations collected at the European Organisation for Astronomical Research in the Southern Hemisphere under ESO programme 189.A-0424. This work made use of the DiRAC High Performance Computing System (HPCS) and the COSMOS shared memory service at the University of Cambridge. These are operated on behalf of the STFC DiRAC HPC facility. This equipment is funded by BIS National E-infrastructure capital grant ST/J005673/1 and STFC grants ST/H008586/1, ST/K00333X/1

The Lyman α forest power spectrum from the XQ-100 legacy survey

We present the Lyman α flux power spectrum measurements of the XQ-100 sample of quasar spectra obtained in the context of the European Southern Observatory Large Programme ‘Quasars and their absorption lines: a legacy survey of the high redshift universe with VLT/XSHOOTER’. Using 100 quasar spectra with medium resolution and signal-to-noise ratio, we measure the power spectrum over a range of redshifts z = 3–4.2 and over a range of scales k = 0.003–0.06 km−1 s. The results agree well with the measurements of the one-dimensional power spectrum found in the literature. The data analysis used in this paper is based on the Fourier transform and has been tested on synthetic data. Systematic and statistical uncertainties of our measurements are estimated, with a total error (statistical and systematic) comparable to the one of the BOSS data in the overlapping range of scales, and smaller by more than 50 per cent for higher redshift bins (z > 3.6) and small scales (k > 0.01 km−1 s). The XQ-100 data set has the unique feature of having signal-to-noise ratios and resolution intermediate between the two data sets that are typically used to perform cosmological studies, i.e. BOSS and high-resolution spectra (e.g. UVES/VLT or HIRES). More importantly, the measured flux power spectra span the high-redshift regime that is usually more constraining for structure formation models

Deep Learning of Dark Energy Spectroscopic Instrument Mock Spectra to Find Damped Ly α Systems

Abstract We have updated and applied a convolutional neural network (CNN) machine-learning model to discover and characterize damped Lyα systems (DLAs) based on Dark Energy Spectroscopic Instrument (DESI) mock spectra. We have optimized the training process and constructed a CNN model that yields a DLA classification accuracy above 99% for spectra that have signal-to-noise ratios (S/N) above 5 per pixel. The classification accuracy is the rate of correct classifications. This accuracy remains above 97% for lower S/N ≈1 spectra. This CNN model provides estimations for redshift and H i column density with standard deviations of 0.002 and 0.17 dex for spectra with S/N above 3 pixel−1. Also, this DLA finder is able to identify overlapping DLAs and sub-DLAs. Further, the impact of different DLA catalogs on the measurement of baryon acoustic oscillations (BAO) is investigated. The cosmological fitting parameter result for BAO has less than 0.61% difference compared to analysis of the mock results with perfect knowledge of DLAs. This difference is lower than the statistical error for the first year estimated from the mock spectra: above 1.7%. We also compared the performances of the CNN and Gaussian Process (GP) models. Our improved CNN model has moderately 14% higher purity and 7% higher completeness than an older version of the GP code, for S/N &gt; 3. Both codes provide good DLA redshift estimates, but the GP produces a better column density estimate by 24% less standard deviation. A credible DLA catalog for the DESI main survey can be provided by combining these two algorithms.</jats:p

The Wide-field Spectroscopic Telescope (WST) Science White Paper

The Wide-field Spectroscopic Telescope (WST) is proposed as a new facility dedicated to the efficient delivery of spectroscopic surveys. This white paper summarises the initial concept as well as the corresponding science cases. WST will feature simultaneous operation of a large field-of-view (3 sq. degree), a high multiplex (20,000) multi-object spectrograph (MOS) and a giant 3x3 sq. arcmin integral field spectrograph (IFS). In scientific capability these requirements place WST far ahead of existing and planned facilities. Given the current investment in deep imaging surveys and noting the diagnostic power of spectroscopy, WST will fill a crucial gap in astronomical capability and work synergistically with future ground and space-based facilities. This white paper shows that WST can address outstanding scientific questions in the areas of cosmology; galaxy assembly, evolution, and enrichment, including our own Milky Way; origin of stars and planets; time domain and multi-messenger astrophysics. WST's uniquely rich dataset will deliver unforeseen discoveries in many of these areas. The WST Science Team (already including more than 500 scientists worldwide) is open to the all astronomical community. To register in the WST Science Team please visit https://www.wstelescope.com/for-scientists/participat

The Lyman-beta forest as a cosmic thermometer

We present a comprehensive analysis of high resolution hydrodynamic simulations in terms of Lyman-alpha and Lyman-beta one dimensional flux power spectra ((P alpha alpha) and P-beta beta). In particular, we focus on the behaviour that the flux auto-power spectra and cross-power spectra (P-alpha beta) display when the intergalactic medium (IGM) thermal history is changed in a range of values that bracket a reference model, while cosmological parameters are kept fixed to best fit the cosmic microwave background data. We present empirical fits that describe at the sub-percent level the dependence of the power spectra on the thermal parameters. At the largest scales, the power spectra show a constant bias between each other that is set by the parameters describing the IGM thermal state. The cross-power spectrum has an oscillatory pattern and crosses zero at a scale which depends on T-0, the IGM temperature at the mean density, for reasonable values of the power-law index gamma of the IGM temperature-density relation (T = T-0(1 + delta)(gamma-1)). By performing a Fisher matrix analysis, we find that the power spectrum Po is more sensitive to the thermal history than P-alpha alpha alone, due to the fact that it probes denser regions than Lyman-a. When we combine the power and cross spectra the constraints on gamma can be improved by a factor similar to 4, while the constraints on T-0 improve by a factor of similar to 2. We address the role of signal-to-noise and resolution by mocking realistic observations and we conclude that the framework presented in this work can significantly improve the knowledge of the IGM thermal state, which will in turn guarantee better constraints on IGM-derived cosmological parameters

Forecasts for WEAVE-QSO: 3D clustering of critical points with Lyman-alpha tomography

The upcoming WEAVE-QSO survey will target a high density of quasars over a large area, enabling the reconstruction of the 3D density field through Lyman-훼 tomography over unprecedented volumes smoothed on intermediate cosmological scales (≈ 16 Mpc/h). We produce mocks of the Lyman-훼 forest using LyMAS, and reconstruct the 3D density field between sightlines through Wiener filtering in a configuration compatible with the future WEAVE-QSO observations. The fidelity of the reconstruction is assessed by measuring one- and two-point statistics from the distribution of critical points in the cosmic web. In addition, initial Lagrangian statistics are predicted from first principles, and measurements of the connectivity of the cosmic web are performed. The reconstruction captures well the expected features in the auto- and cross-correlations of the critical points. This remains true after a realistic noise is added to the synthetic spectra, even though sparsity of sightlines introduces systematics, especially in the cross-correlations of points with mixed signature. Specifically, the most striking clustering features involving filaments and walls could be measured with up to 4 sigma of significance with a WEAVE-QSO-like survey. Moreover, the connectivity of each peak identified in the reconstructed field is globally consistent with its counterpart in the original field, indicating that the reconstruction preserves the geometry of the density field not only statistically, but also locally. Hence the critical points relative positions within the tomographic reconstruction could be used as standard rulers for dark energy by WEAVE-QSO and similar surveys

Deep Learning of Dark Energy Spectroscopic Instrument Mock Spectra to Find Damped Ly α Systems

We have updated and applied a convolutional neural network (CNN) machine-learning model to discover and characterize damped Lyα systems (DLAs) based on Dark Energy Spectroscopic Instrument (DESI) mock spectra. We have optimized the training process and constructed a CNN model that yields a DLA classification accuracy above 99% for spectra that have signal-to-noise ratios (S/N) above 5 per pixel. The classification accuracy is the rate of correct classifications. This accuracy remains above 97% for lower S/N ≈1 spectra. This CNN model provides estimations for redshift and H i column density with standard deviations of 0.002 and 0.17 dex for spectra with S/N above 3 pixel-1. Also, this DLA finder is able to identify overlapping DLAs and sub-DLAs. Further, the impact of different DLA catalogs on the measurement of baryon acoustic oscillations (BAO) is investigated. The cosmological fitting parameter result for BAO has less than 0.61% difference compared to analysis of the mock results with perfect knowledge of DLAs. This difference is lower than the statistical error for the first year estimated from the mock spectra: above 1.7%. We also compared the performances of the CNN and Gaussian Process (GP) models. Our improved CNN model has moderately 14% higher purity and 7% higher completeness than an older version of the GP code, for S/N &gt; 3. Both codes provide good DLA redshift estimates, but the GP produces a better column density estimate by 24% less standard deviation. A credible DLA catalog for the DESI main survey can be provided by combining these two algorithms